Inexact Matching of Ontology Graphs Using Expectation-Maximization

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 21

Special Track on Artificial Intelligence and the Web

We present a new method for mapping ontology schemas that address similar domains. The problem of ontology mapping is crucial since we are witnessing a decentralized development and publication of ontological data. We formulate the problem of inferring a match between two ontologies as a maximum likelihood problem, and solve it using the technique of expectation-maximization (EM). Specifically, we adopt directed graphs as our model for ontologies and use a generalized version of EM to arrive at a mapping between the nodes of the graphs. We exploit the structural and lexical similarity between the graphs, and improve on previous approaches by generating a many-one correspondence between the concept nodes. We provide preliminary experimental results in support of our method and outline its limitations.