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Semantic Interoperability among Thesauri: A Challenge in the Multicultural Legal Domain

  • Enrico Francesconi
  • Ginevra Peruginelli
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 57)

Abstract

In the last few years crucial issues like cross-language legal information retrieval, document classification, legal knowledge discovery and extraction have been considered in theory and in practice. The availability of services allowing cross-language and cross-collection retrieval is a growing necessity. This paper focuses on the need to develop solutions for automatic, language-independent procedures to provide interoperability between mono/poly-lingual thesauri at national and European levels. This will guarantee sustainable and scalable services enabling to manage the multilingual complexity of the European Union legal context to be used for cross-language and cross-collection legal information retrieval. Wider use of the service can also be envisaged as support to legal translation services, as well as in general to promote integration and sharing of widespread and heterogeneous legal resources, providing new market opportunities for stakeholders to exploit the economic potential of public sector information in a multilanguage environment.

Keywords

Thesauri Semantic Interoperability Legal Information Indexing Cross-collection Information Retrieval SKOS 

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References

  1. 1.
    Sadat, F., Yoshikawa, M., Uemura, S.: Exploiting thesauri and hierarchical categories in cross-language information retrieval. Springer, Berlin (2002)Google Scholar
  2. 2.
    Chan, L.M., Zeng, M.L.: Ensuring interoperability among subject vocabularies and knowledge (2002), http://www.ifla.org/IV/ifla68/papers/008-122e.pdf
  3. 3.
    Fluhr, C.: Multilingual information retrieval. In: Survey of the State of the Art in Human Language Technology (1996), http://cslu.cse.ogi.edu/HLTsurvey/ch8node7.html
  4. 4.
    Oard, D.W.: Alternative approaches for cross-language text retrieval (1997), http://www.glue.umd.edu/~dlrg/filter/sss/papers/oard/paper.html
  5. 5.
    Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)CrossRefGoogle Scholar
  6. 6.
    Curtin, D.M.: Citizens’ fundamental right of access to eu information: an evolving digital Passepartout? Common Market Law Review 37(1), 7–41 (2000)CrossRefGoogle Scholar
  7. 7.
    Doerr, M.: Semantic problems of thesaurus mapping. Journal of Digital Information 1(8) (2001)Google Scholar
  8. 8.
    Miles, A., Matthews, B.: Deliverable 8.4: Inter-thesaurus mapping (2004), http://www.w3c.rl.ac.uk/SWAD/deliverables/8.4.html
  9. 9.
    Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. The International Journal on Very Large Data Bases 10(4), 334–350 (2001)CrossRefGoogle Scholar
  10. 10.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Trojahn, C., Moraes, M., Quaresma, P., Vieira, R.: A Cooperative Approach for Composite Ontology Mapping. Journal on Data Semantics, 237–263 (2008)Google Scholar
  12. 12.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  13. 13.
    Miles, A., Brickley, D.: Skos simple knowledge organization system reference (2008), http://www.w3.org/TR/skos-reference
  14. 14.
    Sowa, J.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)Google Scholar
  15. 15.
    Zhong, J., Zhu, H., Li, J., Yu, Y.: Conceptual graph matching for semantic search. In: Priss, U., Corbett, D.R., Angelova, G. (eds.) ICCS 2002. LNCS (LNAI), vol. 2393, pp. 92–106. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Montes-y-Gómez, M., Gelbukh, A., López-López, A., Baeza-Yates, R.: Flexible Comparison of Conceptual Graphs. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 102–111. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Rasmussen, E.: Clustering algorithms. In: Frakes, W.B., Baeza-Yates, R. (eds.) Information Retrieval: Data Structures & Algorithms (1992)Google Scholar
  18. 18.
    Cai, W.-T., Wang, S.-R., Jiang, Q.-S.: Address extraction: a graph matching and ontology-based approach to conceptual information retrieval. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 1571–1576 (2004)Google Scholar
  19. 19.
    Liang, A.C., Sini, M.: Mapping AGROVOC and the Chinese Agricultural Thesaurus: Definitions, tools, procedures. New Review of Hypermedia and Multimedia 12(1), 51–62 (2006)CrossRefGoogle Scholar
  20. 20.
    Tiscornia, D.: The Lois Project: Lexical Ontologies for Legal Information Sharing. In: Proceedings of the V Legislative XML Workshop, pp. 189–204. European Press Academic Publishing (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Enrico Francesconi
    • 1
  • Ginevra Peruginelli
    • 1
  1. 1.Institute of Legal Information Theory and TechniquesItalian National Research Council 

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