34 research outputs found
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