Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning

We study the problem of rewriting a disjunctive datalog program into plain datalog. We show that a disjunctive program is rewritable if and only if it is equivalent to a linear disjunctive program, thus providing a novel characterisation of datalog rewritability. Motivated by this result, we propose weakly linear disjunctive datalog---a novel rule-based KR language that extends both datalog and linear disjunctive datalog and for which reasoning is tractable in data complexity. We then explore applications of weakly linear programs to ontology reasoning and propose a tractable extension of OWL 2 RL with disjunctive axioms. Our empirical results suggest that many non-Horn ontologies can be reduced to weakly linear programs and that query answering over such ontologies using a datalog engine is feasible in practice.Comment: 14 pages. To appear at AAAI-1

Combining Rewriting and Incremental Materialisation Maintenance for Datalog Programs with Equality

Materialisation precomputes all consequences of a set of facts and a datalog program so that queries can be evaluated directly (i.e., independently from the program). Rewriting optimises materialisation for datalog programs with equality by replacing all equal constants with a single representative; and incremental maintenance algorithms can efficiently update a materialisation for small changes in the input facts. Both techniques are critical to practical applicability of datalog systems; however, we are unaware of an approach that combines rewriting and incremental maintenance. In this paper we present the first such combination, and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation.Comment: All proofs contained in the appendix. 7 pages + 4 pages appendix. 7 algorithms and one table with evaluation result

On the Decidability of Connectedness Constraints in 2D and 3D Euclidean Spaces

We investigate (quantifier-free) spatial constraint languages with equality, contact and connectedness predicates as well as Boolean operations on regions, interpreted over low-dimensional Euclidean spaces. We show that the complexity of reasoning varies dramatically depending on the dimension of the space and on the type of regions considered. For example, the logic with the interior-connectedness predicate (and without contact) is undecidable over polygons or regular closed sets in the Euclidean plane, NP-complete over regular closed sets in three-dimensional Euclidean space, and ExpTime-complete over polyhedra in three-dimensional Euclidean space.Comment: Accepted for publication in the IJCAI 2011 proceeding

Optimised Storage for Datalog Reasoning

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.Comment: 19 page

日本経団連が国家エネルギー戦略確立を提言

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are often used in industry to capture the conceptual information models underpinning applications. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance