Discovering conceptual relations from text

TLDR

Non-taxonomic conceptual relations are a key component of ontologies, and defining them consumes a large portion of ontology engineering time. The study proposes a new method to automatically discover non-taxonomic conceptual relations from text, aiming to produce succinct, maintainable ontologies. The approach employs a generalized association rule algorithm to detect relations and infer appropriate abstraction levels, and it is evaluated against a manually engineered ontology using a novel matching paradigm.

Abstract

Non-taxonomic relations between concepts appear as a major building block in common ontology definitions. In fact, their definition consumes much of the time needed for engineering an ontology. We here describe a new approach to discover non-taxonomic conceptual relations from text building on shallow text processing techniques. We use a generalized association rule algorithm that does not only detect relations between concepts, but also determines the appropriate level of abstraction at which to define relations. This is crucial for an appropriate ontology definition in order that it be succinct and conceptually adequate and, hence, easy to understand, maintain, and extend. We also perform an empirical evaluation of our approach with regard to a manually engineered ontology. For this purpose, we present a new paradigm suited to evaluate the degree to which relations that are learned match relations in a manually engineered ontology.