Abstract
The large amounts of linked data are a valuable resource for the development of semantic applications. However, these applications often meet the challenges posed by flawed or incomplete schema, which would lead to the loss of meaningful facts. Association rule mining has been applied to learn many types of axioms. In this paper, we first use a statistical approach based on the association rule mining to enrich OWL ontologies. Then we propose some improvements according to this approach. Finally, we describe the quality of the acquired axioms by evaluations on DBpedia datasets.
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Acknowledgments
The work is supported by the Natural Science Foundation of Jiangsu Province under Grant BK20140643 and the National Natural Science Foundation of China under grant No. 61502095.
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Li, Y., Li, H., Shi, J. (2016). Mining RDF Data for OWL2 RL Axioms. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_12
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DOI: https://doi.org/10.1007/978-981-10-3168-7_12
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