Affiliations: Dipartimento di Informatica, Università di Bari, via E.
Orabona, 4 - 70125 Bari, Italia. {ferilli, basile, biba, ndm,
esposito}@di.uniba.it
Note: [] Address for correspondence: Dipartimento di Informatica,
Università di Bari, via E. Orabona, 4 - 70125 Bari, Italia
Abstract: First-Order Logic formulæ are a powerful representation formalism
characterized by the use of relations, that cause serious computational
problems due to the phenomenon of indeterminacy (various portions of one
description are possibly mapped in different ways onto another description).
Being able to identify the correct corresponding parts of two descriptions
would help to tackle the problem: hence, the need for a framework for the
comparison and similarity assessment. This could have many applications in
Artificial Intelligence: guiding subsumption procedures and theory revision
systems, implementing flexible matching, supporting instance-based learning and
conceptual clustering. Unfortunately, few works on this subject are available
in the literature. This paper focuses on Horn clauses, which are the basis for
the Logic Programming paradigm, and proposes a novel similarity formula and
evaluation criteria for identifying the descriptions components that are more
similar and hence more likely to correspond to each other, based only on their
syntactic structure. Experiments on real-world datasets prove the effectiveness
of the proposal, and the efficiency of the corresponding implementation in the
above tasks.
