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An Efficient Ontology-Based Expert Peering System

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Graph-Based Representations in Pattern Recognition (GbRPR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4538))

Abstract

This paper proposes a novel expert peering system for information exchange. Our objective is to develop a real-time search engine for an online community where users can query experts, who are simply other participating users knowledgeable in that area, for help on various topics. We consider a graph-based scheme consisting of an ontology tree where each node represents a (sub)topic. Consequently, the fields of expertise or profiles of the participating experts correspond to subtrees of this ontology. Since user queries can also be mapped to similar tree structures, assigning queries to relevant experts becomes a problem of graph matching. A serialization of the ontology tree allows us to use simple dot products on the ontology vector space effectively to address this problem. As a demonstrative example, we conduct extensive experiments with different parameterizations. We observe that our approach is efficient and yields promising results.

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References

  1. Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Press, Dordrecht (2002)

    Google Scholar 

  2. Hofmann, T.: Latent Semantic Models for Collaborative Filtering. ACM Trans. on Information Systems 22(1), 89–115 (2004)

    Article  Google Scholar 

  3. Ding, C.: Document Retrieval and Clustering: from Principal Component Analysis to Self-aggregation Networks. In: Proc. Int. Workshop on Artificial Intelligence and Statistics (2003)

    Google Scholar 

  4. Miller, L.: Document Representation Models for Retrieval Systems. ACM SIGIR Forum 14(2), 41–44 (1979)

    Article  Google Scholar 

  5. Schenker, A., Last, M., Bunke, H., Kandel, A.: Classification of web documents using a graph mode. In: Proc. Int. Conf. on Document Analysis and Recognition, pp. 240–244 (2003)

    Google Scholar 

  6. Schenker, A., Last, M., Bunke, H., Kandel, A.: Classification of Web Documents Using Graph Matching. Int. J. of Patter Recognition and Artificial Intelligence 18(3), 475–496 (2004)

    Article  Google Scholar 

  7. Lim, S.Y., Park, S.B., Lee, S.J.: Document retrieval using semantic relation in domain ontology. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 266–271. Springer, Heidelberg (2005)

    Google Scholar 

  8. Chung, S., Jun, J., McLeod, D.: A web-based novel term similarity framework for ontology learning. In: ODBASE: Int. Conf. on Ontologies, Databases and Applications of Semantics, Montpellier, France (2006)

    Google Scholar 

  9. Wu, J., Yang, G.: An ontology-based method for project and domain expert matching. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 176–185. Springer, Heidelberg (2006)

    Google Scholar 

  10. Bird, S., Klein, E., Loper, E.: The natural language toolkit (NLTK) (2001)

    Google Scholar 

  11. Porter, M.: An Algorithm for Suffix Stripping. Program 14(3), 130–137 (1980)

    Google Scholar 

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Francisco Escolano Mario Vento

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© 2007 Springer-Verlag Berlin Heidelberg

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Alpcan, T., Bauckhage, C., Agarwal, S. (2007). An Efficient Ontology-Based Expert Peering System. In: Escolano, F., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2007. Lecture Notes in Computer Science, vol 4538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72903-7_25

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  • DOI: https://doi.org/10.1007/978-3-540-72903-7_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72902-0

  • Online ISBN: 978-3-540-72903-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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