A Partial Materialization-Based Approach to Scalable Query Answering in OWL 2 DL

Abstract

This paper focuses on the efficient ontology-mediated querying (OMQ) problem. Compared with query answering in plain databases, which deals with fixed finite database instances, a key challenge in OMQ is to deal with the possibly infinite large set of consequences entailed by the ontology, i.e., the so-called chase. Existing techniques mostly avoid materializing the chase by query rewriting to address this issue, which, however, comes at the cost of query rewriting and query evaluation at runtime, and the possibility of missing optimization opportunity at the data level. Instead, pure materialization technology is adopted in this paper. The query-rewriting is unnecessary at materialization. A query analysis algorithm (QAA) is proposed for ensuring the completeness and soundness of OMQ over partial materialization for rooted queries in $\textit{DL-Lite}^{\mathcal {N}}_{ horn }$. We also soundly and incompletely expand our method to deal with OWL 2 DL. Finally, we implement our approach as a prototype system SUMA by integrating off-the-shelf efficient SPARQL query engines. The experiments show that SUMA is complete on each test ontology and each test query, which is the same as Pellet and outperforms PAGOdA. In addition, SUMA is highly scalable on large data sets.