Using Automatic Ontology Learning Methods in Human Plausible Reasoning Based Systems
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Using Automatic Ontology Learning Methods in Human Plausible Reasoning Based Systems

Authors: A. R. Vazifedoost, M. Rahgozar, F. Oroumchian

Abstract:

Knowledge discovery from text and ontology learning are relatively new fields. However their usage is extended in many fields like Information Retrieval (IR) and its related domains. Human Plausible Reasoning based (HPR) IR systems for example need a knowledge base as their underlying system which is currently made by hand. In this paper we propose an architecture based on ontology learning methods to automatically generate the needed HPR knowledge base.

Keywords: Ontology Learning, Human Plausible Reasoning, knowledge extraction, knowledge representation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332364

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References:


[1] A.Collins, R. Michalskl, The Logic Of Plausible Reasoning A Core Theory, Cognitive Science, Vol. 13, pp. 1-49. 1989.
[2] M. Burstein, A. Collins and M. Baker. Plausible Generalization: Extending a Model of Human Plausible Reasoning Journal of the Learning Sciences, Vol. 1, No. 2., 1992.
[3] H.Cunningham, Y. Wilks and R. Gaizauskas. GATE -- a General Architecture for Text Engineering. In Proceedings of the 16th Conference on Computational Linguistics (COLING96) , 1996
[4] P. Cimiano, J. Volker, Text2Onto, a FrameWork for Ontlogy Learning and Data-dirven Change Discovery, In Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB'05). 2005
[5] P. Velardi, R. Navigli, A. Cuchiarelli, F. Neri, Evaluation of OntoLearn, a Methodology for Automatic Learning of Domain Ontologies, In Ontology Learning from Texts: Methods, Evaluation and application, IOS Press.2005
[6] R. Navigli and P. Velardi. Learning DomainOntologies from Document Warehouses and Dedicated Websites. Computational Linguistics, 30(2), 2004
[7] M.A. Hearst. Automatic Acquisition of Hyponyms from Large Text Corpora. In proceedings of the14th international conference on computational linguistics, 1992.
[8] E. Charniak and M. Berland. Finding parts in very large corpora. In Proceedings of the 37th Annual Meeting of the ACL, 1999.
[9] P. Cimiano, A. Pivk, L. Schmidt-Thieme, and S. Staab. Learning taxonomic relations from heterogeneous evidence. In Ontology Learning from Text: Methods, Applications and Evaluation. IOS Press, 2005.
[10] Hearst. Untangling text data mining. In Proceedings of the ACL-99: the 37th Annual Meeting of the Association for Computational Linguistics. University of Maryland, 1999
[11] D. Faure and C. N'edellec. ASIUM: Learning subcategorization frames and restrictions of selection. In 10th Conference on Machine Learning -- Workshop on Text Mining, Chemnitz, Germany, 1998.
[12] R. Girju and D. I. Moldovan. Text mining for causal relations. In FLAIRS 2002
[13] F. Oroumchian, R.N. Oddy, An Application of Plausible Reasoning to Information Retrieval, ACM-s SIGIR 1996
[14] M. Karimzadegan, F. Oroumchian, J. Habibi, XML Information Retrieval by Means of Plausible Inferences. In Proceedings of the 5th International Conference on Recent Advances in Soft Computing, 2004.
[15] F. Oroumchian, B.N. Araabi, E. Ashoori, An Application of Plausible Reasoning and Dempster-Shafer Theory of Evidence in Information Retrieval. In proceeding of InternationalConference on Fuzzy Systems and Knowledge Discovery (FSKD), 2002.
[16] F. Oroumchian, B. Khandzah, Modeling an Intelligent Tutoring System by Plausible Inferences. In proceeding of International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), 2002.
[17] A. Jalali, F. Oroumchian, An Evaluation of Document Clustering by means of Plausible Inferences. International Journal of Computational Intelligence, 2004.
[18] E. Darrudi, M. Rahgozar, F. Oroumchian, Human Plausible Reasoning for Question Answering Systems. In proceeding of Advances in Intelligent Systems - Theory and Applications.Luxembourg, 2004.