SWARM: An Approach for Mining Semantic Association Rules from Semantic Web Data
SWARM: An Approach for Mining Semantic Association Rules from Semantic Web Data
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DOI：
10.1007/978-3-319-42911-3_3
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Molood Barati;Q. Bai;Qing Liu
中科院分区：
文献类型：
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作者：
Molood Barati;Q. Bai;Qing Liu
语义网数据量的不断增长使得分析用户所需的信息变得越来越困难。关联规则挖掘是发现RDF三元组中频繁模式的最有用的技术之一。在这种情况下，一些统计方法强烈依赖于用户干预，由于大量数据，这是耗时且容易出错的。在这些研究中，规则质量因子（例如支持度和置信度）仅考虑实例级数据中的知识。然而，语义Web数据包含了实例级和模式级的知识。在本文中，我们介绍了一种方法，称为SWARM（语义Web关联规则挖掘），自动挖掘语义关联规则的RDF数据。讨论了如何利用模式级的知识编码来丰富规则的语义。我们还表明，我们的方法是能够揭示常见的行为模式与知识的实例级和模式级。提出的规则质量因子（支持度和置信度）不仅考虑了实例级的知识，还考虑了模式级的知识。在DBpedia数据集（3.8）上进行的实验证明了所提出的方法的有效性。
The ever growing amount of Semantic Web data has made it increasingly difficult to analyse the information required by the users. Association rule mining is one of the most useful techniques for discovering frequent patterns among RDF triples. In this context, some statistical methods strongly rely on the user intervention that is time-consuming and error-prone due to a large amount of data. In these studies, the rule quality factors (e.g. Support and Confidence measures) consider only knowledge in the instance-level data. However, Semantic Web data contains knowledge in both instance-level and schema-level. In this paper, we introduce an approach called SWARM (Semantic Web Association Rule Mining) to automatically mine Semantic Association Rules from RDF data. We discuss how to utilize knowledge encode in the schema-level to enrich the semantics of rules. We also show that our approach is able to reveal common behavioral patterns associated with knowledge in the instance-level and schema-level. The proposed rule quality factors (Support and Confidence) consider knowledge not only in the instance-level but also schema-level. Experiments performed on the DBpedia Dataset (3.8) demonstrate the usefulness of the proposed approach.