Affiliations: [a] School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China | [b] Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, Jiangsu, China | [c] School of Modern Posts and Institute of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Huan Gao, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China. E-mail: [email protected].
Abstract: With the development of the Semantic Web, more and more semantic data including many useful knowledge bases has been published on the Web. Such knowledge bases always lack expressive schema information, especially disjointness axioms and subclass axioms. This makes it difficult to perform many critical Semantic Web tasks like ontology reasoning, inconsistency handling and ontology mapping. To deal with this problem, a few approaches have been proposed to generate terminology axioms. However, they often adopt the closed world assumption which is opposite to the assumption adopted by the semantic data. This may lead to a lot of noisy negative examples so that existing learning approaches fail to perform well on such incomplete data. In this paper, a novel framework is proposed to automatically obtain disjointness axioms and subclass axioms from incomplete semantic data. This framework first obtains probabilistic type assertions by exploiting a type inference algorithm. Then a mining approach based on association rule mining is proposed to learn high-quality schema information. To address the incompleteness problem of semantic data, the mining model introduces novel definitions to compute the support and confidence for pruning false axioms. Our experimental evaluation shows promising results over several real-life incomplete knowledge bases like DBpedia and LUBM by comparing with existing relevant approaches.
Keywords: Ontology learning, knowledge bases, open world assumption, association rule mining, semantic web
