Ontology Module Extraction via Datalog Reasoning
Ontology Module Extraction via Datalog Reasoning
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DOI：
10.1609/aaai.v29i1.9418
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
A. A. Romero-A.;M. Kaminski;B. C. Grau;Ian Horrocks
中科院分区：
文献类型：
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作者：
A. A. Romero-A.;M. Kaminski;B. C. Grau;Ian Horrocks
模块提取-计算本体T的（优选地小的）片段M的任务，其保留签名S上的蕴涵-近年来已经发现了许多应用。然而，提取最小尺寸的模块在计算上是困难的，并且通常在算法上是不可行的。因此，实用的技术是基于近似，其中M可证明地捕获相关蕴涵，但不能保证是最小的。现有的近似，但是，确保M保持所有的二阶蕴涵的T w.r.t. S，它比许多应用中所需的更强，并且在实践中可能导致大模块。在本文中，我们提出了一种新的方法，其中模块提取减少到一个推理问题的数据库。我们的方法不仅以优雅的方式概括了现有的近似，而且还可以定制为仅保留特定类型的蕴涵，这使我们能够提取更小的模块。对广泛使用的本体的评估显示出非常令人鼓舞的结果。
Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.