Consistent Knowledge Discovery from Evolving Ontologies
Consistent Knowledge Discovery from Evolving Ontologies
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DOI：
10.1609/aaai.v29i1.9175
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
F. Lécué;Jeff Z. Pan
中科院分区：
文献类型：
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作者：
F. Lécué;Jeff Z. Pan
演绎推理和归纳学习是获取知识最常用的方法。在现实世界的应用中，当数据是动态和不完整的，特别是那些由传感器暴露的数据时，推理受到数据动态的限制，而学习则受到数据不完整的偏见。因此，从不完整和动态的数据中发现一致的知识是一个具有挑战性的开放问题。在我们的方法中，通过本体捕获数据的语义，从而通过（描述逻辑）推理来增强学习（挖掘）能力。一致性知识发现是通过应用通用的、有意义的、有代表性的关联语义规则来实现的。实验表明，都柏林数据的知识发现具有可扩展性、准确性和一致性。
Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.