A. Pipitone, R. Pirrone

A Hidden Markov Model for Automatic Generation of ER Diagrams from OWL Ontology

Natural Language Processing Artificial Intelligence

Connecting ontological representations and data models is a crucial need in enterprise knowledge management, above all in the case of federated enterprises where corporate ontologies are used to share information coming from different databases. OWL to ERD transformations are a challenging research field in this scenario, due to the loss of expressiveness arising when OWL axioms have to be represented using ERD notation. In this paper we propose an innovative technique for estimating the most likely composition of ERD constructs that correspond to a given sequence of OWL axioms. We model such a process using a Hidden Markov Model (HMM) where the OWL inputs are the observable states, while ERD structures are the hidden states. Transition and emission probabilities have been set up heuristically through the analysis of a purposely defined grammar describing the ERD syntax, and all the OWL/ERD mapping rules presented in the literature. The theoretical model is explained in detail, a case study is exploited, and the experimental results are presented.

This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE