C2Ont: An OWL Ontology Learning Approach from Apache Cassandra

N. Soe, Tin Tin Yee, Ei Chaw Htoon
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引用次数: 1

Abstract

Big Data is a massive volume of both unstructured and structured data. It is crucial to efficiently represent big data as knowledge for data management. Ontologies provide knowledge as a formal description of a domain of interest. Therefore, the ontology learning approach is proposed for Apache Cassandra. It is composed of six mapping rules and converts OWL ontology from data in Cassandra by applying these mapping rules. NorthWind dataset is applied for demonstrating how to learn ontology from data in Cassandra. The evaluation result indicates that our approach can learn ontology in covering terminologically the modeled domain since the adequacy of extracted ontology is greater than 15%.
基于Apache Cassandra的OWL本体学习方法
大数据是大量的非结构化和结构化数据。有效地将大数据表示为数据管理的知识是至关重要的。本体作为感兴趣的领域的正式描述提供知识。因此,针对Apache Cassandra提出了本体学习方法。它由6个映射规则组成,并通过应用这些映射规则将OWL本体从Cassandra中的数据转换过来。应用NorthWind数据集演示了如何在Cassandra中从数据中学习本体。评价结果表明,我们的方法可以在术语上覆盖建模领域学习本体,提取的本体充分性大于15%。
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