A Bayesian Methodology towards Automatic Ontology Mapping

This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. The pro-posed method includes four components: 1) learning prob-abilities (priors about concepts, conditionals between sub-concepts and superconcepts, and raw semantic similarities between concepts in two different ontologies) using Naive Bayes text classification technique, by explicitly associating a concept with a group of sample documents retrieved and selected automatically from World Wide Web (WWW); 2) representing in OWL the learned probability information concerning the entities and relations in given ontologies; 3) using the BayesOWL framework to automatically translate given ontologies into the Bayesian network (BN) structures and to construct the conditional probability tables (CPTs) of a BN from those learned priors or conditionals, with reason-ing services within a single ontology supported by Bayesian inference; and 4) taking a set of learned initial raw similarities as input and finding new mappings between concepts from two different ontologies as an application of our formalized BN mapping theory that is based on evidential reasoning across two BNs.

[1]  Pedro M. Domingos,et al.  Learning to map between ontologies on the semantic web , 2002, WWW '02.

[2]  Georg Groh,et al.  Facilitating the Exchange of Explicit Knowledge through Ontology Mappings , 2001, FLAIRS.

[3]  Yun Peng,et al.  A probabilistic extension to ontology language OWL , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[4]  Leah S. Larkey,et al.  Some Issues in the Automatic Classification of U.S. Patents Working Notes for the AAAI-98 Workshop on Learning for Text Categorization , 1998 .

[5]  W. Deming,et al.  On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known , 1940 .

[6]  I. Csiszár.  $I$-Divergence Geometry of Probability Distributions and Minimization Problems , 1975 .

[7]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[8]  Yun Peng,et al.  AT ool For Mapping Between Two Ontologies Using Explicit Information , 2002 .

[9]  Anuj R. Jaiswal,et al.  OMEN: A Probabilistic Ontology Mapping Tool , 2005, SEMWEB.

[10]  Heiner Stuckenschmidt,et al.  Semantic Translation Based on Approximate Re-Classication , 2000 .

[11]  Jirí Vomlel,et al.  Soft evidential update for probabilistic multiagent systems , 2002, Int. J. Approx. Reason..

[12]  Ian Horrocks,et al.  Ontology Reasoning in the SHOQ(D) Description Logic , 2001, IJCAI.

[13]  Yang Xiang,et al.  PROBABILISTIC REASONING IN MULTIAGENT SYSTEMS: A GRAPHICAL MODELS APPROACH, by Yang Xiang, Cambridge University Press, Cambridge, 2002, xii + 294 pp., ISBN 0-521-81308-5 (Hardback, £45.00). , 2002, Robotica.

[14]  Phillip M. Yelland,et al.  Market Analysis Using a Combination of Bayesian Networks and Description Logics , 1999 .

[15]  Jochen Heinsohn,et al.  Probabilistic Description Logics , 1994, UAI.

[16]  Gramer Erhard.  PROBABILITY MEASURES WITH GIVEN MARGINALS AND CONDITIONALS: I-PROJECTIONS AND CONDITIONAL ITERATIVE PROPORTIONAL FITTING , 2000 .

[17]  Manfred Jaeger,et al.  Probabilistic Reasoning in Terminological Logics , 1994, KR.

[18]  Thomas Lukasiewicz,et al.  P-SHOQ(D): A Probabilistic Extension of SHOQ(D) for Probabilistic Ontologies in the Semantic Web , 2002, JELIA.

[19]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[20]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[21]  Jiri Vomlel.  Methods Of Probabilistic Knowledge Integration , 1999 .

[22]  Alon Y. Halevy,et al.  P-CLASSIC: A Tractable Probablistic Description Logic , 1997, AAAI/IAAI.

[23]  Yun Peng,et al.  Modifying Bayesian Networks by Probability Constraints , 2005, UAI.

[24]  Yun Peng,et al.  A Bayesian Approach to Uncertainty Modelling in OWL Ontology , 2006 .

[25]  Eero Hyvönen,et al.  Probabilistic Information Retrieval Based on Conceptual Overlap in Semantic Web Ontologies , 2004 .

[26]  Tom M. Mitchell,et al.  Learning to construct knowledge bases from the World Wide Web , 2000, Artif. Intell..

[27]  J. Pearl.  Jeffrey's Rule, Passage of Experience, and Neo-Bayesianism , 1990 .