Abstract
We propose in this paper an approach to learn term to concept mapping with the joint utilization of an existing ontology and verb relations. This is a non-supervised solution that can be applied to any field for which an ontology modeling verbs as relations holding between the concepts was already created. Conceptual graphs are learned from a natural language corpus by using part-of-speech information and statistic measures. Labeling strategies are proposed to assign terms of the corpus to concepts of the ontology by taking into account the structure of the ontology and the extracted conceptual graphs. This paper presents the approach proposed to learn the conceptual graphs from the corpus and the labeling strategies. A first experimentation in the field of accidentology was done and its results are also presented.
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References
Ceausu, V., Desprès, S.: Towards a text mining driven approach for terminology construction. In: 7th International conference on Terminology and Knowledge Engineering (2005)
Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: International Conference on New Methods in Language Processing (1994)
Roux, C., Prouxet, D., Rechenmann, F., Julliard, L.: An ontology enrichment method for a pragmatic information extraction system gathering data on genetic interactions. In: Ontology Learning Workshop at ECAI (2000)
Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string distance metrics for name-matching tasks. In: IJCAI 2003,Workshop on Information Integration on the Web pages (2003)
Monge, A., Elkan, C.: The field-matching problem: algorithm and applications. In: Second International Conference on Knowledge Discovery and Data Mining (1996)
Biébow, B., Szulman, S.: A linguistic-based tool for the building of a domain ontology. In: International Conference on Knowledge Engineering and Knowledge Management (1999)
Szulman, S., Biébow, B.: Owl et terminae. 14-ème Journée Francophone d’ Ingénierie des Connaissances (2004)
Faure, D., Nedellec, C.: Asium, learning subcategorization frames and restrictions of selection. In: 10th European Conference On Machine Learning, Workshop on text mining, Chemnitz, Germany (1998)
Schutz, A., Buitelaar, P.: Relext: A tool for relation extraction from text in ontology extension. In: International Semantic Web Conference, pp. 593–606 (2005)
Alfonseca, E., Manandhar, S.: Improving an ontology refinement method with hyponymy patterns. In: Third International Conference on Language Resources and Evaluation (2001)
Faatz, A., Steinmetz, R.: Ontology enrichment with texts from the www. In: SemanticWeb Mining 2nd Workshop at ECML/PKDD (2002)
Monge, A., Elkan, C.: An efficient domain-independent algorithm for detecting approximately duplicate database records. In: Workshop on data mining and knowledge discovery, SIGMOD (1997)
Miller, G.: Wordnet: A lexical database for english. CACM 38, 39–41 (1995)
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Ceausu, V., Desprès, S. (2006). An Ontology Supported Approach to Learn Term to Concept Mapping. In: Hoffmann, A., Kang, Bh., Richards, D., Tsumoto, S. (eds) Advances in Knowledge Acquisition and Management. PKAW 2006. Lecture Notes in Computer Science(), vol 4303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11961239_20
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DOI: https://doi.org/10.1007/11961239_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68955-3
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