Ontology-Supported Document Ranking for Novelty Search

  • Michael Färber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)


Within specific domains, users generally face the challenge to populate an ontology according to their needs. Especially in case of novelty detection and forecast, the user wants to integrate novel information contained in natural text documents into his/her own ontology in order to utilise the knowledge base in a further step. In this paper, a semantic document ranking approach is proposed which serves as a prerequisite for ontology population. By using the underlying ontology for both query generation and document ranking, query and ranking are structured and, therefore, promise to provide a better ranking in terms of relevance and novelty than without using semantics.


Document ranking Ontology-based information extraction Novelty detection Semantic similarity 


  1. 1.
    Wimalasuriya, D.C., Dou, D.: Ontology-based information extraction: An introduction and a survey of current approaches. Journal of Information Science 36(3), 306–323 (2010)CrossRefGoogle Scholar
  2. 2.
    Li, X., Croft, W.B.: An information-pattern-based approach to novelty detection. Information Processing & Management 44(3), 1159–1188 (2008)CrossRefGoogle Scholar
  3. 3.
    Gabrilovich, E., Dumais, S., Horvitz, E.: Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, pp. 482–490. ACM, New York (2004)Google Scholar
  4. 4.
    Zhang, Y., Callan, J., Minka, T.: Novelty and redundancy detection in adaptive filtering. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2002, pp. 81–88. ACM, New York (2002)CrossRefGoogle Scholar
  5. 5.
    Soboroff, I., Harman, D.: Novelty detection: the TREC experience. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 105–112. Association for Computational Linguistics, Stroudsburg (2005)CrossRefGoogle Scholar
  6. 6.
    Ji, H., Grishman, R., Dang, H.T.: Overview of the TAC2011 Knowledge Base Population Track (2011)Google Scholar
  7. 7.
    Bendersky, M., Metzler, D., Croft, W.B.: Effective query formulation with multiple information sources. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 443–452. ACM, New York (2012)CrossRefGoogle Scholar
  8. 8.
    Meij, E., Bron, M., Hollink, L., Huurnink, B., de Rijke, M.: Learning Semantic Query Suggestions. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 424–440. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Bendersky, M., Croft, W.B.: Modeling higher-order term dependencies in information retrieval using query hypergraphs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 941–950. ACM, New York (2012)CrossRefGoogle Scholar
  10. 10.
    Bendersky, M., Metzler, D., Croft, W.B.: Parameterized concept weighting in verbose queries. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 605–614. ACM, New York (2011)Google Scholar
  11. 11.
    Aleman-Meza, B., Arpinar, I.B., Nural, M.V., Sheth, A.P.: Ranking Documents Semantically Using Ontological Relationships. In: Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing, ICSC 2010, pp. 299–304. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  12. 12.
    Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 509–518. ACM, New York (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Färber
    • 1
  1. 1.Karlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations