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

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

A Framework for Reasoning with Expressive Continuous Fuzzy Description Logics

Abstract. In the current paper we study the reasoning problem for fuzzy SI (f-SI) under arbitrary continuous fuzzy operators. Our work can be seen as an extension of previous works that studied reasoning algorithms for f-SI, but focused on specific fuzzy operators, e.g. fKD-SI and of reasoning algorithms for less expressive fuzzy DLs, like fL-ALC and fP-ALC (fuzzy ALC under the Lukasiewicz and product fuzzy operators, respectively). We show how transitivity can be handled for all the range of continuous fuzzy DLs and discuss about blocking and correctness in this setting. Based on these analysis, we present a unifying framework for reasoning over the class of continuous fuzzy DLs. Finally use the results to prove decidability of several fuzzy SI DLs.

Hybrid Query Answering Over Expressive DL

The design of tractable Description Logics, like EL [1], DL-Lite [2], and RL [5] have spurred the design and implementation of highly scalable systems such as OWLim, Oracle’s Semantic Graph, and more. The attractive properties of the these systems have in many occasions led application developers to use the

Query Rewriting Under Query Extensions for OWL 2 QL Ontologies

Abstract. Conjunctive query answering is a key reasoning service for many ontology-based applications. With the advent of lightweight ontology languages, such as OWL 2 QL, several query answering systems have been proposed which compute the so called UCQ rewriting of a given query. It is often the case in realistic scenarios, that users refine their original queries, by e.g., extending them with new constraints and making them more precise. To the best of our knowledge, in such cases, all OWL 2 QL systems would need to recompute the rewriting of the refined query from scratch. In this paper we study the problem of computing the rewriting of the refined query by ‘extending ’ the pre-computed rewriting and avoiding re-computation. We study the problem from a theoretical point of view and present a practical algorithm. Finally, we evaluate our implementation experimentally by comparing it against many state-ofthe-art query rewriting systems, obtaining encouraging results.