Skip to main content

An Ontology-Based Method for Project and Domain Expert Matching

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3614))

Abstract

In this paper, we present a novel method to find the right expert who matches a certain project well. The idea behind this method includes building domain ontologies to describe projects and experts and calculating similarities between projects and domain experts for matching. The developed system consists of four main components: ontology building, document formalization, similarity calculation and user interface. First, we utilize Protégé to develop the predetermined domain ontologies in which some related concepts are defined. Then, documents concerning experts and projects are formalized by means of concept trees with weights. This process can be done either automatically or manually. Finally, a new method that integrates node-based and edge-based approach is proposed to measure the semantic similarities between projects and experts with the help of the domain ontologies. The experimental results show that the developed information matching system can reach the satisfied recall and precision.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Guarino, N., Giaretta, P.: Ontologies and knowledge bases: Towards a terminological clarification. In: Mars, N.J.I. (ed.) Towards Very Large Know ledge Bases, IOS Press, Amsterdam (1995)

    Google Scholar 

  2. Heftin, J., Hendler, J.: Searching the Web with SHOE. In: Artificial Intelligence for Web Search. Papers from the AAAI Workshop. 01, pp. 35–40. AAAI Press, Menlo Park (2000)

    Google Scholar 

  3. Resnick, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), pp. 448–453 (1995)

    Google Scholar 

  4. Leacock, C., Chodorow, M.: Filling in a sparse training space for word sense identification. ms (1994)

    Google Scholar 

  5. Salton, G., Wong, A.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18, 613–620 (1975)

    Article  MATH  Google Scholar 

  6. Guarino, N., Masolo, C., Vetere, G.: OntoSeek: Content-Based Access to the Web. IEEE Intelligent Systems 14(3), 70–80 (1999)

    Article  Google Scholar 

  7. Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)

    MATH  Google Scholar 

  8. Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proc. of Int’l Conf. on Research on Computational Linguistics, Taiwan (1997)

    Google Scholar 

  9. Gruber, T.: Toward principles for the design of ontologies used for knowledge sharing. Technical Report KSL-93-4, Knowledge Systems Laboratory, Stanford University. Communications of the ACM 37(7), 48–53 (1993)

    Google Scholar 

  10. Decker, S., Erdmann, M., Fensel, D., Studer, R.: Ontobroker: Ontology based access to distributed and semi-structured information. In: Meersman, R. (ed.) Proceedings of DS-8 Semantic Issues in Multimedia Systems, pp. 351–369. Kluwer Academic Publisher, Dordrecht (1999)

    Google Scholar 

  11. Protégé: http://protege.stanford.edu

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, J., Yang, G. (2005). An Ontology-Based Method for Project and Domain Expert Matching. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_22

Download citation

  • DOI: https://doi.org/10.1007/11540007_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28331-7

  • Online ISBN: 978-3-540-31828-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics