Using Automatic Ontology Learning Methods in Human Plausible Reasoning Based Systems

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.

Authors:

• A. R. Vazifedoost
• M. Rahgozar
• F. Oroumchian

Keywords:

• Ontology Learning
• Human Plausible Reasoning
• knowledge extraction
• knowledge representation.

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.