A. Pipitone, F. Anastasio, R. Pirrone

HOWERD: A Hidden Markov Model for Automatic OWL-ERD Alignment

Natural Language Processing Artificial Intelligence

The HOWERD model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD) is presented. Automatic alignment between relational schema and ontology represents a big challenge in Semantic Web research due to the different expressiveness of these representations. A relational schema is less expressive than the ontology, this is a non trivial problem when accessing data via an ontology and for ontology storing by means of a relational schema. Existent alignment methodologies fail in loosing some contents of the involved representations because the ontology captures more semantic information, and several elements are left unaligned. HOWERD relies on a Hidden Markov Model (HMM) to estimate the most likely sequence of ERD symbols in a relational schema that correspond to the constructs of an OWL axiom in the ontology to be aligned. Such constructs are the observable states in the HMM, while hidden states are modeled as the symbols of a context free grammar defined purposely for describing the input ERD lexically.

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