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A Proposal for Annotation, Semantic Similarity and Classification of Textual Documents

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4183))

Abstract

In this paper, we present an approach for classifying documents based on the notion of a semantic similarity and the effective representation of the content of the documents. The content of a document is annotated and the resulting annotation is represented by a labeled tree whose nodes and edges are represented by concepts lying within a domain ontology. A reasoning process may be carried out on annotation trees, allowing the comparison of documents between each others, for classification or information retrieval purposes. An algorithm for classifying documents with respect to semantic similarity and a discussion conclude the paper.

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References

  1. Al-Hulou, R., Napoli, A., Nauer, E.: Une mesure de similarité sémantique pour raisonner sur des documents. In: Euzenat, J., Carré, B. (eds.) Langages et modéles á objets, Lille (LMO 2004), Hermés, L’objet, vol. 10(2–3), pp. 217–230 (2004)

    Google Scholar 

  2. Antoniou, G., van Harmelen, F.: A Semantic Web Primer. MIT Press, Cambridge (2004)

    Google Scholar 

  3. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  4. Baader, F., Sertkaya, B.: Applying Formal Concept Analysis to Description Logics. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 261–286. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Baader, F., Sertkaya, B., Turhan, A.-Y.: Computing the least common subsumer w.r.t. a background terminology. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS, vol. 3229, pp. 400–412. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Baader, F.: Computing the least common subsumer in the description logic \(\cal EL\) w.r.t. terminological cycles with descriptive semantics. In: Ganter, B., de Moor, A., Lex, W. (eds.) ICCS 2003. LNCS, vol. 2746, pp. 117–130. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  7. Chein, M., Mugnier, M.-L.: Conceptual graphs: Fundamental notions. Revue d’intelligence artificielle 6(4), 365–406 (1992)

    Google Scholar 

  8. Fensel, D., Hendler, J., Lieberman, H., Wahlster, W. (eds.): Spinning the Semantic Web. MIT Press, Cambridge (2003)

    Google Scholar 

  9. Handschuh, S., Staab, S.: Annotation for the Semantic Web. Frontiers in Artificial Intelligence and Applications, vol. 96. IOS Press, Amsterdam (2003)

    MATH  Google Scholar 

  10. Heflin, J., Hendler, J.A., Luke, S.: SHOE: A blueprint for the semantic web. In: Spinning the Semantic Web, pp. 29–63 (2003)

    Google Scholar 

  11. Langley, P.: Elements of Machine Learning. Morgan Kaufmann Publishers, San Francisco (1996)

    Google Scholar 

  12. Lieber, J., Napoli, A.: Correct and Complete Retrieval for Case-Based Problem-Solving. In: Prade, H. (ed.) Proceedings of the 13th European Conference on Artificial Intelligence (ECAI 1998), Brighton, UK, pp. 68–72. John Wiley & Sons Ltd., Chichester (1998)

    Google Scholar 

  13. Maedche, A., Staab, S., Stojanovic, N., Studer, R., Sure, Y.: SEmantic portAL: the SEAL Approach. In: Fensel, D., Hendler, J., Lieberman, H., Wahlster, W. (eds.) Spinning the Semantic Web, pp. 317–359. MIT Press, Cambridge (2003)

    Google Scholar 

  14. Mugnier, M.L.: On generalization/specialization for conceptual graphs. Journal of Experimental & Theoretical Artificial Intelligence 6(3), 325–344 (1995)

    Article  Google Scholar 

  15. Napoli, A., Laurenço, C., Ducournau, R.: An object-based representation system for organic synthesis planning. International Journal of Human-Computer Studies 41(1/2), 5–32 (1994)

    Article  Google Scholar 

  16. Sebastiani, F. (ed.): ECIR 2003. LNCS, vol. 2633. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  17. Staab, S., Studer, R. (eds.): Handbook on Ontologies. Springer, Berlin (2004)

    Google Scholar 

  18. Uren, V., Cimiano, P., Iria, J., Handschuh, S., Vargas-Vera, M., Motta, E., Ciravegna, F.: Semantic annotation for knowledge management: Requirements and a survey of the state of the art. Journal of Web Semantics 4(1) (2005)

    Google Scholar 

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Nauer, E., Napoli, A. (2006). A Proposal for Annotation, Semantic Similarity and Classification of Textual Documents. In: Euzenat, J., Domingue, J. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2006. Lecture Notes in Computer Science(), vol 4183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861461_22

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  • DOI: https://doi.org/10.1007/11861461_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40930-4

  • Online ISBN: 978-3-540-40931-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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