Hybrid Query Answering Over OWL Ontologies

Abstract. Query answering over OWL 2 DL ontologies is an important reasoning task for many modern applications. Unfortunately, due to its high computational complexity, OWL 2 DL systems are still not able to cope with datasets containing billions of data. Consequently, application developers often employ provably scalable systems which only support a fragment of OWL 2 DL and which are, hence, most likely incomplete for the given input. However, this notion of completeness is too coarse since it implies that there exists some query and some dataset for which these systems would miss answers. Nevertheless, there might still be a large number of user queries for which they can compute all the right answers even over OWL 2 DL ontologies. In the current paper, we investigate whether, given a query Q with only distinguished variables over an OWL 2 DL ontology T and a system ans, it is possible to identify in an efficient way if ans is complete for Q, T and every dataset. We give sufficient conditions for (in)completeness and present a hybrid query answering algorithm which uses ans when it is complete, otherwise it falls back to a fully-fledged OWL 2 DL reasoner. However, even in the latter case, our algorithm still exploits ans as much as possible in order to reduce the search space of the OWL 2 DL reasoner. Finally, we have implemented our approach using a concrete system ans and OWL 2 DL reasoner obtaining encouraging results.

Benchmarking ontologybased query rewriting systems

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates “relevant ” test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of “inferences”, each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system

Reasoning with Very Expressive Fuzzy Description Logics

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN

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

Abstract. In previous work it has been shown how an OWL 2 DL on-tology 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 com-plex 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 rst prototype. Sec-ond, we show how (arbitrary) conjunctive queries can be supported by integrating well-known query rewriting techniques with OWL 2 RL sys-tems via repairing. Third, we perform an extensive experimental evalua-tion obtaining encouraging results. In more detail, our results show that we can compute repairs even for very large real-world ontologies in a rea-sonable 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 eciently compute the correct answers for real-world challenging scenarios.

Description of alignment implementation and benchmarking results

stuckenschmidt2005aThis deliverable presents the evaluation campaign carried out in 2005 and the improvement participants to these campaign and others have to their systems. We draw lessons from this work and proposes improvements for future campaigns