The W3C Web Ontology Language (OWL) is a powerful knowledge representation
formalism at the basis of many semantic-centric applications. Since its
unrestricted usage makes reasoning undecidable already in case of very simple
tasks, expressive yet decidable fragments have been identified. Among them, we
focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart
from supporting all RDFS datatypes. Although popular Web resources - such as
DBpedia - fall in OWL 2 RL, only a few systems have been designed and
implemented for this fragment. None of them, however, fully satisfy all the
following desiderata: (i) being freely available and regularly maintained; (ii)
supporting query answering and SPARQL queries; (iii) properly applying the
sameAs property without adopting the unique name assumption; (iv) dealing with
concrete datatypes. To fill the gap, we present DaRLing, a freely available
Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In
particular, we describe its architecture, the rewriting strategies it
implements, and the result of an experimental evaluation that demonstrates its
practical applicability. This paper is under consideration in Theory and
Practice of Logic Programming (TPLP).

描述了 DaRLing 系统，它是一个 OWL 2 RL 本体推理的 Datalog 重写器，在 SPARQL 查询下具有实际适用性。

DaRLing: OWL 2 RL 本体推理下的 SPARQL 查询 Datalog 重写器

DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under  SPARQL queries

Rewriting is widely used to optimise owl:sameAs reasoning in materialisation
based OWL 2 RL systems. We investigate issues related to both the correctness
and efficiency of rewriting, and present an algorithm that guarantees
correctness, improves efficiency, and can be effectively parallelised. Our
evaluation shows that our approach can reduce reasoning times on practical data
sets by orders of magnitude.

本研究旨在解决重写技术在 OWL 2 RL 系统中优化 owl:sameAs 推理的正确性和效率问题。我们提出了一个可保证正确性、提高效率并可有效并行化的算法，并评估显示该方法可以将实际数据集上的推理时间大幅缩短。

通过重写处理 owl:sameAs

Handling owl:sameAs via Rewriting

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.

研究了将一个分离的 Datalog 程序重写为普通的 Datalog 的问题，并提出了一种新的基于规则的知识表示语言（KR language）—— 弱线性分离 Datalog，以及在本文的框架下对本体推理问题的应用，结果表明许多非 Horn 本体可以被简化为弱线性程序，并可以用 Datalog 引擎在实践中进行查询回答。