Skip to main content
SpringerLink
Log in

Combining Concept Learning and Probabilistic Information Retrieval Model to Understand User’s Searching Intent in OWL Knowledge Base

  • Conference paper
  • First Online:
Book cover Knowledge Management and Acquisition for Intelligent Systems (PKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11016))

Included in the following conference series:

Abstract

Understanding and describing user’s searching intent in exploratory information retrieval is a key issue for improving the relevance of search results. Employing concept learning method and probabilistic information retrieval model, this paper proposes an exploratory information retrieval strategy that can explain user’s search intent in a formal way. User’s relevance feedback from the initial search results are considered as examples and the user’s searching intent is described as concepts learned from the knowledge base and examples. Uncertain inference with respect to the concept learned in knowledge base is used to implement probabilistic information retrieval. By constructing a probabilistic OWL knowledge base, this paper develops a healthcare interactive information retrieval prototype to evaluate the method proposed. The experiment results prove the advantages of using concept learning in exploratory semantic retrieval.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bansal, M., Arora, J.: A review on ontology based information retrieval system. Int. J. Eng. Dev. Res. 4(2), 263–265 (2016)

    Google Scholar 

  2. Tulasi, R.L., et al.: Ontology-Based Automatic Annotation: An Approach for Efficient Retrieval of Semantic Results of Web (2017)

    Google Scholar 

  3. Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    Book  Google Scholar 

  4. Ruotsalo, T., Jacucci, G., Myllymäki, P., et al.: Interactive intent modeling: information discovery beyond search. Commun. ACM 58(1), 86–92 (2015)

    Article  Google Scholar 

  5. McGuinness, D.L., Van Harmelen, F.: OWL Web Ontology Language Overview (W3C Candidate Recommendation 2003) (2015). http://www.w3.org/TR/owl-features/

  6. Basu, A.: Semantic web, ontology, and linked data. Information Retrieval and Management: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, 24 (2018)

    Google Scholar 

  7. Gulwani, S., Hernandez-Orallo, J., Kitzelmann, E., Muggleton, S.H., Schmid, U., Zorn, B.: Inductive programming meets the real world. Commun. ACM 58(11), 90–99 (2015)

    Article  Google Scholar 

  8. Džeroski, S.: Relational data mining. Springer, Boston (2009)

    Book  Google Scholar 

  9. Bühmann, L., Lehmann, J., Westphal, P.: DL-Learner—A framework for inductive learning on the Semantic Web. Web Semant. Sci. Serv. Agents World Wide Web 39, 15–24 (2016)

    Article  Google Scholar 

  10. Lehmann, J., Hitzler, P.: Concept learning in description logics using refinement operators. Mach. Learn. 78(1–2), 203–250 (2010)

    Article  MathSciNet  Google Scholar 

  11. Munir, K., Anjum, M.S.: The use of ontologies for effective knowledge modelling and information retrieval. Appl. Comput. Inf. (2017)

    Google Scholar 

  12. Krishnamurthy, S., Akila, V.: Information retrieval models: trends and techniques. In: Web Semantics for Textual and Visual Information Retrieval, pp. 17–42. IGI Global (2017)

    Google Scholar 

  13. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)

    Article  Google Scholar 

  14. Hu, J., Wang, G., Lochovsky, F., et al.: Understanding user’s query intent with wikipedia. In: Proceedings of the 18th International Conference on World Wide Web, pp. 471–480. ACM, (2009)

    Google Scholar 

  15. Zenz, G., Zhou, X., Minack, E., et al.: From keywords to semantic queries—incremental query construction on the Semantic Web. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 166–176 (2009)

    Article  Google Scholar 

  16. Caruccio, L., Deufemia, V., Polese, G.: Understanding user intent on the web through interaction mining. J. Vis. Lang. Comput. 31, 230–236 (2015)

    Article  Google Scholar 

  17. Bobed, C., Esteban, G., Mena, E.: Enabling keyword search on Linked Data repositories: an ontology-based approach. Int. J. Knowl. Based Intell. Eng. Syst. 17(1), 67–77 (2013)

    Article  Google Scholar 

  18. Loggie, W.T.H.: Using inductive logic programming to assist in the retrieval of relevant information from an electronic library system. In: Notes of the Workshop on Data Mining, Decision Support, Meta Learning and ILP held at The Fourth European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France (2000)

    Google Scholar 

  19. Li, H., Zhengdong, L.: Deep learning for information retrieval. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM (2016)

    Google Scholar 

  20. Lewis, D.D.: Learning in intelligent information retrieval. In: Machine Learning: Proceedings of the Eighth International Workshop (2014)

    Google Scholar 

  21. Fuhr, N.: Probabilistic models in information retrieval. Comput. J. 35(3), 243–255 (1992)

    Article  MathSciNet  Google Scholar 

  22. Sontag, D., et al.: Probabilistic models for personalizing web search. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. ACM (2016)

    Google Scholar 

  23. Zese, R., Bellodi, E., Lamma, E., Riguzzi, F., Aguiari, F.: Semantics and inference for probabilistic description logics. In: Bobillo, F., et al. (eds.) Uncertainty Reasoning for the Semantic Web III, URSW 2012, URSW 2011, URSW 2013. LNCS, vol. 8816, pp. 79–99. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13413-0_5

    Chapter  Google Scholar 

  24. Ferré, S., Hermann, A.: Semantic search: reconciling expressive querying and exploratory search. In: The Semantic Web–ISWC 2011, pp. 177–192 (2011)

    Chapter  Google Scholar 

  25. Varožek, M.: Exploratory search in the adaptive social semantic web. Inf. Sci. Technol. Bull. ACM Slovakia 3(1), 42–51 (2011)

    Google Scholar 

  26. Zenz, G., Zhou, X., Minack, E., et al.: Interactive query construction for keyword search on the semantic web. In: De Virgilio, R., Guerra, F., Velegrakis, Y. (eds.) Semantic Search over the Web, pp. 109–130. Springer, Berlin Heidelberg (2012)

    Chapter  Google Scholar 

  27. Baader, F.: The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, New York (2003)

    MATH  Google Scholar 

  28. Herbelot, A., Vecchi, E.M.: Building a shared world: mapping distributional to model-theoretic semantic spaces. In: EMNLP (2015)

    Google Scholar 

Download references

Acknowledgement

Supported by the Fundamental Research Funds for the Central Universities (grant number GK201503066), and National Natural Science Foundation of China (grant number 61771297).

Author information

Authors and Affiliations

  1. College of Computer Science, Shaanxi Normal University, Xi’an, China

    Liu Yuan

Authors
  1. Liu Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liu Yuan .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tokyo, Japan

    Kenichi Yoshida

  2. Shih Chien University, Taipei City, Taiwan

    Maria Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yuan, L. (2018). Combining Concept Learning and Probabilistic Information Retrieval Model to Understand User’s Searching Intent in OWL Knowledge Base. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97289-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97288-6

  • Online ISBN: 978-3-319-97289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation