Plugging Taxonomic Similarity in First-Order Logic Horn Clauses Comparison

Abstract

Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a taxonomic background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation predicates are not purposely engineered but rather derive from the particular words found in a text.

This work proposes the exploitation of a taxonomic background knowledge to better assess the similarity between two First-Order Logic (Horn clause) descriptions, beyond the simple syntactical matching between predicates. To this aim, an existing distance framework is extended by applying the underlying distance measure also to parameters coming from the taxonomic background knowledge. The viability of the solution is demonstrated on sample problems.