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Acquiring an Ontology from the Text

A Legal Case Study

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4031))

Abstract

A topic ontology applies the usual ontological constructs to the task of annotating the topic of a document. The topic is the highly summarized essence of the document. The topics are usually chosen intuitively and rarely questioned. However, we have studied several ways of allocating frequently asked questions from a legal domain into a set of topical sub-domains. Our criteria were: 1) The sub-domains should not overlap. 2) The sub-domain should be objectively identifiable from the words of the text. 3) Which words and grammatical categories can serve as keywords? 4) Can the structure of sub-domains be induced semi-automatically from the text itself?

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References

  1. Benjamins, V.R., Contreras, J., Blázquez, M., Rodrigo, L., Casanovas, P., Poblet, M.: The SEKT use legal case components: ontology and architecture. In: Gordon, T.B. (ed.) Legal Knowledge and Information Systems. Jurix 2004, pp. 69–77. IOS Press, Amsterdam (2004)

    Google Scholar 

  2. Benjamins, V.R., Casanovas, P., Contreras, J., Lpez-Cobo, J.M., Lemus, L.: Iuriservice: An Intelligent Frequently Asked Questions System to Assist Newly Appointed Judges. In: Benjamins, V.R., et al. (eds.) Law and the Semantic Web, pp. 205–222. Springer, London (2005)

    Chapter  Google Scholar 

  3. Casanovas, P., Poblet, M., Casellas, N., Vallbé, J.J., Ramos, F., Benjamins, R., Blázquez, M., Rodrigo, L., Contreras, J., Gorro-ogoitia, J.: D 10.2.1 Legal Scenario. Deliverable WP10 Case Study: Intelligent integrated decision support for legal professionals. SEKT Project (2005), www.sekt-project.com

  4. Casanovas, P., Poblet, M., Casellas, N., Contreras, J., Benjamins, V.R., Blázquez, M.: Supporting newly-appointed judges: a legal knowledge management case study. Journal of Knowledge Management 9(5), 7–27 (2005)

    Article  Google Scholar 

  5. Casanovas, P., Casellas, N., Tempich, C., Vrandec̆ić, D., Benjamins, V.R.: OPJK modeling methodology. In: Lehman, J., Biasiotti, M.A., Francesconi, E., Sagri, M.T. (eds.): IAAIL Proceedings. LOAIT - Legal Ontologies and Artificial Intelligence Techniques. Bologna, pp. 121–133 (June 2005)

    Google Scholar 

  6. Casellas, N., Blázquez, M., Kiryakov, A., Casanovas, P., Benjamins, V.R.: OPJK into PROTON: legal domain ontology integration into an upper-level ontology. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2005. LNCS, vol. 3762, pp. 846–855. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Jakulin, A., Moz̆ina, M., Dems̆ar, J., Brat́ko, I., Zupan, B.: Nomograms for visualizing support vector machines. In: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. KDD 2005 (2005)

    Google Scholar 

  8. Joachims, T.: Making large-scale SVM learning practical. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)

    Google Scholar 

  9. Lewis, D.D., Yang, Y., Rose, T., Li, F.: RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research 5, 361–397 (2004)

    Google Scholar 

  10. Terziev, I., Kiryakov, A., Manov, D.: D 1.8.1. Base upper-level ontology (BULO) Guidance, report EU-IST Integrated Project (IP) IST-2003- 506826 SEKT) (2004)

    Google Scholar 

  11. Vallbé, J.J., Martí, M.A., Fortuna, B., Jakulin, A., Mladenić, D., Casanovas, P.: Stemming and lemmatisation: improving knowledge management through language processing techniques. In: Casanovas, P., Bourcier, D., Noriega, P., Cáceres, E., Galindo, F.: The regulation of electronic social systems. Law and the Semantic Web. Proceedings of the B4-Workshop on Artificial Intelligence and Law. IVR 2005-Granada, May 25th-27th. XXII World Conference of Philosophy of Law and Social Philosophy. Instituto de Investigaciones Jurídicas, UNAM (México) (in press, 2005) http://www.lefis.org

  12. Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Casellas, N., Jakulin, A., Vallbé, JJ., Casanovas, P. (2006). Acquiring an Ontology from the Text. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_107

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  • DOI: https://doi.org/10.1007/11779568_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35453-6

  • Online ISBN: 978-3-540-35454-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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