| Abstract |
Ontology-based access to knowledge graphs (KGs) has recently gained a lot of attention. One of the research
challenges when accessing these large data structures is to enable "the capability of combining diverse reasoning methods
and knowledge representations while guaranteeing the required scalability, according to the reasoning task at hand." [1]
In our work, we address this challenge with a focus on reasoning with KGs extended with Description Logics (DL) ontologies.
In principle, one could make use of existing DL reasoners to solve these reasoning tasks. However, DL reasoners---which are
designed to deal with complex terminological axioms---do not scale well in the presence of large amounts of assertional
information. In contrast, existing rule engines such as VLog or RDFOx can efficiently reason with data-intensive knowledge bases.
To take advantage of these powerful implementations, we propose several data-independent mappings from DL TBoxes into rule
sets that preserve the outcomes of conjunctive query (CQ) answering. Our experiments indicate that reasoning with rule engines
over the resulting CQ-preserving rewritings can be significantly more efficient than using state-of-the-art DL reasoners over the
original DL ontologies.
[1] This quote is taken from the description of a recent Daghstul seminar on "Knowledge Graphs: New Directions for Knowledge
Representation on the Semantic Web" (https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18371) |