Redundancy-free Verbalization of Individuals for Ontology Validation
This work addresses the challenge of improving ontology validation for domain experts by generating clearer verbalizations, though it is incremental as it builds on existing verbalization methods.
The paper tackles the problem of generating redundancy-free natural language descriptions from OWL ontology axioms, specifically for individuals in SHIQ-based ontologies, by applying semantic reduction at the logical level to remove redundancies, with experiments showing it helps produce more meaningful and human-understandable texts.
We investigate the problem of verbalizing Web Ontology Language (OWL) axioms of domain ontologies in this paper. The existing approaches address the problem of fidelity of verbalized OWL texts to OWL semantics by exploring different ways of expressing the same OWL axiom in various linguistic forms. They also perform grouping and aggregating of the natural language (NL) sentences that are generated corresponding to each OWL statement into a comprehensible structure. However, no efforts have been taken to try out a semantic reduction at logical level to remove redundancies and repetitions, so that the reduced set of axioms can be used for generating a more meaningful and human-understandable (what we call redundancy-free) text. Our experiments show that, formal semantic reduction at logical level is very helpful to generate redundancy-free descriptions of ontology entities. In this paper, we particularly focus on generating descriptions of individuals of SHIQ based ontologies. The details of a case study are provided to support the usefulness of the redundancy-free NL descriptions of individuals, in knowledge validation application.