Abstract
Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSCs that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL , allowing it to generate much simpler and smaller concepts that are specific enough to answer a given query. With independence between computed MSCs, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries.
Highlights
Description logics (DLs) play an ever growing role in providing a formal and semantic-rich way to model andHow to cite this paper: Xu, J., Shironoshita, P., Visser, U., John, N. and Kabuka, M. (2015) Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies
This strategy makes our most specific concept (MSC) method suitable for query answering in ontologies, where frequent modifications to the ontology data are not uncommon; 2) We propose optimizations that can be used to further reduce sizes of computed concepts in practical ontologies for more efficient instance checking; 3) we evaluate our approach on a range of test ontologies with large ABoxes, including ones generated by existing benchmark tools and realistic ones used in biomedical research
The rest of the paper is organized as follows: in Section 2, we introduce the background knowledge of a description logic and DL ontology; in Section 3, we give more detailed discussion about the MSC method and our call-by-need strategy; Section 4 presents the technical details of the revised MSC method; Section 5 discusses the related work; Section 6 presents an empirical evaluation on our proposed method; and Section 7 concludes our work
Summary
Description logics (DLs) play an ever growing role in providing a formal and semantic-rich way to model and. Our contributions in this paper are summarized as follows: 1) We propose a call-by-need strategy for the original MSC method, instead of computing the most specific concepts offline to handle any given query, which allows us to focus on the current queries and to generate online much smaller concepts that are sufficient to compute the answers This strategy makes our MSC method suitable for query answering in ontologies, where frequent modifications to the ontology data are not uncommon; 2) We propose optimizations that can be used to further reduce sizes of computed concepts in practical ontologies for more efficient instance checking; 3) we evaluate our approach on a range of test ontologies with large ABoxes, including ones generated by existing benchmark tools and realistic ones used in biomedical research. The rest of the paper is organized as follows: in Section 2, we introduce the background knowledge of a description logic and DL ontology; in Section 3, we give more detailed discussion about the MSC method and our call-by-need strategy; Section 4 presents the technical details of the revised MSC method; Section 5 discusses the related work; Section 6 presents an empirical evaluation on our proposed method; and Section 7 concludes our work
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