@prefix : . @prefix bd: . @prefix bds: . @prefix dc: . @prefix default1: . @prefix default2: . @prefix default3: . @prefix default4: . @prefix default5: . @prefix fn: . @prefix foaf: . @prefix hint: . @prefix owl: . @prefix rdf: . @prefix rdfs: . @prefix sesame: . @prefix xml: . @prefix xsd: . a , default3:Article, default1:Paper ; default4:abstract "In order to make computers understand human languages and to reason, human knowledge needs to be represented and stored in a form that can be processed by computers. Knowledge graphs have been developed for use as a form of the knowledge base for words and general relationships among words. However, they have two limitations. One is that the knowledge graph is limited in size and scope for most of the human languages. Another is that they are not able to deal with neologisms that form a part of the human common sense. Addressing these problems, we have developed and validated PolarisX which can automatically expand a knowledge graph, by crawling and analyzing the news sites and social media in real-time. We utilize and fine-tune the pre-trained multilingual BERT model for the construction of knowledge graphs without language dependencies. We extract new relationships using the BERT-based relation extraction model and integrate them into the knowledge graph. We verify the novelty and accuracy of PolarisX. It deals with neologisms and does not have language dependencies. © 2019 Elsevier Ltd. All rights reserved." ; default4:contributor "OkRan Jeong", "SoYeop Yoo" ; default4:date "2019" ; default4:title "Automating the expansion of a knowledge graph" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "SoYeop" ; default5:fullName "SoYeop Yoo" ; default5:initials "S." ; default5:lastName "Yoo" ] [ a default5:AuthorName ; default5:firstNames "OkRan" ; default5:fullName "OkRan Jeong" ; default5:initials "O." ; default5:lastName "Jeong" ] ) ; default5:authorStr "Yoo & Jeong" ; default5:id "fda8ce4cc17b9320a35d2fc0c76ffcedd0be422f" ; default5:refCount 42 ; default2:abstract "Abstract In order to make computers understand human languages and to reason, human knowledge needs to be represented and stored in a form that can be processed by computers. Knowledge graphs have been developed for use as a form of the knowledge base for words and general relationships among words. However, they have two limitations. One is that the knowledge graph is limited in size and scope for most of the human languages. Another is that they are not able to deal with neologisms that form a part of the human common sense. Addressing these problems, we have developed and validated PolarisX which can automatically expand a knowledge graph, by crawling and analyzing the news sites and social media in real-time. We utilize and fine-tune the pre-trained multilingual BERT model for the construction of knowledge graphs without language dependencies. We extract new relationships using the BERT-based relation extraction model and integrate them into the knowledge graph. We verify the novelty and accuracy of PolarisX. It deals with neologisms and does not have language dependencies." ; default2:contributor , ; default2:date "2020" ; default2:subject , , , , , ; default2:title "Automating the expansion of a knowledge graph" ; default3:doi "10.1016/j.eswa.2019.112965" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Expert Syst. Appl." . a , default3:Article, default1:Paper ; default4:abstract "Internet is prevalent in our lives nowadays; access to it, is now easy and at high speeds and the availability of small portable devices has made it an integral part of our everyday life. It constitutes a basic medium for keeping ourselves updated. To this direction, following the news on the internet is gaining ground every day. Moreover, thousands of news items are produced everyday across the earth; whether important or trivial, they need to be saved, archived, searched and indexed. Information does not only involve pieces of text, but also images, video and audio files that complement the news articles and make their impact more significant. Lately, with the rise of social platforms, news articles can be shared through social networks, commented or even “followed” by external users. In this exploding and demanding landscape, numerous research works focus on managing news articles. For modelling news articles for example BBC created its own ontology. IPTC on the other hand, the global standards body of the news media, generated some widely used mark-up languages such as NewsML and" ; default4:contributor "Anna Maridaki", "Aris Papakonstantinou", "Dawid Wiśniewski", "George Vavoulas", "Haridimos Kondylakis", "Konstantinos Iatrou", "Koralia Papadokostaki", "Nikos Papadakis", "Paraskevi Piperaki", "Piotr Arent", "Savvas Lemonakis", "Stavros Charitakis", "Stella Panou" ; default4:date "2017" ; default4:title "News Articles Platform: Semantic Tools and Services for Aggregating and Exploring News Articles" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Koralia" ; default5:fullName "Koralia Papadokostaki" ; default5:initials "K." ; default5:lastName "Papadokostaki" ] [ a default5:AuthorName ; default5:firstNames "Stavros" ; default5:fullName "Stavros Charitakis" ; default5:initials "S." ; default5:lastName "Charitakis" ] [ a default5:AuthorName ; default5:firstNames "George" ; default5:fullName "George Vavoulas" ; default5:initials "G." ; default5:lastName "Vavoulas" ] [ a default5:AuthorName ; default5:firstNames "Stella" ; default5:fullName "Stella Panou" ; default5:initials "S." ; default5:lastName "Panou" ] [ a default5:AuthorName ; default5:firstNames "Paraskevi" ; default5:fullName "Paraskevi Piperaki" ; default5:initials "P." ; default5:lastName "Piperaki" ] [ a default5:AuthorName ; default5:firstNames "Aris" ; default5:fullName "Aris Papakonstantinou" ; default5:initials "A." ; default5:lastName "Papakonstantinou" ] [ a default5:AuthorName ; default5:firstNames "Savvas" ; default5:fullName "Savvas Lemonakis" ; default5:initials "S." ; default5:lastName "Lemonakis" ] [ a default5:AuthorName ; default5:firstNames "Anna" ; default5:fullName "Anna Maridaki" ; default5:initials "A." ; default5:lastName "Maridaki" ] [ a default5:AuthorName ; default5:firstNames "Konstantinos" ; default5:fullName "Konstantinos Iatrou" ; default5:initials "K." ; default5:lastName "Iatrou" ] [ a default5:AuthorName ; default5:firstNames "Piotr" ; default5:fullName "Piotr Arent" ; default5:initials "P." ; default5:lastName "Arent" ] [ a default5:AuthorName ; default5:firstNames "Dawid" ; default5:fullName "Dawid Wiśniewski" ; default5:initials "D." ; default5:lastName "Wiśniewski" ] [ a default5:AuthorName ; default5:firstNames "Nikos" ; default5:fullName "Nikos Papadakis" ; default5:initials "N." ; default5:lastName "Papadakis" ] [ a default5:AuthorName ; default5:firstNames "Haridimos" ; default5:fullName "Haridimos Kondylakis" ; default5:initials "H." ; default5:lastName "Kondylakis" ] ) ; default5:authorStr "Papadokostaki et al." ; default5:id "6b590c8f59974a079e532a8542115c948763ebaf" ; default5:refCount 9 ; default2:abstract "As the Internet grows daily and millions of news articles are produced everyday worldwide by various sources, the need to store, index, search, and explore news articles is more than prominent. In this paper, we present an integrated platform dedicated to news articles, providing storage, indexing and searching functionalities, implemented using semantic web technologies and services. Besides using the developed APIs, the users through intuitive graphical user interfaces can save articles from RSS channels, import them through wrappers from external news sites or manually insert them using forms. A search engine on top allows the users to explore all registered information. All components have been implemented using semantic web technologies, using a novel ontology to model the news domain, a triple store for the management of data and web services exchanging JSON-DL messages. The registered articles become automatically part of the Linked Open Data cloud, enabling better data and knowledge sharing. Our preliminary evaluation shows the high quality of the developed platform and the benefits of our approach." ; default2:contributor , , , , , , , , , , , , ; default2:date "2017" ; default2:isReferencedBy ; default2:subject , , , , , , , , , , ; default2:title "News Articles Platform: Semantic Tools and Services for Aggregating and Exploring News Articles" ; default3:doi "10.1007/978-3-319-56288-9_68" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "__MISSING__" . a , default3:Article, default1:Paper ; default4:abstract "Flight Center and other organizations conducting research and applications in earth science. So, we’re continually challenged with providing improved information systems infrastructures in a complex environment inundated with extensive heterogeneous and disparate information sources. To achieve the desired capabilities for expanded use of the science, data, and technologies generated by and for our customers, we must identify, evaluate, and develop new strategies for automating knowledge management and decision support. Semantic Web technologies could enable a new generation of earth science information systems that" ; default4:date "2004" ; default4:title "AnnoTerra: building an integrated earth science resource using semantic Web technologies" ; default5:authorList () ; default5:authorStr "" ; default5:id "f8be6a1a3704ffa0fa2a8eff218eeb3c75e6523a" ; default5:refCount 4 ; default2:abstract "We discuss about AnnoTerra, a prototype knowledge-based system that uses semantic Web technologies to present enhanced earth science news feeds by making focused semantic searches on NASA resource catalogs using earth science concepts and relationships." ; default2:contributor , , ; default2:date "2004" ; default2:isReferencedBy , ; default2:references , , ; default2:subject , , ; default2:title "AnnoTerra: building an integrated earth science resource using semantic Web technologies" ; default3:doi "10.1109/MIS.2004.3" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "IEEE Intelligent Systems" . a , default3:Article, default1:Paper ; default4:abstract "0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.06.003 ⇑ Corresponding author. Fax: +34 868884151. E-mail addresses: elupiani@um.es (E. Lupian (I. García-Manotas), valencia@um.es (R. Valen (F. García-Sánchez), dcastellanos@um.es (D. Castella (J.T. Fernández-Breis), jbcamon@um.es (J.B. Camón-H An increasingly large amount of financial information available in a number of heterogeneous business sources implies that the traditional methods of analysis are no longer applicable. These financial data sources are characterized by the use of disparate data models and their unstructured content with implicit knowledge. In addition, the most up-to-date financial information typically resides in the vast amount of financial-related news that brokers take into account when investing. As Semantic Technologies mature, they provide a consistent and reliable basis for the development of superior, more precise mechanisms to deal with heterogeneous data. In this paper, we present a financial news semantic search engine based on Semantic Web technologies. The search engine is accompanied by an ontology population tool that assists in keeping the financial ontology up-to-date. In addition, a further module has been developed that is capable of crawling the Web in search of financial news and annotating it with knowledge entities from the financial ontology that match with the contents of the news. Our contribution is an overall solution based on a fully fledged architecture that has been validated in a use case scenario for the Spanish stock exchange. 2011 Elsevier Ltd. All rights reserved." ; default4:contributor "Dagoberto Castellanos-Nieves", "Eduardo Lupiani-Ruiz", "Francisco García-Sánchez", "Ignacio García-Manotas", "Jesualdo Tomás Fernández-Breis", "Juan Bosco Camón-Herrero", "Rafael Valencia-García" ; default4:date "2011" ; default4:title "Financial news semantic search engine" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Eduardo" ; default5:fullName "Eduardo Lupiani-Ruiz" ; default5:initials "E." ; default5:lastName "Lupiani-Ruiz" ] [ a default5:AuthorName ; default5:firstNames "Ignacio" ; default5:fullName "Ignacio García-Manotas" ; default5:initials "I." ; default5:lastName "García-Manotas" ] [ a default5:AuthorName ; default5:firstNames "Rafael" ; default5:fullName "Rafael Valencia-García" ; default5:initials "R." ; default5:lastName "Valencia-García" ] [ a default5:AuthorName ; default5:firstNames "Francisco" ; default5:fullName "Francisco García-Sánchez" ; default5:initials "F." ; default5:lastName "García-Sánchez" ] [ a default5:AuthorName ; default5:firstNames "Dagoberto" ; default5:fullName "Dagoberto Castellanos-Nieves" ; default5:initials "D." ; default5:lastName "Castellanos-Nieves" ] [ a default5:AuthorName ; default5:firstNames "Jesualdo Tomás" ; default5:fullName "Jesualdo Tomás Fernández-Breis" ; default5:initials "J.T." ; default5:lastName "Fernández-Breis" ] [ a default5:AuthorName ; default5:firstNames "Juan Bosco" ; default5:fullName "Juan Bosco Camón-Herrero" ; default5:initials "J.B." ; default5:lastName "Camón-Herrero" ] ) ; default5:authorStr "Lupiani-Ruiz et al." ; default5:id "6ca2a1fc1fe6249145cd769175c695d6d2776236" ; default5:refCount 24 ; default2:abstract "An increasingly large amount of financial information available in a number of heterogeneous business sources implies that the traditional methods of analysis are no longer applicable. These financial data sources are characterized by the use of disparate data models and their unstructured content with implicit knowledge. In addition, the most up-to-date financial information typically resides in the vast amount of financial-related news that brokers take into account when investing. As Semantic Technologies mature, they provide a consistent and reliable basis for the development of superior, more precise mechanisms to deal with heterogeneous data. In this paper, we present a financial news semantic search engine based on Semantic Web technologies. The search engine is accompanied by an ontology population tool that assists in keeping the financial ontology up-to-date. In addition, a further module has been developed that is capable of crawling the Web in search of financial news and annotating it with knowledge entities from the financial ontology that match with the contents of the news. Our contribution is an overall solution based on a fully fledged architecture that has been validated in a use case scenario for the Spanish stock exchange." ; default2:contributor , , , , , , ; default2:date "2011" ; default2:isReferencedBy , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , ; default2:title "Financial news semantic search engine" ; default3:doi "10.1016/j.eswa.2011.06.003" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Expert Syst. Appl." . a , default3:Article, default1:Paper ; default4:abstract "Purpose – The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems. Design/methodology/approach – The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (named entity recognition). The semantic extraction rules are constructed based on defined language models and the developed ontology. Findings – The proposed method is implemented as a part of the sport news semantic annotations-generating prototype BKAnnotation. This component is a part of the sport integration system based on Web Semantics BKSport. The semantic annotations generated are used for improving features of news searching – sorting – association. The experiments on the news data from SkySport (2014) channel showed positive results. The precisions achieved in both cases, with and without integration of the pronoun recognition method, are both over 80 per cent. In particular, the latter helps increase the recall value to around 10 per cent. Originality/value – This is one of the initial proposals in automatic creation of semantic data about news, football news in particular and sport news in general. The combination of ontology, knowledge base and patterns of language model allows detection of not only entities with corresponding types but also semantic triples. At the same time, the authors propose a pronoun recognition method using extraction rules to improve the relation recognition process." ; default4:contributor "Quang-Minh Nguyen", "Tuan-Dung Cao" ; default4:date "2015" ; default4:title "A novel approach for automatic extraction of semantic data about football transfer in sport news" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Quang-Minh" ; default5:fullName "Quang-Minh Nguyen" ; default5:initials "Q." ; default5:lastName "Nguyen" ] [ a default5:AuthorName ; default5:firstNames "Tuan-Dung" ; default5:fullName "Tuan-Dung Cao" ; default5:initials "T." ; default5:lastName "Cao" ] ) ; default5:authorStr "Nguyen & Cao" ; default5:id "c8bfd9e67a8467ffaeaa4798aaebe1ff22fd3fff" ; default5:refCount 31 ; default2:abstract "Purpose – The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems. Design/methodology/approach – The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (name..." ; default2:contributor , ; default2:date "2015" ; default2:subject , , , , , , , , , , , ; default2:title "A novel approach for automatic extraction of semantic data about football transfer in sport news" ; default3:doi "10.1108/IJPCC-03-2015-0018" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Int. J. Pervasive Computing and Communications" . a , default3:Article, default1:Paper ; default4:abstract "In this paper, we present the KnowledgeSeeker, an ontological agent-based system that is designed to help users find, retrieve, and analyze news article from the Internet and then present the content in a semantic web. We present the benefits of using ontologies to analyze the semantics of Chinese text, and also the advantages of using a semantic web to organize information semantically. KnowledgeSeeker also demonstrates the advantages of using ontologies to identify topics. We use a Chinese document corpus to evaluate KnowledgeSeeker and the testing result was compared to other approaches. KnowledgeSeeker is able to identify the topics of Chinese web articles with an accuracy of nearly 87% and has a processing speed of less than one second per article. It is also able to organize content flexibly and understands knowledge more accurately than methods that use ontology definition." ; default4:contributor "James N. K. Liu" ; default4:date "2008" ; default4:title "KnowledgeSeeker - An Ontological Agent-based System for Retrieving and Analyzing Chinese Web Articles" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "James N. K." ; default5:fullName "James N. K. Liu" ; default5:initials "J.N.K." ; default5:lastName "Liu" ] ) ; default5:authorStr "Liu" ; default5:id "25577a41e33794958de3fe2d723e15c8e57f2a05" ; default5:refCount 17 ; default2:abstract "In this paper, we present the KnowledgeSeeker, an ontological agent-based system that is designed to help users find, retrieve, and analyze news article from the Internet and then present the content in a semantic web. We present the benefits of using ontologies to analyze the semantics of Chinese text, and also the advantages of using a semantic web to organize information semantically. KnowledgeSeeker also demonstrates the advantages of using ontologies to identify topics. We use a Chinese document corpus to evaluate KnowledgeSeeker and the testing result was compared to other approaches. KnowledgeSeeker is able to identify the topics of Chinese web articles with an accuracy of nearly 87% and has a processing speed of less than one second per article. It is also able to organize content flexibly and understands knowledge more accurately than methods that use ontology definition." ; default2:contributor , , ; default2:date "2008" ; default2:isReferencedBy , , , ; default2:references , , , , , ; default2:subject , , , ; default2:title "KnowledgeSeeker — an ontological agent-based system for retrieving and analyzing Chinese web articles" ; default3:doi "10.1109/FUZZY.2008.4630497" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)" . a , default3:Article, default1:Paper ; default4:abstract "The value in the vast amount of linked data and open data produced during the last decade is widely recognized and being exploited by different initiatives. However, a remaining challenge is to integrate government information with semi-structured data in sources relevant to citizens, who have become skeptical of official versions and more interested in information associated with their own interests and values. We present a system that integrates and provides uniform access to government data linked to news portals, via an automated named entity linking process, and information provided by a parliament monitoring organization. We develop a prototype to show how this system can be used to develop semantic web applications that assist citizens in making informed political decisions using data linked to their interests and sources not affiliated with the government. This enables them to contrast the official information and find political figures associated to their own personal interests." ; default4:contributor "Claudia Jiménez-Guarín", "Daniel Sarmiento Suárez" ; default4:date "2014" ; default4:title "Natural Language Processing for Linking Online News and Open Government Data" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Daniel Sarmiento" ; default5:fullName "Daniel Sarmiento Suárez" ; default5:initials "D.S." ; default5:lastName "Suárez" ] [ a default5:AuthorName ; default5:firstNames "Claudia" ; default5:fullName "Claudia Jiménez-Guarín" ; default5:initials "C." ; default5:lastName "Jiménez-Guarín" ] ) ; default5:authorStr "Suárez & Jiménez-Guarín" ; default5:id "33aaedc155b257709303073582e38bf7f0ae6415" ; default5:refCount 11 ; default2:abstract "The value in the vast amount of linked data and open data produced during the last decade is widely recognized and being exploited by different initiatives. However, a remaining challenge is to integrate government information with semi-structured data in sources relevant to citizens, who have become skeptical of official versions and more interested in information associated with their own interests and values. We present a system that integrates and provides uniform access to government data linked to news portals, via an automated named entity linking process, and information provided by a parliament monitoring organization. We develop a prototype to show how this system can be used to develop semantic web applications that assist citizens in making informed political decisions using data linked to their interests and sources not affiliated with the government. This enables them to contrast the official information and find political figures associated to their own personal interests." ; default2:contributor , ; default2:date "2014" ; default2:isReferencedBy ; default2:subject , , , , , , , , , ; default2:title "Natural Language Processing for Linking Online News and Open Government Data" ; default3:doi "10.1007/978-3-319-12256-4_26" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "ER Workshops" . a , default3:Article, default1:Paper ; default4:abstract "Agent-based computing environments provide the capability to establish an enterprise of autonomous agents that collaborate and share information on common interests. Discovery of new data can alert users to changes in the information environment of importance to them. The goal of this research is to define an agent environment that models an organization’s tacit knowledge. This document proposes a process to find named entities in the content of documents and to derive context from a user community. The EKA system ingests RSS feeds of data sources that are representative of separate users, extracts entities from the text, and provides context on each entity using related tags. Knowledge representations of the news feeds are created, managed, and reasoned over by a collection of agents. Agents will respond to pre-registered topics and provide notifications when those topics have been identified. The purpose here is to illustrate that an agent can serve as a broker of knowledge and assist users in identifying like-minded individuals through an analysis of the data the user is working with and initiate collaboration." ; default4:contributor "George Mason", "Josh Church", "Ruhollah Farchtchi" ; default4:date "2009" ; default4:title "Enterprise Knowledge Agents: Agent-based Discovery of Like-minded Individuals" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Josh" ; default5:fullName "Josh Church" ; default5:initials "J." ; default5:lastName "Church" ] [ a default5:AuthorName ; default5:firstNames "Ruhollah" ; default5:fullName "Ruhollah Farchtchi" ; default5:initials "R." ; default5:lastName "Farchtchi" ] [ a default5:AuthorName ; default5:firstNames "George" ; default5:fullName "George Mason" ; default5:initials "G." ; default5:lastName "Mason" ] ) ; default5:authorStr "Church et al." ; default5:id "290f56dd79c2bcccd7c12454067bf7759aac7ba7" ; default5:refCount 7 ; default2:abstract "Agent-based computing environments provide the capability to establish an enterprise of autonomous agents that collaborate and share information on common interests. Discovery of new data can alert users to changes in the information environment of importance to them. The goal of this research is to define an agent environment that models an organization's tacit knowledge. This document proposes a process to find named entities in the content of documents and to derive context from a user community. The EKA system ingests RSS feeds of data sources that are representative of separate users, extracts entities from the text, and provides context on each entity using related tags. Knowledge representations of the news feeds are created, managed, and reasoned over by a collection of agents. Agents will respond to pre-registered topics and provide notifications when those topics have been identified. The purpose here is to illustrate that an agent can serve as a broker of knowledge and assist users in identifying like-minded individuals through an analysis of the data the user is working with and initiate collaboration." ; default2:contributor , ; default2:date "2008" ; default2:references , , , , , , ; default2:subject , , , , , , ; default2:title "Enterprise Knowledge Agents: Agent-Based Discovery of Like-Minded Individuals" ; default3:doi "10.1109/CECandEEE.2008.96" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services" . a , default3:Article, default1:Paper ; default4:abstract "We present a system for browsing a news repository that is based on semantic similarity of documents. News documents get automatically annotated semantically using information extraction. Annotations are displayed to a user who can easily retrieve crosslingual semantically related documents by selecting interesting content items." ; default4:contributor "Jörg Steffen", "Walter Kasper", "Yajing Zhang" ; default4:date "2008" ; default4:title "News Annotations for Navigation by Semantic Similarity" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Walter" ; default5:fullName "Walter Kasper" ; default5:initials "W." ; default5:lastName "Kasper" ] [ a default5:AuthorName ; default5:firstNames "Jörg" ; default5:fullName "Jörg Steffen" ; default5:initials "J." ; default5:lastName "Steffen" ] [ a default5:AuthorName ; default5:firstNames "Yajing" ; default5:fullName "Yajing Zhang" ; default5:initials "Y." ; default5:lastName "Zhang" ] ) ; default5:authorStr "Kasper et al." ; default5:id "550d6bb432c9cf469409ebd1e7c76aa16e78bddf" ; default5:refCount 12 ; default2:abstract "We present a system for browsing a news repository that is based on semantic similarity of documents. News documents get automatically annotated semantically using information extraction. Annotations are displayed to a user who can easily retrieve crosslingual semantically related documents by selecting interesting content items." ; default2:contributor , , ; default2:date "2008" ; default2:isReferencedBy ; default2:subject , ; default2:title "News Annotations for Navigation by Semantic Similarity" ; default3:doi "10.1007/978-3-540-85845-4_29" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "KI" . a , default3:Article, default1:Paper ; default4:abstract "Although RDF/XML has been widely recognized as the standard vehicle for representing semantic information on the Web, an enormous amount of semantic data is still being encoded in HTML documents that are designed primarily for human consumption and not directly amenable to machine processing. This paper seeks to bridge this semantic gap by addressing the fundamental problem of automatically annotating HTML documents with semantic labels. Exploiting a key observation that semantically related items exhibit consistency in presentation style as well as spatial locality in template-based content-rich HTML documents, we have developed a novel framework for automatically partitioning such documents into semantic structures. Our framework tightly couples structural analysis of documents with semantic analysis incorporating domain ontologies and lexical databases such as WordNet. We present experimental evidence of the effectiveness of our techniques on a large collection of HTML documents from various news portals." ; default4:contributor "Guizhen Yang", "I.V. Ramakrishnan", "Saikat Mukherjee" ; default4:date "2003" ; default4:title "Automatic Annotation of Content-Rich HTML Documents: Structural and Semantic Analysis" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Saikat" ; default5:fullName "Saikat Mukherjee" ; default5:initials "S." ; default5:lastName "Mukherjee" ] [ a default5:AuthorName ; default5:firstNames "Guizhen" ; default5:fullName "Guizhen Yang" ; default5:initials "G." ; default5:lastName "Yang" ] [ a default5:AuthorName ; default5:firstNames "I.V." ; default5:fullName "I.V. Ramakrishnan" ; default5:initials "I." ; default5:lastName "Ramakrishnan" ] ) ; default5:authorStr "Mukherjee et al." ; default5:id "173c4d17ea61b896248db4f62a201f20a8304f5f" ; default5:refCount 32 ; default2:abstract "Although RDF/XML has been widely recognized as the standard vehicle for representing semantic information on the Web, an enormous amount of semantic data is still being encoded in HTML documents that are designed primarily for human consumption and not directly amenable to machine processing. This paper seeks to bridge this semantic gap by addressing the fundamental problem of automatically annotating HTML documents with semantic labels. Exploiting a key observation that semantically related items exhibit consistency in presentation style as well as spatial locality in template-based content-rich HTML documents, we have developed a novel framework for automatically partitioning such documents into semantic structures. Our framework tightly couples structural analysis of documents with semantic analysis incorporating domain ontologies and lexical databases such as WordNet. We present experimental evidence of the effectiveness of our techniques on a large collection of HTML documents from various news portals." ; default2:contributor , , ; default2:date "2003" ; default2:isReferencedBy , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , ; default2:title "Automatic Annotation of Content-Rich HTML Documents: Structural and Semantic Analysis" ; default3:doi "10.1007/978-3-540-39718-2_34" ; default1:citationVelocity 0 ; default1:influentialCitationCount 5 ; default1:url ; default1:venue "International Semantic Web Conference" . a , default3:Article, default1:Paper ; default4:abstract "In order to understand news, dependency patterns between objects in (economic) news items have to be detected. We propose a framework which makes it possible to discover these patterns, and support the observations with statistical analysis. Based on these patterns, alerts can be generated based on emerging news. These alerts can then be used to manage (equity) portfolios. We test our framework based on historical data. The tests show statistically significant results supporting the idea that it is possible to discover such dependency patterns between objects in news items." ; default4:contributor "Flavius Frasincar", "Maarten Jongmans", "Viorel Milea(B" ; default4:date "2014" ; default4:title "A Semantic Web Approach for Visualization-Based News Analytics" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Maarten" ; default5:fullName "Maarten Jongmans" ; default5:initials "M." ; default5:lastName "Jongmans" ] [ a default5:AuthorName ; default5:firstNames "Viorel" ; default5:fullName "Viorel Milea(B" ; default5:initials "V." ; default5:lastName "Milea(B" ] [ a default5:AuthorName ; default5:firstNames "Flavius" ; default5:fullName "Flavius Frasincar" ; default5:initials "F." ; default5:lastName "Frasincar" ] ) ; default5:authorStr "Jongmans et al." ; default5:id "8e6776f27e5dd945e20a1cc7a0a63842f8e9b575" ; default5:refCount 13 ; default2:abstract "In order to understand news, dependency patterns between objects in (economic) news items have to be detected. We propose a framework which makes it possible to discover these patterns, and support the observations with statistical analysis. Based on these patterns, alerts can be generated based on emerging news. These alerts can then be used to manage (equity) portfolios. We test our framework based on historical data. The tests show statistically significant results supporting the idea that it is possible to discover such dependency patterns between objects in news items." ; default2:contributor , , ; default2:date "2014" ; default2:references , , , , , , , , , , , ; default2:subject , , , , ; default2:title "A Semantic Web Approach for Visualization-Based News Analytics" ; default3:doi "10.1007/978-3-319-08618-7_20" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "KMO" . a , default3:Article, default1:Paper ; default4:abstract "As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal workflows. In this paper, we propose a new approach for automatic discovery of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73% of the original sources of 410 news stories, at 68% precision. Lastly, we discuss possible improvements and future work." ; default4:contributor "Davy Van Deursen", "Erik Mannens", "Rik Van de Walle", "Sam Coppens", "Tom De Nies" ; default4:date "2012" ; default4:title "Automatic Discovery of High-Level Provenance Using Semantic Similarity" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Tom De" ; default5:fullName "Tom De Nies" ; default5:initials "T.D." ; default5:lastName "Nies" ] [ a default5:AuthorName ; default5:firstNames "Sam" ; default5:fullName "Sam Coppens" ; default5:initials "S." ; default5:lastName "Coppens" ] [ a default5:AuthorName ; default5:firstNames "Davy Van" ; default5:fullName "Davy Van Deursen" ; default5:initials "D.V." ; default5:lastName "Deursen" ] [ a default5:AuthorName ; default5:firstNames "Erik" ; default5:fullName "Erik Mannens" ; default5:initials "E." ; default5:lastName "Mannens" ] [ a default5:AuthorName ; default5:firstNames "Rik Van de" ; default5:fullName "Rik Van de Walle" ; default5:initials "R.V.d." ; default5:lastName "Walle" ] ) ; default5:authorStr "Nies et al." ; default5:id "9a0d04cdd8dc53dc1063ec764fb8abac48c28fc2" ; default5:refCount 12 ; default2:abstract "As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal workflows. In this paper, we propose a new approach for automatic discovery of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73% of the original sources of 410 news stories, at 68% precision. Lastly, we discuss possible improvements and future work." ; default2:contributor , , , , ; default2:date "2012" ; default2:isReferencedBy , , , , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , ; default2:subject , , , , , , , ; default2:title "Automatic Discovery of High-Level Provenance Using Semantic Similarity" ; default3:doi "10.1007/978-3-642-34222-6_8" ; default1:citationVelocity 0 ; default1:influentialCitationCount 3 ; default1:url ; default1:venue "IPAW" . a , default3:Article, default1:Paper ; default4:abstract "We propose a two-fold approach that is able to both consume and exploit semantics encoded in the Linking Open Data (LOD) cloud, and create news that document events reported in micro-blogging posts that correspond to documentary tweets. A documentary tweet is similar to a newspaper headline and reports an incident or event. Knowledge extracted from documentary tweets are used to develop a story line which will be augmented with RDF facts consumed from the LOD cloud. The resulting news content is represented in RDF using the rNews Ontology, facilitating news generation and retrieval. We study effectiveness of our approach with respect to a gold standard of manually tagged tweets. Initial experimental results suggest that our techniques are able to generate content that reflects up to 76.38% of the manually tagged terms." ; default4:contributor "Francisco Berrizbeita", "M.-E. Vidal" ; default4:date "2014" ; default4:title "Traversing the Linking Open Data Cloud to Create News from Tweets" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Francisco" ; default5:fullName "Francisco Berrizbeita" ; default5:initials "F." ; default5:lastName "Berrizbeita" ] [ a default5:AuthorName ; default5:firstNames "M.-E." ; default5:fullName "M.-E. Vidal" ; default5:initials "M." ; default5:lastName "Vidal" ] ) ; default5:authorStr "Berrizbeita & Vidal" ; default5:id "46e16bee35c56a036036acd4f7c0ac04bfead6e0" ; default5:refCount 8 ; default2:abstract "We propose a two-fold approach that is able to both consume and exploit semantics encoded in the Linking Open Data LOD cloud, and create news that document events reported in micro-blogging posts that correspond to documentary tweets. A documentary tweet is similar to a newspaper headline and reports an incident or event. Knowledge extracted from documentary tweets are used to develop a story line which will be augmented with RDF facts consumed from the LOD cloud. The resulting news content is represented in RDF using the rNews Ontology, facilitating news generation and retrieval. We study effectiveness of our approach with respect to a gold standard of manually tagged tweets. Initial experimental results suggest that our techniques are able to generate content that reflects up to 76.38% of the manually tagged terms." ; default2:contributor , ; default2:date "2014" ; default2:subject , , , ; default2:title "Traversing the Linking Open Data Cloud to Create News from Tweets" ; default3:doi "10.1007/978-3-662-45550-0_48" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "OTM Workshops" . a , default3:Article, default1:Paper ; default4:abstract "Given the intense attention that environmental topics such as climate change attract in news and social media coverage, scientists and communication professionals want to know how different stakeholders perceive observable threats and policy options, how specific media channels react to new insights, and how journalists present scientific knowledge to the public. This paper investigates the potential of semantic technologies to address these questions. After summarizing methods to extract and disambiguate context information, we present visualization techniques to explore the lexical, geospatial, and relational context of topics and entities referenced in these repositories. The examples stem from the Media Watch on Climate Change, the Climate Resilience Toolkit and the NOAA Media Watch—three applications that aggregate environmental resources from a wide range of online sources. These systems not only show the value of providing comprehensive information to the public, but also have helped to develop a novel communication success metric that goes beyond bipolar assessments of sentiment." ; default4:contributor "Albert Weichselbraun", "Alexander Hubmann-Haidvogel", "Arno Scharl", "Daniel Fischl", "David Herring", "Michael Föls", "Ruslan Kamolov", "Walter Rafelsberger" ; default4:date "2017" ; default4:title "Semantic Systems and Visual Tools to Support Environmental Communication" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Arno" ; default5:fullName "Arno Scharl" ; default5:initials "A." ; default5:lastName "Scharl" ] [ a default5:AuthorName ; default5:firstNames "David" ; default5:fullName "David Herring" ; default5:initials "D." ; default5:lastName "Herring" ] [ a default5:AuthorName ; default5:firstNames "Walter" ; default5:fullName "Walter Rafelsberger" ; default5:initials "W." ; default5:lastName "Rafelsberger" ] [ a default5:AuthorName ; default5:firstNames "Alexander" ; default5:fullName "Alexander Hubmann-Haidvogel" ; default5:initials "A." ; default5:lastName "Hubmann-Haidvogel" ] [ a default5:AuthorName ; default5:firstNames "Ruslan" ; default5:fullName "Ruslan Kamolov" ; default5:initials "R." ; default5:lastName "Kamolov" ] [ a default5:AuthorName ; default5:firstNames "Daniel" ; default5:fullName "Daniel Fischl" ; default5:initials "D." ; default5:lastName "Fischl" ] [ a default5:AuthorName ; default5:firstNames "Michael" ; default5:fullName "Michael Föls" ; default5:initials "M." ; default5:lastName "Föls" ] [ a default5:AuthorName ; default5:firstNames "Albert" ; default5:fullName "Albert Weichselbraun" ; default5:initials "A." ; default5:lastName "Weichselbraun" ] ) ; default5:authorStr "Scharl et al." ; default5:id "77e5237117ef40385e6bf4dbfa0cbd666631644d" ; default5:refCount 20 ; default2:abstract "Given the intense attention that environmental topics such as climate change attract in news and social media coverage, scientists and communication professionals want to know how different stakeholders perceive observable threats and policy options, how specific media channels react to new insights, and how journalists present scientific knowledge to the public. This paper investigates the potential of semantic technologies to address these questions. After summarizing methods to extract and disambiguate context information, we present visualization techniques to explore the lexical, geospatial, and relational context of topics and entities referenced in these repositories. The examples stem from the Media Watch on Climate Change, the Climate Resilience Toolkit and the NOAA Media Watch—three applications that aggregate environmental resources from a wide range of online sources. These systems not only show the value of providing comprehensive information to the public, but also have helped to develop a novel communication success metric that goes beyond bipolar assessments of sentiment." ; default2:contributor , , , , , , , ; default2:date "2017" ; default2:isReferencedBy , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , ; default2:title "Semantic Systems and Visual Tools to Support Environmental Communication" ; default3:doi "10.1109/JSYST.2015.2466439" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "IEEE Systems Journal" . a , default3:Article, default1:Paper ; default4:abstract "Semantic enrichment of textual data is the operation of linking mentions with the entities they refer to, and the subsequent enrichment of such entities with the background knowledge about them available in one or more knowledge bases (or in the entire web). Information about the context in which a mention occurs, (e.g., information about the time, the topic, and the space, which the text is relative to) constitutes a critical resource for a correct semantic enrichment for two reasons. First, without context, mentions are “too little text” to unambiguously refer to a single entity. Second, knowledge about entities is also context dependent (e.g., speaking about political life of Illinois during 1996, Obama is a Senator, while since 2009, Obama is the US president). In this paper, we describe a concrete approach to contextdriven semantic enrichment, built upon four core sub-tasks: detection of mentions in text (i.e., finding references to people, locations and organizations); determination of the context of discourses of the text, identification of the referred entities in the knowledge base, and enrichment of the entity with the knowledge relevant to the context. In such approach, context-driven semantic enrichment needs also to have contextualized background knowledge. To cope with this aspect, we propose a customization of Sesame, one of state-of-the-art knowledge repositories, to support representation and reasoning with contextualized knowledge. The approach has been fully implemented in a system, which has been practically deployed and applied to the textual archive of the local Italian newspaper “L’Adige”, covering the decade of years from 1999 to" ; default4:contributor "Andrei Tamilin", "Bernardo Magnini", "Christian Girardi", "Luciano Serafini", "Mathew Joseph", "Roberto Zanoli" ; default4:date "2010" ; default4:title "Context-Driven Semantic Enrichment of