Abstract
We introduce an Information Extraction (IE) system which uses the logical theory of an ontology as a generalisation of the typical information extraction patterns to extract biological interactions from text. This provides inferences capabilities beyond current approaches: first, our system is able to handle multiple relations; second, it allows to handle dependencies between relations, by deriving new relations from the previously extracted ones, and using inference at a semantic level; third, it addresses recursive or mutually recursive rules. In this context, automatically acquiring the resources of an IE system becomes an ontology learning task: terms, synonyms, conceptual hierarchy, relational hierarchy, and the logical theory of the ontology have to be acquired. We focus on the last point, as learning the logical theory of an ontology, and a fortiori of a recursive one, remains a seldom studied problem. We validate our approach by using a relational learning algorithm, which handles recursion, to learn a recursive logical theory from a text corpus on the bacterium Bacillus subtilis. This theory achieves a good recall and precision for the ten defined semantic relations, reaching a global recall of 67.7% and a precision of 75.5%, but more importantly, it captures complex mutually recursive interactions which were implicitly encoded in the ontology.
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References
Ananiadou, S., Kell, D.B., Tsujii, J.: Text mining and its potential applications in systems biology. Trends in Biotechnology 24 (2006)
Ciaramita, M., Gangemi, A., Ratsch, E., Saric, J., Rojas, I.: Unsupervised learning of semantic relations between concepts of a molecular biology ontology. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, UK (2005)
Manine, A.P., Alphonse, E., Bessiere, P.: Information extraction as an ontology population task and its application to genic interactions. In: 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2008, vol. 2, pp. 74–81 (2008)
Craven, M., Kumlien, J.: Constructing biological knowledge bases by extracting information from text sources. In: Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, pp. 77–86. AAAI Press, Menlo Park (1999)
Rindflesch, T., Tanabe, L., Weinstein, J., Hunter, L.: EDGAR: extraction of drugs, genes and relations from the biomedical literature. In: Proceedings of the Fifth Pacific Symposium on Biocomputing (PSB 2003), pp. 517–528 (2000)
Blaschke, C., Andrade, M.A., Ouzounis, C., Valencia, A.: Automatic extraction of biological information from scientific text: Protein-protein interactions. In: Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, pp. 60–67. AAAI Press, Menlo Park (1999)
Ono, T., Hishigaki, H., Tanigami, A., Takagi, T.: Automated extraction of information on protein-protein interactions from the biological literature. Bioinformatics 17, 155–161 (2001)
Saric, J., Jensen, L., Ouzounova, R., Rojas, I., Bork, P.: Large-scale extraction of protein/gene relations for model organisms. In: First International Symposium on Semantic Mining in Biomedicine 2005 (2005)
Nédellec, C.: Learning language in logic — Genic interaction extraction challenge. In: Cussens, J., Nédellec, C. (eds.) Proceedings of the Fourth Learning Language in Logic Workshop (LLL 2005), pp. 31–37 (2005)
Fundel, K., Küffner, R., Zimmer, R.: RelEx — relation extraction using dependency parse trees. Bioinformatics 23, 365–371 (2007)
Hauser, M.D., Chomsky, N., Fitch, W.T.: The faculty of language: What is it, who has it, and how did it evolve? Science 298, 1569–1579 (2002)
Boström, H.: Induction of recursive transfer rules. In: Cussens, J., Džeroski, S. (eds.) LLL 1999. LNCS (LNAI), vol. 1925, pp. 52–62. Springer, Heidelberg (2000)
Gómez-Pérez, A.: Ontological engineering: A state of the art. Expert Update 2, 33–43 (1999)
McGuinness, D., van Harmelen, F.: OWL web ontology language overview: W3C recommendation, February 10, 2004, Technical report, W3C (2004)
Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and frame-based languages. J. ACM 42, 741–843 (1995)
Salakoski, T., Rebholz-Schuhmann, D., Pyysalo, S. (eds.): Proceedings of the Third International Symposium on Semantic Mining in Biomedicine (SMBM 2008). Turku Centre for Computer Science (TUCS), Turku (2008)
Krallinger, M., Leitner, F., Valencia, A.: Assessment of the second BioCreAtIvE PPI task: Automatic extraction of protein-protein interactions. In: Proceedings of the Second BioCreAtIvE Challenge Evaluation Workshop, pp. 41–54 (2007)
Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. Journal of Artificial Intelligence Research (JAIR) 24 (2005)
Varlaro, A., Berardi, M., Malerba, D.: Learning recursive theories with the separate-and-parallel conquer strategy. In: Proceedings of the Workshop on Advances in Inductive Rule Learning in conjunction with ECML/PKDD, pp. 179–193 (2004)
Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: An overview. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications, vol. 123. IOS Press, Amsterdam (2005)
Lin, D., Pantel, P.: DIRT discovery of inference rules from text. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 323–328. ACM, New York (2001)
Völker, J., Vrandecic, D., Sure, Y., Hotho, A.: Learning disjointness. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 175–189. Springer, Heidelberg (2007)
Riloff, E.: Automatically generating extraction patterns from untagged text. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI 1996), pp. 1044–1049. AAAI Press / The MIT Press (1996)
Rosario, B., Hearst, M.A.: Classifying semantic relations in bioscience texts. In: ACL 2004: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Morristown, NJ, USA, p. 430. Association for Computational Linguistics (2004)
Berardi, M., Malerba, D.: Learning recursive patterns for biomedical information extraction. In: Muggleton, S., Otero, R.P., Tamaddoni-Nezhad, A. (eds.) ILP 2006. LNCS (LNAI), vol. 4455, pp. 79–93. Springer, Heidelberg (2007)
Cimiano, P., Haase, P., Herold, M., Mantel, M., Buitelaar, P.: LexOnto: A model for ontology lexicons for ontology-based NLP. In: Proceedings of the OntoLex 2007 Workshop held in conjunction with ISWC 2007 (2007)
Buitelaar, P., Sintek, M., Kiesel, M.: A multilingual/multimedia lexicon model for ontologies. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 502–513. Springer, Heidelberg (2006)
Muggleton, S., Raedt, L.D.: Inductive Logic Programming: Theory and methods. Journal of Logic Programming 19, 20, 629–679 (1994)
Bunescu, R., Ge, R., Kate, R.J., Marcotte, E.M., Mooney, R.J., Ramani, A.K., Wong, Y.W.: Comparative experiments on learning information extractors for proteins and their interactions. Artificial Intelligence in Medicine 33, 139–155 (2005)
Pyysalo, S., Airola, A., Heimonen, J., Bjorne, J., Ginter, F., Salakoski, T.: Comparative analysis of five protein-protein interaction corpora. BMC Bioinformatics 9, S6 (2008)
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Manine, AP., Alphonse, E., Bessières, P. (2010). Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2010. Lecture Notes in Computer Science, vol 6008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12116-6_47
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DOI: https://doi.org/10.1007/978-3-642-12116-6_47
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