Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?
This work addresses the efficiency trade-offs in ontology engineering for knowledge representation researchers, but it is incremental as it builds on existing locality-based module concepts.
The paper tackled the problem of extracting semantic locality-based modules (LBMs) from OWL ontologies, which was previously unimplemented due to intractable reasoning, and found that semantic LBMs offer only marginal improvements over syntactic LBMs in real-life ontologies, with extraction times being significantly higher (e.g., up to 100 times slower).
Extracting a subset of a given OWL ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules (LBMs). These come in two flavours, syntactic and semantic, and a syntactic LBM is known to contain the corresponding semantic LBM. For syntactic LBMs, polynomial extraction algorithms are known, implemented in the OWL API, and being used. In contrast, extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL, and these algorithms had not been implemented yet for expressive ontology languages. We present the first implementation of semantic LBMs and report on experiments that compare them with syntactic LBMs extracted from real-life ontologies. Our study reveals whether semantic LBMs are worth the additional extraction effort, compared with syntactic LBMs.