Italian News Archive" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Andrei" ; default5:fullName "Andrei Tamilin" ; default5:initials "A." ; default5:lastName "Tamilin" ] [ a default5:AuthorName ; default5:firstNames "Bernardo" ; default5:fullName "Bernardo Magnini" ; default5:initials "B." ; default5:lastName "Magnini" ] [ a default5:AuthorName ; default5:firstNames "Luciano" ; default5:fullName "Luciano Serafini" ; default5:initials "L." ; default5:lastName "Serafini" ] [ a default5:AuthorName ; default5:firstNames "Christian" ; default5:fullName "Christian Girardi" ; default5:initials "C." ; default5:lastName "Girardi" ] [ a default5:AuthorName ; default5:firstNames "Mathew" ; default5:fullName "Mathew Joseph" ; default5:initials "M." ; default5:lastName "Joseph" ] [ a default5:AuthorName ; default5:firstNames "Roberto" ; default5:fullName "Roberto Zanoli" ; default5:initials "R." ; default5:lastName "Zanoli" ] ) ; default5:authorStr "Tamilin et al." ; default5:id "fe4cd3c88bace9bf529899ec9430ca49140ad65e" ; default5:refCount 14 ; default2:abstract "Semantic enrichment of textual data is the operation of linking mentions with the entities they refer to, and the subsequent enrichment of such entities with the background knowledge about them available in one or more knowledge bases (or in the entire web). Information about the context in which a mention occurs, (e.g., information about the time, the topic, and the space, which the text is relative to) constitutes a critical resource for a correct semantic enrichment for two reasons. First, without context, mentions are “too little text” to unambiguously refer to a single entity. Second, knowledge about entities is also context dependent (e.g., speaking about political life of Illinois during 1996, Obama is a Senator, while since 2009, Obama is the US president). In this paper, we describe a concrete approach to context-driven semantic enrichment, built upon four core sub-tasks: detection of mentions in text (i.e., finding references to people, locations and organizations); determination of the context of discourses of the text, identification of the referred entities in the knowledge base, and enrichment of the entity with the knowledge relevant to the context. In such approach, context-driven semantic enrichment needs also to have contextualized background knowledge. To cope with this aspect, we propose a customization of Sesame, one of state-of-the-art knowledge repositories, to support representation and reasoning with contextualized knowledge. The approach has been fully implemented in a system, which has been practically deployed and applied to the textual archive of the local Italian newspaper “L'Adige”, covering the decade of years from 1999 to 2009." ; default2:contributor , , , , , ; default2:date "2010" ; default2:isReferencedBy , , , , ; default2:references , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , , ; default2:title "Context-Driven Semantic Enrichment of Italian News Archive" ; default3:doi "10.1007/978-3-642-13486-9_25" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "ESWC" . a , default3:Article, default1:Paper ; default4:abstract "We describe our research on automatically generating rich s emantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenge s involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementa tio of the theory of ontological semantics under continuous development for over fifteen years, uses a specia lly onstructed NLP-oriented ontology and an ontologicalsemantic lexicon to translate English text into a custom ont ol gy-motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem c oncentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To ada pt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the T MR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an e xperimental web service that monitors RSS news sources, processes the summaries of the news stories an d publishes a structured representation of the meaning of the text in the news story." ; default4:contributor "Akshay Java", "Anupam Joshi", "Jesse English", "Marjorie McShane", "Sergei Nirneburg", "Timothy F inin" ; default4:date "2007" ; default4:title "Using a Natural Language Understanding System to Generate Semantic Web Content" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Akshay" ; default5:fullName "Akshay Java" ; default5:initials "A." ; default5:lastName "Java" ] [ a default5:AuthorName ; default5:firstNames "Sergei" ; default5:fullName "Sergei Nirneburg" ; default5:initials "S." ; default5:lastName "Nirneburg" ] [ a default5:AuthorName ; default5:firstNames "Marjorie" ; default5:fullName "Marjorie McShane" ; default5:initials "M." ; default5:lastName "McShane" ] [ a default5:AuthorName ; default5:firstNames "Timothy F" ; default5:fullName "Timothy F inin" ; default5:initials "T.F." ; default5:lastName "inin" ] [ a default5:AuthorName ; default5:firstNames "Jesse" ; default5:fullName "Jesse English" ; default5:initials "J." ; default5:lastName "English" ] [ a default5:AuthorName ; default5:firstNames "Anupam" ; default5:fullName "Anupam Joshi" ; default5:initials "A." ; default5:lastName "Joshi" ] ) ; default5:authorStr "Java et al." ; default5:id "abce40408b4ee2464bb08284fbc93c613c5a120a" ; default5:refCount 44 ; default2:abstract "We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over fifteen years, uses a specia lly constructed NLP-oriented ontology and an ontologicalsemantic lexicon to translate English text into a custom ontology-motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an e xperimental web service that monitors RSS news sources, processes the summaries of the news stories and publishes a structured representation of the meaning of the text in the news story." ; default2:contributor , , , , , ; default2:date "2007" ; default2:isReferencedBy , , , , , , , , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , ; default2:title "Using a Natural Language Understanding System to Generate Semantic Web Content" ; default3:doi "10.4018/jswis.2007100103" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "Int. J. Semantic Web Inf. Syst." . a , default3:Article, default1:Paper ; default4:abstract "This paper proposes a semantic computing application for a news delivering service collects and recommends news articles which user feels interested in based on semantic relations between terms in the articles. We de ̄ne interested articles as the ones that users have curiosity and serendipity. The semantic relations between terms are represented by graphs of Linked Data. We ̄rst create News Articles Linked Data, which are news articles for recommendation to users and User's preferences Linked Data created from the users' preferred articles. Then, common subgraphs between two graphs are searched for the recommendation of news articles. The experiment showed that the curiosity score is 3.30 (min:0, max:4), and the serendipity score is 2.93 in our approach, but a baseline method indicated the curiosity score: 3.03 and the serendipity score: 2.79. Thus, we con ̄rmed that our approach is more e®ective than the baseline method. In the future, we will deploy our semantic technology to practical use for automatically delivering information selected from a vast amount of news sources." ; default4:contributor "Akihiko Ohsuga", "Ryohei Yokoo", "Takahiro Kawamura" ; default4:date "2017" ; default4:title "Semantics-Based News Delivering Service" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Ryohei" ; default5:fullName "Ryohei Yokoo" ; default5:initials "R." ; default5:lastName "Yokoo" ] [ a default5:AuthorName ; default5:firstNames "Takahiro" ; default5:fullName "Takahiro Kawamura" ; default5:initials "T." ; default5:lastName "Kawamura" ] [ a default5:AuthorName ; default5:firstNames "Akihiko" ; default5:fullName "Akihiko Ohsuga" ; default5:initials "A." ; default5:lastName "Ohsuga" ] ) ; default5:authorStr "Yokoo et al." ; default5:id "f9829f93d75354d3fed17cf7287c56f11006017f" ; default5:refCount 20 ; default2:contributor , , ; default2:date "2016" ; default2:title "Semantics-Based News Delivering Service" ; default3:doi "10.1142/S1793351X1640016X" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Int. J. Semantic Computing" . a , default3:Article, default1:Paper ; default4:abstract "In this paper a knowledge base concept driven named entity recognition (NER) approach is presented. The technique is used for information extraction from news articles and linking it with background concepts in knowledge base. The work specifically focuses on extracting entity mentions from unstructured articles. The extraction of entity mentions from articles is based on the existing concepts from DBPedia ontology, representing the knowledge associated with the concepts present in Wikipedia knowledge base. A collection of the Wikipedia concepts through structured DBpedia ontology has been extracted and developed. For processing of unstructured text, Dawn news articles have been scrapped, preprocessed and thereby a corpus has been built. The proposed knowledge base driven system shows that given an article, the system identifies the entity mentions in the text article and how they can automatically be linked with the concepts to the corresponding entity mentions representing their respective pages on Wikipedia. The system is evaluated on three test collections of news articles on politics, sports and entertainment domains. The experimental results in respect of entity mentions are reported. The results are presented as precision, recall and f-measure, where the precision of extraction of relevant entity mentions identified yields the best results with a little variation in percent recall and f-measures. Additionally, facts associated with the extracted entity mentions both in form of sentences and Resource Description Framework (RDF) triples are presented so as to enhance the user’s understanding of the related facts presented in the article. Keywords—Ontology-based information extraction; semantic web; named entity recognition; entity linking" ; default4:contributor "Adeel Ahmed", "Saif ur Rahman" ; default4:date "2017" ; default4:title "DBpedia based Ontological Concepts Driven Information Extraction from Unstructured Text" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Adeel" ; default5:fullName "Adeel Ahmed" ; default5:initials "A." ; default5:lastName "Ahmed" ] [ a default5:AuthorName ; default5:firstNames "Saif ur" ; default5:fullName "Saif ur Rahman" ; default5:initials "S.u." ; default5:lastName "Rahman" ] ) ; default5:authorStr "Ahmed & Rahman" ; default5:id "7887d35cb7aabb28f148a553471f702b42359996" ; default5:refCount 18 ; default2:abstract "In this paper a knowledge base concept driven named entity recognition (NER) approach is presented. The technique is used for information extraction from news articles and linking it with background concepts in knowledge base. The work specifically focuses on extracting entity mentions from unstructured articles. The extraction of entity mentions from articles is based on the existing concepts from DBPedia ontology, representing the knowledge associated with the concepts present in Wikipedia knowledge base. A collection of the Wikipedia concepts through structured DBpedia ontology has been extracted and developed. For processing of unstructured text, Dawn news articles have been scrapped, preprocessed and thereby a corpus has been built. The proposed knowledge base driven system shows that given an article, the system identifies the entity mentions in the text article and how they can automatically be linked with the concepts to the corresponding entity mentions representing their respective pages on Wikipedia. The system is evaluated on three test collections of news articles on politics, sports and entertainment domains. The experimental results in respect of entity mentions are reported. The results are presented as precision, recall and f-measure, where the precision of extraction of relevant entity mentions identified yields the best results with a little variation in percent recall and f-measures. Additionally, facts associated with the extracted entity mentions both in form of sentences and Resource Description Framework (RDF) triples are presented so as to enhance the user’s understanding of the related facts presented in the article." ; default2:contributor , ; default2:date "2017" ; default2:isReferencedBy ; default2:references , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:title "DBpedia based Ontological Concepts Driven Information Extraction from Unstructured Text" ; default3:doi "10.14569/IJACSA.2017.080954" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "__MISSING__" . a , default3:Article, default1:Paper ; default4:abstract "News agencies are one of the main actors in the news business. These agencies produce daily big amounts of digital contents, which need to be managed and distributed to their customers. The NEWS (News Engine Web Services) project, already completed, had as goal to develop tools that, using state of the art technologies, may help news agencies to improve their production and distribution processes. As a central piece in the NEWS system, an ontology for the news domain was implemented. In this paper, we describe the NEWS ontology, its intended uses in the context of the production and distribution processes of a news agency, and the NEWSPlugin, a Protégé extension developed to partially automate its" ; default4:contributor "Damaris Fuentes", "Jesús A. Fisteus", "Luis Sánchez", "Norberto Fernández" ; default4:date "2010" ; default4:title "The NEWS ontology: Design and applications" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Norberto" ; default5:fullName "Norberto Fernández" ; default5:initials "N." ; default5:lastName "Fernández" ] [ a default5:AuthorName ; default5:firstNames "Damaris" ; default5:fullName "Damaris Fuentes" ; default5:initials "D." ; default5:lastName "Fuentes" ] [ a default5:AuthorName ; default5:firstNames "Luis" ; default5:fullName "Luis Sánchez" ; default5:initials "L." ; default5:lastName "Sánchez" ] [ a default5:AuthorName ; default5:firstNames "Jesús A." ; default5:fullName "Jesús A. Fisteus" ; default5:initials "J.A." ; default5:lastName "Fisteus" ] ) ; default5:authorStr "Fernández et al." ; default5:id "dd740ff67a643139153c8d8dc3cb84917e0a2a9c" ; default5:refCount 21 ; default2:abstract "News agencies are one of the main actors in the news business. These agencies produce daily big amounts of digital contents, which need to be managed and distributed to their customers. The NEWS (News Engine Web Services) project, already completed, had as goal to develop tools that, using state of the art technologies, may help news agencies to improve their production and distribution processes. As a central piece in the NEWS system, an ontology for the news domain was implemented. In this paper, we describe the NEWS ontology, its intended uses in the context of the production and distribution processes of a news agency, and the NEWSPlugin, a