Toward Ontology Representation and Reasoning for News

  • Xubo Wen
  • Xiaoli Ma
  • Juanzi Li
  • Jeff Z. Pan
  • Jiayu Xie
Part of the Communications in Computer and Information Science book series (CCIS, volume 406)


Most research work on news mining nowadays covers phrase and topic level. A few works conducted on logical level mainly focus on personalized news service and no special efforts are put on the applications of ontology techniques on deep news mining. In this paper, we demonstrate a whole strategy for deeply understanding event-focused news taking the advantage of ontology representation and ontology reasoning. We propose an ontology-enriched news deep understanding framework ONDU which addresses the following problems: (1) how to transfer parsed news content into logical triples by using domain ontology. (2) The application of ONDU based on the reasoning results from the ontology reasoner TrOWL over the RDF data expressing the news. Through this whole strategy we can detect the inconsistence among multiple news articles and compare the different information implied in different news. We can even integrate a set of news content through merging the RDF data. The empirical experiment conducted on news from several portals shows the effectiveness and usefulness of our method.


Ontology reasoning news mining text understanding TrOWL 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xubo Wen
    • 1
    • 2
  • Xiaoli Ma
    • 2
  • Juanzi Li
    • 2
  • Jeff Z. Pan
    • 3
  • Jiayu Xie
    • 1
  1. 1.Information Engineering Institute of Technology, Naval Academy of ArmamentP.R. China
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingP.R. China
  3. 3.Department of Computer ScienceUniversity of AberdeenUK

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