Efficient OWL2QL Meta-reasoning Using ASP-based Hybrid Knowledge Bases
This work addresses the problem of efficient meta-reasoning for applications that require metamodeling, which is an incremental improvement in the field of knowledge representation.
This work tackles the problem of efficient OWL2QL meta-reasoning by improving the theoretical basis of reductions and using alternative tools, resulting in competitive performance. The approach builds on earlier work that reduces metamodeling query answering to Datalog query answering.
Metamodeling refers to scenarios in ontologies in which classes and roles can be members of classes or occur in roles. This is a desirable modelling feature in several applications, but allowing it without restrictions is problematic for several reasons, mainly because it causes undecidability. Therefore, practical languages either forbid metamodeling explicitly or treat occurrences of classes as instances to be semantically different from other occurrences, thereby not allowing metamodeling semantically. Several extensions have been proposed to provide metamodeling to some extent. Building on earlier work that reduces metamodeling query answering to Datalog query answering, recently reductions to query answering over hybrid knowledge bases were proposed with the aim of using the Datalog transformation only where necessary. Preliminary work showed that the approach works, but the hoped-for performance improvements were not observed yet. In this work we expand on this body of work by improving the theoretical basis of the reductions and by using alternative tools that show competitive performance.