Protege extension developed to partially automate its maintenance." ; default2:contributor , , , ; default2:date "2010" ; default2:isReferencedBy , , , , , , , , ; default2:subject , ; default2:title "The NEWS ontology: Design and applications" ; default3:doi "10.1016/j.eswa.2010.06.055" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Expert Syst. Appl." . a , default3:Article, default1:Paper ; default4:abstract "Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities or goals. Challenging issues in their research agenda include the sparsity of user preference data, and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. We propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities, and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources." ; default4:contributor "Alejandro Bellogín", "Iván Cantador", "Pablo Castells" ; default4:date "2011" ; default4:title "An Enhanced Semantic Layer for Hybrid Recommender Systems: Application to News Recommendation" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Iván" ; default5:fullName "Iván Cantador" ; default5:initials "I." ; default5:lastName "Cantador" ] [ a default5:AuthorName ; default5:firstNames "Pablo" ; default5:fullName "Pablo Castells" ; default5:initials "P." ; default5:lastName "Castells" ] [ a default5:AuthorName ; default5:firstNames "Alejandro" ; default5:fullName "Alejandro Bellogín" ; default5:initials "A." ; default5:lastName "Bellogín" ] ) ; default5:authorStr "Cantador et al." ; default5:id "a599d00549e2207635a5e8963ad442df2ba688c3" ; default5:refCount 35 ; default2:abstract "Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources." ; default2:contributor , , ; default2:date "2011" ; default2:isReferencedBy , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , ; default2:title "An Enhanced Semantic Layer for Hybrid Recommender Systems: Application to News Recommendation" ; default3:doi "10.4018/jswis.2011010103" ; default1:citationVelocity 0 ; default1:influentialCitationCount 3 ; default1:url ; default1:venue "Int. J. Semantic Web Inf. Syst." . a , default3:Article, default1:Paper ; default4:abstract "Six years after Tim Berners-Lee and his colleagues drew the vision of the Semantic Web (SW) in 1998, the SW is very likely to take off in the near future based on a set of specifications such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL) 1. As a natural result, people will see the coexistence of the WWW and the SW for a certain long time. Under this situation, how to bridge the gap between the WWW and the SW will become an inevitable issue. In this paper, we present a contextaware approach on enabling agents to understand semantic resources on the two webs. The basic idea of this approach is to structure user-centred contextual information to facilitate agent-based (inter)operations on the network. Based on this idea, we design a Knowledge Interoperation Reference Model (KIRM) to address the interoperation issue at the global level. To demonstrate how agents understand semantic resources in a context-aware manner in real practices, we develop a news aggregation system based on RDF Site Summary / Really Simple Syndication (RSS) using agents. Considering the fact that RSS format set is a combination of XML and RDF, the success of agent understanding of RSS content under specific semantic contexts shows the possibility of extending and applying the context-aware approach in other semantic-based applications on both the WWW and the SW in the future." ; default4:contributor "David Webster", "Weihong Huang" ; default4:date "2004" ; default4:title "Enabling Context-Aware Agents to Understand Semantic Resources on The WWW and The Semantic Web" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Weihong" ; default5:fullName "Weihong Huang" ; default5:initials "W." ; default5:lastName "Huang" ] [ a default5:AuthorName ; default5:firstNames "David" ; default5:fullName "David Webster" ; default5:initials "D." ; default5:lastName "Webster" ] ) ; default5:authorStr "Huang & Webster" ; default5:id "9fc16dcdd7898ecc8266646d534f9807c73d89d0" ; default5:refCount 23 ; default2:abstract "Six years after Tim Berners-Lee and his colleagues drew the vision of the Semantic Web (SW) in 1998, the SW is very likely to take off in the near future based on a set of specifications such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL). As a natural result, people will see the coexistence of the WWW and the SW for a certain long time. Under this situation, how to bridge the gap between the WWW and the SW will become an inevitable issue. In this paper, we present a context-aware approach on enabling agents to understand semantic resources on the two webs. The basic idea of this approach is to structure user-centred contextual information to facilitate agent-based (inter)operations on the network. Based on this idea, we design a Knowledge Interoperation Reference Model (KIRM) to address the interoperation issue at the global level. To demonstrate how agents understand semantic resources in a context-aware manner in real practices, we develop a news aggregation system based on RDF Site Summary / Really Simple Syndication (RSS) using agents. Considering the fact that RSS format set is a combination of XML and RDF, the success of agent understanding of RSS content under specific semantic contexts shows the possibility of extending and applying the context-aware approach in other semantic-based applications on both the WWW and the SW in the future." ; default2:contributor , ; default2:date "2004" ; default2:isReferencedBy , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , ; default2:title "Enabling Context-Aware Agents to Understand Semantic Resources on The WWWand The Semantic Web" ; default3:doi "10.1109/WI.2004.64" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "IEEE/WIC/ACM International Conference on Web Intelligence (WI'04)" . a , default3:Article, default1:Paper ; default4:abstract "Interest in web-based syndication systems has been growing as information streams onto the web at an increasing rate. Technologies, like the standard Semantic Web languages RDF and OWL, make it possible to create expressive representations of the content of publications and subscriptions in a syndication framework. Because these languages are based in description logics, this representation allows the application to reasoning to make more precise matching of user interests with published information. A challenge to this approach is that the consistency of the underlying knowledge base must be maintained for these techniques to work. With the frequent addition of information from new publications, it is likely that inconsistencies will arise. There are many potential mechanisms for choosing which inconsistent information to discard from the KB to regain consistency; in the case of news syndication, we argue keeping the most trusted information is important for generating the most valuable matches. Thus, in this article, we present algorithms for belief-base revision, and specifically look at the user’s trust in the information sources as a metric for deciding what to keep in the KB and what to remove." ; default4:contributor "Christian Halaschek-Wiener", "Jennifer Golbeck" ; default4:date "2007" ; default4:title "Trust-Based Revision for Expressive Web Syndication" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Jennifer" ; default5:fullName "Jennifer Golbeck" ; default5:initials "J." ; default5:lastName "Golbeck" ] [ a default5:AuthorName ; default5:firstNames "Christian" ; default5:fullName "Christian Halaschek-Wiener" ; default5:initials "C." ; default5:lastName "Halaschek-Wiener" ] ) ; default5:authorStr "Golbeck & Halaschek-Wiener" ; default5:id "ac90874bd4d9a24457b96215707b023a725b60fe" ; default5:refCount 60 ; default2:abstract "Interest in web-based syndication systems has been growing as information streams onto the web at an increasing rate. Technologies, like the standard Semantic Web languages RDF and OWL, make it possible to create expressive representations of the content of publications and subscriptions in a syndication framework. Because these languages are based in description logics, this representation allows the application to reasoning to make more precise matching of user interests with published information. A challenge to this approach is that the consistency of the underlying knowledge base must be maintained for these techniques to work. With the frequent addition of information from new publications, it is likely that inconsistencies will arise. There are many potential mechanisms for choosing which inconsistent information to discard from the KB to regain consistency; in the case of news syndication, we argue keeping the most trusted information is important for generating the most valuable matches. Thus, in this article, we present algorithms for belief-base revision, and specifically look at the user's trust in the information sources as a metric for deciding what to keep in the KB and what to remove." ; default2:contributor , ; default2:date "2009" ; default2:isReferencedBy , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , , , , ; default2:title "Trust-based Revision for Expressive Web Syndication" ; default3:doi "10.1093/logcom/exn045" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "J. Log. Comput." . a , default3:Article, default1:Paper ; default4:abstract "The rapid development of the social Web has resulted in diverse Web 2.0 applications for accessing various Web feeds such as Weblogs, news headlines, business products, real time information and Podcasts. Due to the masses of Web feeds available on the Internet, a major challenge is how to access them efficiently. Conventional methods of manually finding and matching keyword for Web feeds are time-consuming and inaccurate. This study addresses this issue by defining a Personalized Web Feeds Framework (PWFF) for integrating ontology technologies into Web feeds and user profiles. The proposed PWFF is used to develop an Ontology-based Personalized Web Feed Platform (OPWFP) that provides customized Web feeds for personnel needs. To demonstrate the feasibility of OPWFP, the experimental results illustrate the efficiency and effectiveness of the proposed approach." ; default4:contributor "I-Ching Hsu" ; default4:date "2013" ; default4:title "Personalized web feeds based on ontology technologies" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "I-Ching" ; default5:fullName "I-Ching Hsu" ; default5:initials "I." ; default5:lastName "Hsu" ] ) ; default5:authorStr "Hsu" ; default5:id "b03649f13d7f12c83c8f3dcb7f3405bbe038965e" ; default5:refCount 19 ; default2:abstract "The rapid development of the social Web has resulted in diverse Web 2.0 applications for accessing various Web feeds such as Weblogs, news headlines, business products, real time information and Podcasts. Due to the masses of Web feeds available on the Internet, a major challenge is how to access them efficiently. Conventional methods of manually finding and matching keyword for Web feeds are time-consuming and inaccurate. This study addresses this issue by defining a Personalized Web Feeds Framework (PWFF) for integrating ontology technologies into Web feeds and user profiles. The proposed PWFF is used to develop an Ontology-based Personalized Web Feed Platform (OPWFP) that provides customized Web feeds for personnel needs. To demonstrate the feasibility of OPWFP, the experimental results illustrate the efficiency and effectiveness of the proposed approach." ; default2:contributor ; default2:date "2013" ; default2:isReferencedBy , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , ; default2:title "Personalized web feeds based on ontology technologies" ; default3:doi "10.1007/s10796-011-9337-6" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "Information Systems Frontiers" . a , default3:Article, default1:Paper ; default4:abstract "This article addresses business news analysis by using artificial intelligence. An interdisciplinary method combining financial textual data mining and ontology-based reasoning is proposed and evaluated. The experiments are performed using a well-known Cyc ontology and textual material from the financial domain. Our experiments show that using semantic technologies for business news analysis helps to provide the user with more relevant answers to his=her queries." ; default4:contributor "Inna Novalija" ; default4:date "2013" ; default4:title "APPLYING SEMANTIC TECHNOLOGY TO BUSINESS NEWS ANALYSIS" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Inna" ; default5:fullName "Inna Novalija" ; default5:initials "I." ; default5:lastName "Novalija" ] ) ; default5:authorStr "Novalija" ; default5:id "57236435573f181a3d5ea734c7c77d4d8974e963" ; default5:refCount 26 ; default2:abstract "This article addresses business news analysis by using artificial intelligence. An interdisciplinary method combining financial textual data mining and ontology-based reasoning is proposed and evaluated. The experiments are performed using a well-known Cyc ontology and textual material from the financial domain. Our experiments show that using semantic technologies for business news analysis helps to provide the user with more relevant answers to his/her queries." ; default2:contributor , ; default2:date "2013" ; default2:isReferencedBy , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , , , ; default2:title "Applying Semantic Technology to Business News Analysis" ; default3:doi "10.1080/08839514.2013.805600" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Applied Artificial Intelligence" . a , default3:Article, default1:Paper ; default4:abstract "This paper addresses the problem of fake news detection. There are many works already in this space; however, most of them are for social media and not using news content for the decision making. In this paper, we propose some novel approaches, including the B-TransE model, to detecting fake news based on news content using knowledge graphs. In our solutions, we need to address a few technical challenges. Firstly, computational-oriented fact checking is not comprehensive enough to cover all the relations needed for fake news detection. Secondly, it is challenging to validate the correctness of the extracted triples from news articles. Our approaches are evaluated with the Kaggle’s ‘Getting Real about Fake News’ dataset and some true articles from main stream media. The evaluations show that some of our approaches have over 0.80 F1-scores." ; default4:contributor "Chenxi Li", "Jeff Z. Pan1(B", "Jinshuo Liu2(B", "Ningxi Li", "Siyana Pavlova", "Yangmei Li" ; default4:date "2018" ; default4:title "Content Based Fake News Detection Using Knowledge Graphs" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Jeff Z." ; default5:fullName "Jeff Z. Pan1(B" ; default5:initials "J.Z." ; default5:lastName "Pan1(B" ] [ a default5:AuthorName ; default5:firstNames "Siyana" ; default5:fullName "Siyana Pavlova" ; default5:initials "S." ; default5:lastName "Pavlova" ] [ a default5:AuthorName ; default5:firstNames "Chenxi" ; default5:fullName "Chenxi Li" ; default5:initials "C." ; default5:lastName "Li" ] [ a default5:AuthorName ; default5:firstNames "Ningxi" ; default5:fullName "Ningxi Li" ; default5:initials "N." ; default5:lastName "Li" ] [ a default5:AuthorName ; default5:firstNames "Yangmei" ; default5:fullName "Yangmei Li" ; default5:initials "Y." ; default5:lastName "Li" ] [ a default5:AuthorName ; default5:firstNames "Jinshuo" ; default5:fullName "Jinshuo Liu2(B" ; default5:initials "J." ; default5:lastName "Liu2(B" ] ) ; default5:authorStr "Pan1(B et al." ; default5:id "3323de6686a62b2b21870ca087cc853a069e39a3" ; default5:refCount 17 ; default2:abstract "This paper addresses the problem of fake news detection. There are many works already in this space; however, most of them are for social media and not using news content for the decision making. In this paper, we propose some novel approaches, including the B-TransE model, to detecting fake news based on news content using knowledge graphs. In our solutions, we need to address a few technical challenges. Firstly, computational-oriented fact checking is not comprehensive enough to cover all the relations needed for fake news detection. Secondly, it is challenging to validate the correctness of the extracted triples from news articles. Our approaches are evaluated with the Kaggle’s ‘Getting Real about Fake News’ dataset and some true articles from main stream media. The evaluations show that some of our approaches have over 0.80 F1-scores." ; default2:contributor , , , , , ; default2:date "2018" ; default2:isReferencedBy , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , ; default2:title "Content Based Fake News Detection Using Knowledge Graphs" ; default3:doi "10.1007/978-3-030-00671-6_39" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "International Semantic Web Conference" . a , default3:Article, default1:Paper ; default4:abstract "People on the Web talk about television. TV users’ social activities implicitly connect the concepts referred to by videos, news, comments, and posts. The strength of such connections may change as the perception of users on the Web changes over time. With the goal of leveraging users’ social activities to better understand how TV programs are perceived by the TV public and how the users’ interests evolve in time, we introduce a knowledge graph to model the integration of the heterogeneous and dynamic data coming from different information sources, including broadcasters’ archives, online newspapers, blogs, web encyclopedias, social media platforms, and social networks, which play a role in what we call the “extended life” of TV content. We show how our graph model captures multiple aspects of the television domain, from the semantic characterization of the TV content, to the temporal evolution of its social characterization and of its social perception. Through a real use-case analysis, based on the instance of our knowledge graph extracted from (the analysis of) a set of episodes of an Italian TV talk show, we discuss the involvement of the public of the considered program." ; default4:contributor "Alessio Antonini", "Claudio Schifanella", "Luca Vignaroli", "Maria Luisa Sapino", "Raffaele Teraoni Prioletti", "Ruggero G. Pensa" ; default4:date "2013" ; default4:title "Tracking and analyzing TV content on the web through social and ontological knowledge" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Ruggero G." ; default5:fullName "Ruggero G. Pensa" ; default5:initials "R.G." ; default5:lastName "Pensa" ] [ a default5:AuthorName ; default5:firstNames "Maria Luisa" ; default5:fullName "Maria Luisa Sapino" ; default5:initials "M.L." ; default5:lastName "Sapino" ] [ a default5:AuthorName ; default5:firstNames "Claudio" ; default5:fullName "Claudio Schifanella" ; default5:initials "C." ; default5:lastName "Schifanella" ] [ a default5:AuthorName ; default5:firstNames "Alessio" ; default5:fullName "Alessio Antonini" ; default5:initials "A." ; default5:lastName "Antonini" ] [ a default5:AuthorName ; default5:firstNames "Raffaele Teraoni" ; default5:fullName "Raffaele Teraoni Prioletti" ; default5:initials "R.T." ; default5:lastName "Prioletti" ] [ a default5:AuthorName ; default5:firstNames "Luca" ; default5:fullName "Luca Vignaroli" ; default5:initials "L." ; default5:lastName "Vignaroli" ] ) ; default5:authorStr "Pensa et al." ; default5:id "fa9b6d402c0c67b590d993bfac4fa74cfaa2e910" ; default5:refCount 26 ; default2:abstract "People on the Web talk about television. TV users' social activities implicitly connect the concepts referred to by videos, news, comments, and posts. The strength of such connections may change as the perception of users on the Web changes over time. With the goal of leveraging users' social activities to better understand how TV programs are perceived by the TV public and how the users' interests evolve in time, we introduce a knowledge graph to model the integration of the heterogeneous and dynamic data coming from different information sources, including broadcasters' archives, online newspapers, blogs, web encyclopedias, social media platforms, and social networks, which play a role in what we call the \"extended life\" of TV content. We show how our graph model captures multiple aspects of the television domain, from the semantic characterization of the TV content, to the temporal evolution of its social characterization and of its social perception. Through a real use-case analysis, based on the instance of our knowledge graph extracted from (the analysis of) a set of episodes of an Italian TV talk show, we discuss the involvement of the public of the considered program." ; default2:contributor , , , , , ; default2:date "2013" ; default2:isReferencedBy , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , ; default2:title "Tracking and analyzing TV content on the web through social and ontological knowledge" ; default3:doi "10.1145/2465958.2465978" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "EuroITV" . a , default3:Article, default1:Paper ; default4:abstract "Recently, the Korean popular (K-Pop) music industry has grown into a popular subculture among teenagers and young adults worldwide, which resulted in widespread interest in the fashion and style of idolised Korean singers and groups. Although English social media websites provide some content related to K-Pop, these websites lack diversity and rapid updating of information compared to local Korean websites. This study introduces a K-Pop knowledge graph, which is the basis for describing various objects and their relationships. All contents of the knowledge graph can be distributed and shared across various applications. To do so, this study proposes a semantic data model to represent a comprehensive profile for singers and groups, their activities, organisations and entertainment content. The knowledge graph is created by aggregating a set of relevant datasets from various data sources. In addition, Gnosis, which is a news application, demonstrates how this knowledge graph can be used in a" ; default4:contributor "Haklae Kim" ; default4:date "2017" ; default4:title "Building a K-Pop knowledge graph using an entertainment ontology" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Haklae" ; default5:fullName "Haklae Kim" ; default5:initials "H." ; default5:lastName "Kim" ] ) ; default5:authorStr "Kim" ; default5:id "373304d08f844c279d26a1c215a11e13c1507645" ; default5:refCount 27 ; default2:abstract "Recently, the Korean popular (K-Pop) music industry has grown into a popular subculture among teenagers and young adults worldwide, which resulted in widespread interest in the fashion and style of idolised Korean singers and groups. Although English social media websites provide some content related to K-Pop, these websites lack diversity and rapid updating of information compared to local Korean websites. This study introduces a K-Pop knowledge graph, which is the basis for describing various objects and their relationships. All contents of the knowledge graph can be distributed and shared across various applications. To do so, this study proposes a semantic data model to represent a comprehensive profile for singers and groups, their activities, organisations and entertainment content. The knowledge graph is created by aggregating a set of relevant datasets from various data sources. In addition, Gnosis, which is a news application, demonstrates how this knowledge graph can be used in a real-world service." ; default2:contributor ; default2:date "2017" ; default2:subject , , , ; default2:title "Building a K-Pop knowledge graph using an entertainment ontology" ; default3:doi "10.1057/s41275-017-0056-8" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "__MISSING__" . a , default3:Article, default1:Paper ; default4:abstract "News production is characterized by a complex and dynamic workflow, in which it is important to produce and broadcast reliable news as fast as possible. In this process, the efficient retrieval of previously broadcasted news items is important, both for gathering background information and for reuse of footage in new reports. This paper discusses how the quality of descriptive metadata of news items can be optimized, by collecting data generated during news production. Starting from a description of the news production process of the Flemish public service broadcaster in Belgium (VRT), information systems containing valuable metadata are identified. Subsequently, we present a data model that uniformly represents the available information generated during news production. This data model is then implemented using Semantic Web technologies. Further, we describe how other valuable data sets, present in the Semantic Web, are connected to the data model, enabling semantic search operations." ; default4:contributor "Davy Van Deursen", "Dieter Van Rijsselbergen", "Erik Mannens", "Mike Matton", "Pedro Debevere", "Rik Van de Walle", "Robbie De Sutter" ; default4:date "2011" ; default4:title "Enabling Semantic Search in a News Production Environment" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Pedro" ; default5:fullName "Pedro Debevere" ; default5:initials "P." ; default5:lastName "Debevere" ] [ a default5:AuthorName ; default5:firstNames "Davy Van" ; default5:fullName "Davy Van Deursen" ; default5:initials "D.V." ; default5:lastName "Deursen" ] [ a default5:AuthorName ; default5:firstNames "Dieter Van" ; default5:fullName "Dieter Van Rijsselbergen" ; default5:initials "D.V." ; default5:lastName "Rijsselbergen" ] [ a default5:AuthorName ; default5:firstNames "Erik" ; default5:fullName "Erik Mannens" ; default5:initials "E." ; default5:lastName "Mannens" ] [ a default5:AuthorName ; default5:firstNames "Mike" ; default5:fullName "Mike Matton" ; default5:initials "M." ; default5:lastName "Matton" ] [ a default5:AuthorName ; default5:firstNames "Robbie De" ; default5:fullName "Robbie De Sutter" ; default5:initials "R.D." ; default5:lastName "Sutter" ] [ a default5:AuthorName ; default5:firstNames "Rik Van de" ; default5:fullName "Rik Van de Walle" ; default5:initials "R.V.d." ; default5:lastName "Walle" ] ) ; default5:authorStr "Debevere et al." ; default5:id "ac9f6a366150c62d0ec48233bf06b4b12c3b03ce" ; default5:refCount 14 ; default2:abstract "News production is characterized by a complex and dynamic workflow, in which it is important to produce and broadcast reliable news as fast as possible. In this process, the efficient retrieval of previously broadcasted news items is important, both for gathering background information and for reuse of footage in new reports. This paper discusses how the quality of descriptive metadata of news items can be optimized, by collecting data generated during news production. Starting from a description of the news production process of the Flemish public service broadcaster in Belgium (VRT), information systems containing valuable metadata are identified. Subsequently, we present a data model that uniformly represents the available information generated during news production. This data model is then implemented using Semantic Web technologies. Further, we describe how other valuable data sets, present in the SemanticWeb, are connected to the data model, enabling semantic search operations." ; default2:contributor , , , , , , ; default2:date "2010" ; default2:isReferencedBy , , , , ; default2:references , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , , , , , , , , , , ; default2:title "Enabling Semantic Search in a News Production Environment" ; default3:doi "10.1007/978-3-642-23017-2_3" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "SAMT" . a , default3:Article, default1:Paper ; default4:abstract "Summarizing news articles is becoming crucial for allowing quick and concise access to information about daily events. This task can be challenging when the same event is reported with various levels of detail or is subject to diverse view points. A well established technique in the area of news summarization consists in modeling events as a set of semantic triples. These triples are weighted, mainly based on their frequencies, and then fused to build summaries. Typically, triples are extracted from main clauses, which might lead to information loss. Moreover, some crucial facets of news, such as reasons or consequences, are mostly reported in subordinate clauses and thus they are not properly handled. In this paper, we focus on an existing work that uses a graph structure to model sentences allowing the access to any triple independently from the clause it belongs to. Summary sentences are then generated by taking the top ranked paths that contain many triples and show grammatical correctness. We further provide several improvements to that approach. First, we leverage node degrees for finding the most important triples and facets shared among sentences. Second, we enhance the process of triple fusion by providing more effective similarity measures that exploit entity linking and predicate similarity. We performed extensive experiments using the DUC’04 and DUC’07 datasets showing that our approach outperforms baseline approaches by a large margin in terms of ROUGE and PYRAMID scores." ; default4:contributor "Mouna Kacimi", "Radityo Eko Prasojo(B", "Werner Nutt" ; default4:date "2018" ; default4:title "Modeling and Summarizing News Events Using Semantic Triples" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Radityo Eko" ; default5:fullName "Radityo Eko Prasojo(B" ; default5:initials "R.E." ; default5:lastName "Prasojo(B" ] [ a default5:AuthorName ; default5:firstNames "Mouna" ; default5:fullName "Mouna Kacimi" ; default5:initials "M." ; default5:lastName "Kacimi" ] [ a default5:AuthorName ; default5:firstNames "Werner" ; default5:fullName "Werner Nutt" ; default5:initials "W." ; default5:lastName "Nutt" ] ) ; default5:authorStr "Prasojo(B et al." ; default5:id "942f3703813e44ebe304805d0c19ed098eb978da" ; default5:refCount 27 ; default2:abstract "Summarizing news articles is becoming crucial for allowing quick and concise access to information about daily events. This task can be challenging when the same event is reported with various levels of detail or is subject to diverse view points. A well established technique in the area of news summarization consists in modeling events as a set of semantic triples. These triples are weighted, mainly based on their frequencies, and then fused to build summaries. Typically, triples are extracted from main clauses, which might lead to information loss. Moreover, some crucial facets of news, such as reasons or consequences, are mostly reported in subordinate clauses and thus they are not properly handled. In this paper, we focus on an existing work that uses a graph structure to model sentences allowing the access to any triple independently from the clause it belongs to. Summary sentences are then generated by taking the top ranked paths that contain many triples and show grammatical correctness. We further provide several improvements to that approach. First, we leverage node degrees for finding the most important triples and facets shared among sentences. Second, we enhance the process of triple fusion by providing more effective similarity measures that exploit entity linking and predicate similarity. We performed extensive experiments using the DUC’04 and DUC’07 datasets showing that our approach outperforms baseline approaches by a large margin in terms of ROUGE and PYRAMID scores." ; default2:contributor , , ; default2:date "2018" ; default2:isReferencedBy , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , ; default2:title "Modeling and Summarizing News Events Using Semantic Triples" ; default3:doi "10.1007/978-3-319-93417-4_33" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "ESWC" . a , default3:Article, default1:Paper ; default4:abstract "With the rapid growth of online information services, a sheer volume of news data becomes available. To help people quickly digest the explosive information, we define a new problem – schema-based news event profiling – profiling events reported in open-domain news corpora, with a set of slots and slot-value pairs for each event, where the set of slots forms the schema of an event type. Such profiling not only provides readers with concise views of events, but also facilitates various applications such as information retrieval, knowledge graph construction and question answering. It is however a quite challenging task. The first challenge is to find out events and event types because they are both initially unknown. The second difficulty is the lack of pre-defined event-type schemas. Lastly, even with the schemas extracted, to generate event profiles from them is still essential yet demanding. To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles. First, we develop a Bayesian non-parametricmodel to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles. Second, we propose an unsupervised embedding model for schema induction that encodes the insight: an entity may serve as the values of multiple slots in an event, but if it appears in more sentences along with the same set of more entities in the event, its slots in these sentences tend to be similar. Finally, we build event profiles by extracting slot values for each event based on the slots’ expression patterns. To the best of our knowledge, this is the first work on schema-based profiling for news events. Experimental results on a large news corpus demonstrate the superior performance of our method against the state-of-the-art baselines on event detection, schema induction and event profiling. *This work was done when the first 3 authors were at University of Illinois at UrbanaChampaign. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. CIKM ’18, October 22–26, 2018, Torino, Italy © 2018 Association for Computing Machinery. ACM ISBN 978-1-4503-6014-2/18/10. https://doi.org/10.1145/3269206.3271674 ACM Reference Format: Quan Yuan, Xiang Ren, Wenqi He, Chao Zhang, Xinhe Geng, Lifu Huang, Heng Ji, Chin-Yew Lin, Jiawei Han. 2018. Open-Schema Event Profiling for Massive News Corpora. In The 27th ACM International Conference on Information and Knowledge Management (CIKM ’18), October 22–26, 2018, Torino, Italy.ACM,NewYork, NY, USA, 10 pages. https://doi.org/10.1145/3269206.3271674" ; default4:contributor "Chao Zhang", "Chin-Yew Lin", "Heng Ji", "Jiawei Han", "Lifu Huang", "Quan Yuan", "Wenqi He", "Xiang Ren", "Xinhe Geng" ; default4:date "2018" ; default4:title "Open-Schema Event Profiling for Massive News Corpora" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Quan" ; default5:fullName "Quan Yuan" ; default5:initials "Q." ; default5:lastName "Yuan" ] [ a default5:AuthorName ; default5:firstNames "Xiang" ; default5:fullName "Xiang Ren" ; default5:initials "X." ; default5:lastName "Ren" ] [ a default5:AuthorName ; default5:firstNames "Wenqi" ; default5:fullName "Wenqi He" ; default5:initials "W." ; default5:lastName "He" ] [ a default5:AuthorName ; default5:firstNames "Chao" ; default5:fullName "Chao Zhang" ; default5:initials "C." ; default5:lastName "Zhang" ] [ a default5:AuthorName ; default5:firstNames "Xinhe" ; default5:fullName "Xinhe Geng" ; default5:initials "X." ; default5:lastName "Geng" ] [ a default5:AuthorName ; default5:firstNames "Lifu" ; default5:fullName "Lifu Huang" ; default5:initials "L." ; default5:lastName "Huang" ] [ a default5:AuthorName ; default5:firstNames "Heng" ; default5:fullName "Heng Ji" ; default5:initials "H." ; default5:lastName "Ji" ] [ a default5:AuthorName ; default5:firstNames "Chin-Yew" ; default5:fullName "Chin-Yew Lin" ; default5:initials "C." ; default5:lastName "Lin" ] [ a default5:AuthorName ; default5:firstNames "Jiawei" ; default5:fullName "Jiawei Han" ; default5:initials "J." ; default5:lastName "Han" ] ) ; default5:authorStr "Yuan et al." ; default5:id "be0e834669b4351ed2ee843d5eeb03b084b88968" ; default5:refCount 67 ; default2:abstract """With the rapid growth of online information services, a sheer volume of news data becomes available. To help people quickly digest the explosive information, we define a new problem - schema-based news event profiling - profiling events reported in open-domain news corpora, with a set of slots and slot-value pairs for each event, where the set of slots forms the schema of an event type. Such profiling not only provides readers with concise views of events, but also facilitates various applications such as information retrieval, knowledge graph construction and question answering. It is however a quite challenging task. The first challenge is to find out events and event types because they are both initially unknown. The second difficulty is the lack of pre-defined event-type schemas. Lastly, even with the schemas extracted, to generate event profiles from them is still essential yet demanding. To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles. First, we develop a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles. Second, we propose an unsupervised embedding model for schema induction that encodes the insight: an entity may serve as the values of multiple slots in an event, but if it appears in more sentences along with the same set of more entities in the event, its slots in these sentences tend to be similar. Finally, we build event profiles by extracting slot values for each event based on the slots' expression patterns. To the best of our knowledge, this is the first work on schema-based profiling for news events. Experimental results on a large news corpus demonstrate the superior performance of our method against the state-of-the-art baselines on event detection, schema induction and event profiling.""" ; default2:contributor , , , , , , , , ; default2:date "2018" ; default2:isReferencedBy , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , , , , , ; default2:title "Open-Schema Event Profiling for Massive News Corpora" ; default3:doi "10.1145/3269206.3271674" ; default1:citationVelocity 0 ; default1:influentialCitationCount 2 ; default1:url ; default1:venue "CIKM '18" . a , default3:Article, default1:Paper ; default4:abstract "In media monitoring users have a clearly defined information need to find so far unknown statements regarding certain entities or relations mentioned in natural-language text. However, commonly used keyword-based search technologies are focused on finding relevant documents and cannot judge the novelty of statements contained in the text. In this work, we propose a new semantic novelty measure that allows to retrieve statements, which are both novel and relevant, from naturallanguage sentences in news articles. Relevance is defined by a semantic query of the user, while novelty is ensured by checking whether the extracted statements are related, but non-existing in a knowledge base containing the currently known facts. Our evaluation performed on English news texts and on CrunchBase as the knowledge base demonstrates the effectiveness, unique capabilities and future challenges of this novel approach to novelty." ; default4:contributor "Achim Rettinger", "Andreas Harth", "Michael Färber" ; default4:date "2016" ; default4:title "Towards Monitoring of Novel Statements in the News" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Michael" ; default5:fullName "Michael Färber" ; default5:initials "M." ; default5:lastName "Färber" ] [ a default5:AuthorName ; default5:firstNames "Achim" ; default5:fullName "Achim Rettinger" ; default5:initials "A." ; default5:lastName "Rettinger" ] [ a default5:AuthorName ; default5:firstNames "Andreas" ; default5:fullName "Andreas Harth" ; default5:initials "A." ; default5:lastName "Harth" ] ) ; default5:authorStr "Färber et al." ; default5:id "4141fc2cc02b44f30ac3c3d6ef6677430d3d86da" ; default5:refCount 19 ; default2:abstract "In media monitoring users have a clearly defined information need to find so far unknown statements regarding certain entities or relations mentioned in natural-language text. However, commonly used keyword-based search technologies are focused on finding relevant documents and cannot judge the novelty of statements contained in the text. In this work, we propose a new semantic novelty measure that allows to retrieve statements, which are both novel and relevant, from natural-language sentences in news articles. Relevance is defined by a semantic query of the user, while novelty is ensured by checking whether the extracted statements are related, but non-existing in a knowledge base containing the currently known facts. Our evaluation performed on English news texts and on CrunchBase as the knowledge base demonstrates the effectiveness, unique capabilities and future challenges of this novel approach to novelty." ; default2:contributor , , ; default2:date "2016" ; default2:isReferencedBy , ; default2:references , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , ; default2:title "Towards Monitoring of Novel Statements in the News" ; default3:doi "10.1007/978-3-319-34129-3_18" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "ESWC" . a , default3:Article, default1:Paper ; default4:abstract "The gradual migration of television from broadcast diffusion to Internet diffusion offers countless possibilities for the generation of rich navigable contents. However, it also raises numerous scientific issues regarding delinearization of TV streams and content enrichment. In this paper, we study how speech can be used at different levels of the delinearization process, using automatic speech transcription and natural language processing (NLP) for the segmentation and characterization of TV programs and for the generation of semantic hyperlinks in videos. Transcript-based video delinearization requires natural language processing techniques robust to transcription peculiarities, such as transcription errors, and to domain and genre differences. We therefore propose to modify classical NLP techniques, initially designed for regular texts, to improve their robustness in the context of TV delinearization. We demonstrate that the modified NLP techniques can efficiently handle various types of TV material and be exploited for program description, for topic segmentation, and for the generation of semantic hyperlinks between multimedia contents. We illustrate the concept of cross-media semantic navigation with a description of our news navigation demonstrator presented during the NEM Summit 2009." ; default4:contributor "Camille Guinaudeau", "Guillaume Gravier", "Gwénolé Lecorvé", "Pascale Sébillot" ; default4:date "2011" ; default4:title "Exploiting Speech for Automatic TV Delinearization: From Streams to Cross- Media Semantic Navigation" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Pascale" ; default5:fullName "Pascale Sébillot" ; default5:initials "P." ; default5:lastName "Sébillot" ] [ a default5:AuthorName ; default5:firstNames "Guillaume" ; default5:fullName "Guillaume Gravier" ; default5:initials "G." ; default5:lastName "Gravier" ] [ a default5:AuthorName ; default5:firstNames "Camille" ; default5:fullName "Camille Guinaudeau" ; default5:initials "C." ; default5:lastName "Guinaudeau" ] [ a default5:AuthorName ; default5:firstNames "Gwénolé" ; default5:fullName "Gwénolé Lecorvé" ; default5:initials "G." ; default5:lastName "Lecorvé" ] ) ; default5:authorStr "Sébillot et al." ; default5:id "4a83ffee6ce6f839ee0ad9738d001fa10c930efa" ; default5:refCount 36 ; default2:abstract "The gradual migration of television from broadcast diffusion to Internet diffusion offers countless possibilities for the generation of rich navigable contents. However, it also raises numerous scientific issues regarding delinearization of TV streams and content enrichment. In this paper, we study how speech can be used at different levels of the delinearization process, using automatic speech transcription and natural language processing (NLP) for the segmentation and characterization of TV programs and for the generation of semantic hyperlinks in videos. Transcript-based video delinearization requires natural language processing techniques robust to transcription peculiarities, such as transcription errors, and to domain and genre differences. We therefore propose to modify classical NLP techniques, initially designed for regular texts, to improve their robustness in the context of TV delinearization. We demonstrate that the modified NLP techniques can efficiently handle various types of TV material and be exploited for program description, for topic segmentation, and for the generation of semantic hyperlinks between multimedia contents. We illustrate the concept of cross-media semantic navigation with a description of our news navigation demonstrator presented during the NEM Summit 2009." ; default2:contributor , , , ; default2:date "2011" ; default2:isReferencedBy , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , ; default2:title "Exploiting Speech for Automatic TV Delinearization: From Streams to Cross-Media Semantic Navigation" ; default3:doi "10.1155/2011/689780" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "EURASIP J. Image and Video Processing" . a , default3:Article, default1:Paper ; default4:abstract "News on the Internet today plays an important role in helping people access daily information around the world. News aggregators are websites that collect and provide content from different sources in one location for easy viewing. However, the increasing number of news on the Internet makes it difficult for readers when they desire to access news they are concerned. One solution to this issue is based on employing recommender systems. In this research, we propose a novel method for news recommendation based on a combination of semantic similarity with content similarity between news and implement it as a feature of semantic-based news aggregators BKSport. Experimental results have shown that, a combination of both kind of similarity measures will result in better recommendation than when using either measure separately." ; default4:contributor "Quang-Minh Nguyen", "Tuan-Dung Cao" ; default4:date "2016" ; default4:title "Semantic-Based Recommendation Method for Sport News Aggregation System" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Quang-Minh" ; default5:fullName "Quang-Minh Nguyen" ; default5:initials "Q." ; default5:lastName "Nguyen" ] [ a default5:AuthorName ; default5:firstNames "Tuan-Dung" ; default5:fullName "Tuan-Dung Cao" ; default5:initials "T." ; default5:lastName "Cao" ] ) ; default5:authorStr "Nguyen & Cao" ; default5:id "5db5e8f0bbde255db3e6be20db2add293a1a0f3d" ; default5:refCount 9 ; default2:abstract "News on the Internet today plays an important role in helping people access daily information around the world. News aggregators are websites that collect and provide content from different sources in one location for easy viewing. However, the increasing number of news on the Internet makes it difficult for readers when they desire to access news they are concerned. One solution to this issue is based on employing recommender systems. In this research, we propose a novel method for news recommendation based on a combination of semantic similarity with content similarity between news and implement it as a feature of semantic-based news aggregators BKSport. Experimental results have shown that, a combination of both kind of similarity measures will result in better recommendation than when using either measure separately." ; default2:contributor , , ; default2:date "2016" ; default2:isReferencedBy , , ; default2:subject , , , ; default2:title "Semantic-Based Recommendation Method for Sport News Aggregation System" ; default3:doi "10.1007/978-3-319-49944-4_3" ; default1:citationVelocity 0 ; default1:influentialCitationCount 1 ; default1:url ; default1:venue "CONFENIS" . a , default3:Article, default1:Paper ; default4:abstract "Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly." ; default4:contributor "2(B", "Adrian M. P. Braşoveanu", "Răzvan Andonie" ; default4:date "2019" ; default4:title "Semantic Fake News Detection: A Machine Learning Perspective" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Adrian M. P." ; default5:fullName "Adrian M. P. Braşoveanu" ; default5:initials "A.M.P." ; default5:lastName "Braşoveanu" ] [ a default5:AuthorName ; default5:firstNames "" ; default5:fullName "2(B" ; default5:initials "." ; default5:lastName "2(B" ] [ a default5:AuthorName ; default5:firstNames "Răzvan" ; default5:fullName "Răzvan Andonie" ; default5:initials "R." ; default5:lastName "Andonie" ] ) ; default5:authorStr "Braşoveanu et al." ; default5:id "5aba93b3771aade9e396e088f7f91ccf0934b9cd" ; default5:refCount 32 ; default2:abstract "Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly." ; default2:contributor , ; default2:date "2019" ; default2:subject ; default2:title "Semantic Fake News Detection: A Machine Learning Perspective" ; default3:doi "10.1007/978-3-030-20521-8_54" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "IWANN" . a , default3:Article, default1:Paper ; default4:abstract "0950-7051/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.knosys.2011.12.007 ⇑ Corresponding author. Tel.: +30 210 727 5232; fa E-mail addresses: l.kallipolitis@di.uoa.gr (L. Ka (V. Karpis), izambo@di.uoa.gr (I. Karali). The Semantic Web can have great influence on various domains of information. One of them is the domain of World News. Semantic Web technologies aim at providing the means to organize the vast amount of knowledge that is scattered in the Web, in a machine understandable way. Then, searching and data retrieval would be much easier. This would be particularly helpful in the World News domain. There is a big variety of news sources and it would be useful to provide an efficient method to automatically organize them. In this paper, we describe World News Finder, a system which performs semantic search on the World News domain. The system is based on metadata files created for every single World News HTML webpage in an automatic way. According to a user query, the system performs the search on these metadata files rather than keyword search. To achieve the above, we developed the World News Ontology and a large set of domain-specific heuristic rules. 2011 Elsevier B.V. All rights reserved." ; default4:contributor "Isambo Karali", "Leonidas Kallipolitis", "Vassilis Karpis" ; default4:date "2012" ; default4:title "Semantic search in the World News domain using automatically extracted metadata files" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Leonidas" ; default5:fullName "Leonidas Kallipolitis" ; default5:initials "L." ; default5:lastName "Kallipolitis" ] [ a default5:AuthorName ; default5:firstNames "Vassilis" ; default5:fullName "Vassilis Karpis" ; default5:initials "V." ; default5:lastName "Karpis" ] [ a default5:AuthorName ; default5:firstNames "Isambo" ; default5:fullName "Isambo Karali" ; default5:initials "I." ; default5:lastName "Karali" ] ) ; default5:authorStr "Kallipolitis et al." ; default5:id "ac859a57eaf5aed3cffeb11913ba3a22608bb4ba" ; default5:refCount 42 ; default2:abstract "The Semantic Web can have great influence on various domains of information. One of them is the domain of World News. Semantic Web technologies aim at providing the means to organize the vast amount of knowledge that is scattered in the Web, in a machine understandable way. Then, searching and data retrieval would be much easier. This would be particularly helpful in the World News domain. There is a big variety of news sources and it would be useful to provide an efficient method to automatically organize them. In this paper, we describe World News Finder, a system which performs semantic search on the World News domain. The system is based on metadata files created for every single World News HTML webpage in an automatic way. According to a user query, the system performs the search on these metadata files rather than keyword search. To achieve the above, we developed the World News Ontology and a large set of domain-specific heuristic rules." ; default2:contributor , , ; default2:date "2012" ; default2:isReferencedBy , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , , , ; default2:title "Semantic search in the World News domain using automatically extracted metadata files" ; default3:doi "10.1016/j.knosys.2011.12.007" ; default1:citationVelocity 0 ; default1:influentialCitationCount 0 ; default1:url ; default1:venue "Knowl.-Based Syst." . a , default3:Article, default1:Paper ; default4:abstract "In this paper, a system, RitroveRAI, addressing the general problem of enriching a multimedia news stream with semantic metadata is presented. News metadata here are explicitly derived from transcribed sentences or implicitly expressed into a topical category automatically detected. The enrichment process is accomplished by searching the same news expressed by different agencies reachable over the Web. Metadata extraction from the alternative sources (i.e. Web pages) is similarly applied and finally integration of the sources (according to some heuristic of pertinence) is carried out. Performance evaluation of the current system prototype has been carried out on a large scale. It confirms the viability of the RitroveRAI approach for realistic (i.e. 24 hours) applications and continuous monitoring and metadata extraction from multimedia news data." ; default4:contributor "Marco Cammisa", "Roberto Basili" ; default4:date "2005" ; default4:title "RitroveRAI: A Web Application for Semantic Indexing and Hyperlinking of Multimedia News*" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Roberto" ; default5:fullName "Roberto Basili" ; default5:initials "R." ; default5:lastName "Basili" ] [ a default5:AuthorName ; default5:firstNames "Marco" ; default5:fullName "Marco Cammisa" ; default5:initials "M." ; default5:lastName "Cammisa" ] ) ; default5:authorStr "Basili & Cammisa" ; default5:id "55977665107af0a4b5ad304857267038d613eb8d" ; default5:refCount 10 ; default2:abstract "In this paper, a system, RitroveRAI, addressing the general problem of enriching a multimedia news stream with semantic metadata is presented. News metadata here are explicitly derived from transcribed sentences or implicitly expressed into a topical category automatically detected. The enrichment process is accomplished by searching the same news expressed by different agencies reachable over the Web. Metadata extraction from the alternative sources (i.e. Web pages) is similarly applied and finally integration of the sources (according to some heuristic of pertinence) is carried out. Performance evaluation of the current system prototype has been carried out on a large scale. It confirms the viability of the RitroveRAI approach for realistic (i.e. 24 hours) applications and continuous monitoring and metadata extraction from multimedia news data." ; default2:contributor , , ; default2:date "2005" ; default2:isReferencedBy , , , , , , , , , , , , , , , ; default2:references , , , , , , , , ; default2:subject , , , , , , , , ; default2:title "RitroveRAI: A Web Application for Semantic Indexing and Hyperlinking of Multimedia News" ; default3:doi "10.1007/11574620_10" ; default1:citationVelocity 0 ; default1:influentialCitationCount 2 ; default1:url ; default1:venue "International Semantic Web Conference" . a , default3:Article, default1:Paper ; default4:abstract "The problem we tackle in this work is, given a present news event, to generate a plausible future event that can be caused by the given event. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precise labeled causality examples, we mine 150 years of news articles, and apply semantic natural language modeling techniques to titles containing certain predefined causality patterns. For generalization, the model uses a vast amount of world knowledge ontologies mined from LinkedData, containing 200 datasets with approximately 20 billion relations. Empirical evaluation on real news articles shows that our Pundit algorithm reaches a human-level performance." ; default4:contributor "Kira Radinsky", "Sagie Davidovich", "Shaul Markovitch" ; default4:date "2012" ; default4:title "Learning Causality for News Events Prediction" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Kira" ; default5:fullName "Kira Radinsky" ; default5:initials "K." ; default5:lastName "Radinsky" ] [ a default5:AuthorName ; default5:firstNames "Sagie" ; default5:fullName "Sagie Davidovich" ; default5:initials "S." ; default5:lastName "Davidovich" ] [ a default5:AuthorName ; default5:firstNames "Shaul" ; default5:fullName "Shaul Markovitch" ; default5:initials "S." ; default5:lastName "Markovitch" ] ) ; default5:authorStr "Radinsky et al." ; default5:id "2cd5849d22fc04723232e6da7ff1e87b9eb9a8e0" ; default5:refCount 40 ; default2:abstract "The problem we tackle in this work is, given a present news event, to generate a plausible future event that can be caused by the given event. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precise labeled causality examples, we mine 150 years of news articles, and apply semantic natural language modeling techniques to titles containing certain predefined causality patterns. For generalization, the model uses a vast amount of world knowledge ontologies mined from LinkedData, containing ~200 datasets with approximately 20 billion relations. Empirical evaluation on real news articles shows that our Pundit algorithm reaches a human-level performance." ; default2:contributor , , ; default2:date "2012" ; default2:isReferencedBy , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , ; default2:title "Learning causality for news events prediction" ; default3:doi "10.1145/2187836.2187958" ; default1:citationVelocity 14 ; default1:influentialCitationCount 10 ; default1:url ; default1:venue "WWW" . a , default3:Article, default1:Paper ; default4:abstract "The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%." ; default4:contributor "Axel-Cyrille Ngonga Ngomo", "Daniel Gerber", "Lorenz Bühmann", "Ricardo Usbeck", "Sebastian Hellmann", "Tommaso Soru" ; default4:date "2013" ; default4:title "Real-Time RDF Extraction from Unstructured Data Streams" ; default5:authorList ( [ a default5:AuthorName ; default5:firstNames "Daniel" ; default5:fullName "Daniel Gerber" ; default5:initials "D." ; default5:lastName "Gerber" ] [ a default5:AuthorName ; default5:firstNames "Sebastian" ; default5:fullName "Sebastian Hellmann" ; default5:initials "S." ; default5:lastName "Hellmann" ] [ a default5:AuthorName ; default5:firstNames "Lorenz" ; default5:fullName "Lorenz Bühmann" ; default5:initials "L." ; default5:lastName "Bühmann" ] [ a default5:AuthorName ; default5:firstNames "Tommaso" ; default5:fullName "Tommaso Soru" ; default5:initials "T." ; default5:lastName "Soru" ] [ a default5:AuthorName ; default5:firstNames "Ricardo" ; default5:fullName "Ricardo Usbeck" ; default5:initials "R." ; default5:lastName "Usbeck" ] [ a default5:AuthorName ; default5:firstNames "Axel-Cyrille Ngonga" ; default5:fullName "Axel-Cyrille Ngonga Ngomo" ; default5:initials "A.N." ; default5:lastName "Ngomo" ] ) ; default5:authorStr "Gerber et al." ; default5:id "31ced8966085e3b9658470ae84958026f7f0c1e4" ; default5:refCount 25 ; default2:abstract "The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%." ; default2:contributor , , , , , ; default2:date "2013" ; default2:isReferencedBy , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ; default2:references , , , , , , , , , , , , , , , , , , , , , ; default2:subject , , , , , , , , , , , , , ; default2:title "Real-Time RDF Extraction from Unstructured Data Streams" ; default3:doi "10.1007/978-3-642-41335-3_9" ; default1:citationVelocity 0 ; default1:influentialCitationCount 7 ; default1:url ; default1:venue "International Semantic Web Conference" . a default1:Author, foaf:Person ; default1:alias "A. J. Sun", "Ang John Sun" ; default1:influentialCitationCount 26 ; default1:url ; foaf:name "Ang Sun" . a default1:Author, foaf:Person ; default1:alias "H Lu", "H. 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