PAGOdA: Pay-as-you-go ABox Reasoning
PAGOdA: Pay-as-you-go ABox Reasoning
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PAGOdA：即用即付 ABox Reasoning
DOI：
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Ian Horrocks
中科院分区：
文献类型：
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作者：
Yujiao Zhou;B. C. Grau;Yavor Nenov;Ian Horrocks
本体越来越多地用于提供结构和增强对大型数据集的访问。在这样的应用中，本体可以看作是一个TBox，数据集可以看作是一个ABox，关键的推理问题是联合查询（CQ）回答。不幸的是，对于owl2来说，这个问题已知具有很高的最坏情况复杂性，即使复杂性是根据数据的大小来衡量的，并且在实际设置中数据集可能非常大。解决这个问题的一种方法是将本体限制为具有更好计算属性的片段，这是owl2概要文件背后的动机。另一种方法是优化任意owl2本体的推理。后一种方法被证明对于TBox推理非常成功，像Konclude， HermiT， Pellet和Racer这样的系统被广泛用于对大规模本体进行推理。然而，到目前为止，在实践中，对大型box的推理在很大程度上被限制在owl2配置文件中。在本文中，我们描述了PAGOdA：一个高度优化的推理系统，它支持针对任意owl2本体和RDF数据集（大致相当于SROIQ TBox和ABox）的CQ应答。它使用一种新颖的方法来查询答案，将数据表（或owl2 RL）推理器（目前为RDFox b[11]）与成熟的owl2推理器（目前为HermiT[5]）结合在一起，提供可扩展的性能，同时仍然保证声音和完整的答案。PAGOdA将大量的计算工作负载委托给数据推理器，在多大程度上需要完全成熟的推理器取决于本体、数据集和查询之间的交互。这种方法是“随用随付”的，因为每当输入本体在任何owl2概要文件中表示时，查询回答都完全委托给数据推理器；此外，即使在使用概要外本体时，查询通常也可以仅使用数据推理器完全回答；即使需要完全成熟的推理器，PAGOdA也会采用一系列优化，包括相关子集提取、总结和依赖分析，以减少相关推理问题的数量和大小。这种方法在实践中被证明是非常有效的：在我们对8个本体的4000多个查询进行的测试中，其中没有一个包含在任何OWL配置文件中，超过99%的查询没有得到完全回答
Ontologies are increasingly used to provide structure and enhance access to large datasets. In such applications the ontology can be seen as a TBox, and the dataset as an ABox, with the key reasoning problem being conjunctive query (CQ) answering. Unfortunately, for OWL 2 this problem is known to be of high worst case complexity, even when complexity is measured with respect to the size of the data, and in realistic settings datasets may be very large. One way to address this issue is to restrict the ontology to a fragment with better computational properties, and this is the motivation behind the OWL 2 profiles. Another approach is to optimise reasoning for arbitrary OWL 2 ontologies. This latter approach has proved very successful for TBox reasoning, with systems such as Konclude, HermiT, Pellet and Racer being widely used to reason over large-scale ontologies. Up to now, however, reasoning with large ABoxes has, in practice, largely been restricted to the OWL 2 profiles. In this paper we describe PAGOdA: a highly optimised reasoning system that supports CQ answering with respect to an arbitrary OWL 2 ontology and an RDF dataset (roughly equivalent to a SROIQ TBox and ABox). It uses a novel approach to query answering that combines a datalog (or OWL 2 RL) reasoner (currently RDFox [11]) with a fully-fledged OWL 2 reasoner (currently HermiT [5]) to provide scalable performance while still guaranteeing sound and complete answers. PAGOdA delegates the bulk of the computational workload to the datalog reasoner, with the extent to which the fully-fledged reasoner is needed depending on interactions between the ontology, the dataset and the query. This approach is ‘pay-as-you-go’ in the sense that query answering is fully delegated to the datalog reasoner whenever the input ontology is expressed in any of the OWL 2 profies; furthermore, even when using an out-of-profile ontology, queries can often be fully answered using only the datalog reasoner; and even when the fully-fledged reasoner is required, PAGOdA employs a range of optimisations, including relevant subset extraction, summarisation and dependency analysis, to reduce the number and size of the relevant reasoning problems. This approach has proved to be very effective in practice: in our tests of more than 4,000 queries over 8 ontologies, none of which is contained within any of the OWL profiles, more than 99% of queries were fully answered without
DOI：
10.1007/s10817-014-9305-1
发表时间：
2014-10-01
期刊：
JOURNAL OF AUTOMATED REASONING
影响因子：
--
作者：
Glimm, Birte;Horrocks, Ian;Wang, Zhe
通讯作者：
Wang, Zhe
影响因子：
5
作者：
Motik, Boris;Shearer, Rob;Horrocks, Ian
通讯作者：
Horrocks, Ian