Complete Query Answering over Horn Ontologies Using a Triple Store

Zhou, Yonghui; Nenov, Yavor; Grau, Bernardo Cuenca; Horrocks, Ian

doi:10.1007/978-3-642-41335-3_45

Cited by 8 publications

(10 citation statements)

References 21 publications

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“…This is an extended version of our conference publications (Zhou, Nenov, Cuenca Grau, & Horrocks, 2014;Zhou, Nenov, Grau, & Horrocks, 2013). This work has been supported by the Royal Society under a Royal Society Research Fellowship, by the EPSRC projects Score!, MaSI 3 , and DBOnto, as well as by the EU FP7 project Optique.…”

Section: Acknowledgmentsmentioning

confidence: 99%

PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner

Zhou¹,

Grau²,

Nenov³

et al. 2015

jair

Self Cite

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Answering conjunctive queries over ontology-enriched datasets is a core reasoning task for many applications. Query answering is, however, computationally very expensive, which has led to the development of query answering procedures that sacrifice either expressive power of the ontology language, or the completeness of query answers in order to improve scalability. In this paper, we describe a hybrid approach to query answering over OWL 2 ontologies that combines a datalog reasoner with a fully-fledged OWL 2 reasoner in order to provide scalable `pay-as-you-go' performance. The key feature of our approach is that it delegates the bulk of the computation to the datalog reasoner and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. Furthermore, although our main goal is to efficiently answer queries over OWL 2 ontologies and data, our technical results are very general and our approach is applicable to first-order knowledge representation languages that can be captured by rules allowing for existential quantification and disjunction in the head; our only assumption is the availability of a datalog reasoner and a fully-fledged reasoner for the language of interest, both of which are used as `black boxes'. We have implemented our techniques in the PAGOdA system, which combines the datalog reasoner RDFox and the OWL 2 reasoner HermiT. Our extensive evaluation shows that PAGOdA succeeds in providing scalable pay-as-you-go query answering for a wide range of OWL 2 ontologies, datasets and queries.

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Section: Acknowledgmentsmentioning

confidence: 99%

PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner

Zhou¹,

Grau²,

Nenov³

et al. 2015

jair

Self Cite

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show abstract

“…All in all, computing a non-optimal repair, loading it into OWLim together with the original Fly TBox and ABox, loading Fly on Rapid and, finally, computing the answers for all 5 queries required a total of 233.2 seconds (computing the repair required 178 seconds, loading into OWLim and Rapid around 48.2 seconds and computing and evaluating all rewritings over OWLim around 7 seconds). In contrast, as mentioned in [21,22], over a much faster machine than the one used here, HermiT requires several hours to compute the answers, while the approach proposed in [21,22] requires 657 seconds to pre-process the Fly ontology and an average of 117 seconds per query to compute the answers.…”

Section: Evaluating Hybrid Query Answeringmentioning

confidence: 99%

Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing

Stoilos

2014

Lecture Notes in Computer Science

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Abstract. In previous work it has been shown how an OWL 2 DL ontology O can be 'repaired' for an OWL 2 RL system ans-that is, how we can compute a set of axioms R that is independent from the data and such that ans that is generally incomplete for O becomes complete for all SPARQL queries when used with O ∪ R. However, the initial implementation and experiments were very preliminary and hence it is currently unclear whether the approach can be applied to large and complex ontologies. Moreover, the approach so far can only support instance queries. In the current paper we thoroughly investigate repairing as an approach to scalable (and complete) ontology-based data access. First, we present several non-trivial optimisations to the first prototype. Second, we show how (arbitrary) conjunctive queries can be supported by integrating well-known query rewriting techniques with OWL 2 RL systems via repairing. Third, we perform an extensive experimental evaluation obtaining encouraging results. In more detail, our results show that we can compute repairs even for very large real-world ontologies in a reasonable amount of time, that the performance overhead introduced by repairing is negligible in small to medium sized ontologies and noticeable but manageable in large and complex one, and that the hybrid reasoning approach can very efficiently compute the correct answers for real-world challenging scenarios.

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“…Clearly, in such cases these systems would most likely be incomplete-that is, for some user query and dataset they will fail to compute all certain answers. However, techniques that attempt to deliver complete query answering even when using scalable systems that are not complete for OWL 2 DL have also been proposed [8,9].…”

Section: Introductionmentioning

confidence: 99%

Hydrowl: A Hybrid Query Answering System for OWL 2 DL Ontologies

Stoilos

2014

Web Reasoning and Rule Systems

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Abstract. This system description paper introduces the OWL 2 query answering system Hydrowl. Hydrowl is based on novel hybrid techniques which in order to compute the query answers combine at run-time a reasoner ans1 supporting a (tractable) fragment of OWL 2 (e.g., OWL 2 QL and OWL 2 RL) with a fully-fledged OWL 2 DL reasoner ans2. The motivation is that if most of the (query answering) work is delegated to the (usually) very scalable system ans1 while the interaction with ans2 is kept to a bare minimum, then we can possibly provide with scalable query answering even over expressive fragments of OWL 2 DL. We discuss the system's architecture and we present an overview of the techniques used. Finally, we present some first encouraging experimental results.

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Complete Query Answering over Horn Ontologies Using a Triple Store

Cited by 8 publications

References 21 publications

PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner

PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner

Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing

Hydrowl: A Hybrid Query Answering System for OWL 2 DL Ontologies

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