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References

  • Alexiev, V., Fensel, D., Breu, M., de Bruijn, J., Lara, R., & Lausen, H. (2005). Information integration with ontologies: experiences from an industrial showcase. John Wiley and Sons.
  • Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics, 25 (1), 25.
  • Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M., & Etzioni, O. (2007). Open information extraction from the web. Procs. of the International Joint Conference on Artificial Intelligence.
  • Bunescu, R. C., & Mooney, R. J. (2005). A shortest path dependency kernel for relation extraction. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 724731). Vancouver, British Columbia, Canada: Association for Computational Linguistics.
  • Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge Univ Pr.
  • Culotta, A., & Sorensen, J. (2004). Dependency tree kernels for relation extraction. In Proceedings of ACL (Vol. 4).
  • Fellbaum, C. (1998). WordNet: An electronic lexical database. MIT press Cambridge, MA.
  • Finin, T., Fritzson, R., McKay, D., & McEntire, R. (1994). KQML as an agent communication language. In Proceedings of the third international conference on Information and knowledge management (p. 463). ACM.
  • Fundel, K., Kuffner, R., & Zimmer, R. (2007). RelEx–relation extraction using dependency parse trees. Bioinformatics, 23 (3), 365.
  • Gildea, D., & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28 (3), 245288.
  • Girju, R., Badulescu, A., & Moldovan, D. (2006). Automatic discovery of part-whole relations. Computational Linguistics, 32 (1), 83135.
  • Giuliano, C., Lavelli, A., & Romano, L. (2006). Exploiting shallow linguistic information for relation extraction from biomedical literature. In Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics (EACL-2006) (pp. 57).
  • Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5, 199199.
  • Guarino, N., Masolo, C., & Vetere, G. (1999). Ontoseek: Content-based access to the web. IEEE Intelligent Systems and their Applications, 14 (3), 7080.
  • Harris, Z. (1954). Distributional structure. Word, 10 (23), 146162.
  • Hasegawa, T., Sekine, S., & Grishman, R. (2004). Discovering relations among named entities from large corpora. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (p. 415). Barcelona, Spain: Association for Computational Linguistics.
  • Hersh, W., & Voorhees, E. (2009). TREC genomics special issue overview. Information Retrieval, 12 (1), 115.
  • Lapata, M. (2002). The disambiguation of nominalizations. Computational Linguistics, 28 (3), 357388.
  • Lin, D., & Pantel, P. (2001). DIRT-discovery of inference rules from text. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 323328). Citeseer.
  • Matuszek, C., Cabral, J., Witbrock, M., & DeOliveira, J. (2006). An introduction to the syntax and content of Cyc. In Proceedings of the 2006 AAAI Spring Symposium on Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering (pp. 4449). Citeseer.
  • Morris, J., & Hirst, G. (2004). Non-classical lexical semantic relations. In Workshop on Computational Lexical Semantics, Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4651).
  • NLM 2006. The SPECIALIST Lexicon. Available at: http://www.nlm.nih.gov/pubs/factsheets/umls.html
  • Nirenburg, S., & Raskin, V. (2004). Ontological semantics. MIT Press Boston, MA.
  • Paolucci, M., Kawamura, T., Payne, T., & Sycara, K. (2002). Semantic matching of web services capabilities. The Semantic Web—ISWC 2002, 333347.
  • Rosario, B., & Hearst, M. A. (2005). Multi-way relation classification: application to protein-protein interactions. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (p. 739). Association for Computational Linguistics.
  • Smith, B., Ceusters, W., Klagges, B., Köhler, J., Kumar, A., Lomax, J., Mungall, C., et al. (2005). Relations in biomedical ontologies. Genome Biology, 6 (5), R46.
  • Snow, R., Jurafsky, D., & Ng, A. Y. (2006). Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics (p. 808). Association for Computational Linguistics.
  • Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web (p. 706). ACM.
  • Suchanek, F. M., Ifrim, G., & Weikum, G. (2006). Combining linguistic and statistical analysis to extract relations from web documents. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 712717). Philadelphia, PA, USA: ACM.
  • Vapnik, V. N. (2000). The nature of statistical learning theory. Springer.
  • Welty, C., & Ide, N. (1999). Using the right tools: enhancing retrieval from marked-up documents. Computers and the Humanities, 33 (1), 5984.
  • Yates, A., & Etzioni, O. (2009). Unsupervised methods for determining object and relation synonyms on the web. Journal of Artificial Intelligence Research, 34 (1), 255296.
  • Zelenko, D., Aone, C., & Richardella, A. (2003). Kernel methods for relation extraction. J. Mach. Learn. Res., 3, 10831106.