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Bossam: An Extended Rule Engine for OWL Inferencing

机译:Bossam:用于猫头鹰推理的扩展规则引擎

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In this paper, we describe our effort to build an inference engine for OWL reasoning based on the rule engine paradigm. Rule engines are very practical and effective for their representational simplicity and optimized performance, but their limited expressiveness and web unfriendliness restrict their usability for OWL reasoning. We enumerate and succinctly describe extended features implemented in our rule engine, Bossam, and show that these features are necessary to promote the effectiveness of any ordinary rule engine's OWL reasoning capability. URI referencing and URI-based procedural attachment enhance web-friendliness. OWL importing, support for classical negation and relieved range restrictedness help correctly capture the semantics of OWL. Remote binding enables collaborated reasoning among multiple Bossam engines, which enhances the engine's usability on the distributed semantic web environment. By applying our engine to the W3C's OWL test cases, we got a plausible 70% average success rate for the three OWL species. Our contribution with this paper is to suggest a set of extended features that can enhance the reasoning capabilities of ordinary rule engines on the semantic web.
机译:在本文中,我们描述了根据规则引擎范例为猫头鹰推理构建推理引擎的努力。规则发动机对其代表性的简单性和优化的性能非常实用,有效,但它们有限的表现力和网络不友好限制了他们对猫头鹰推理的可用性。我们枚举和简洁地描述我们的规则引擎,Bossam中实现的扩展功能,并表明这些功能是促进任何普通规则引擎的猫头鹰推理能力的有效性。 URI引用和基于URI的程序附件增强了Web友好性。 owl导入,支持古典否定和缓解范围限制有助于正确捕获猫头鹰的语义。远程绑定使多个Bossam发动机之间的合作推理能够增强发动机对分布式语义Web环境的可用性。通过将我们的发动机应用于W3C的猫头鹰测试案例,我们为三个猫头鹰种类进行了70%的平均成功率。我们本文的贡献是建议一组扩展功能,可以增强在语义网络上的普通规则发动机的推理能力。

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