Cuifang Zhao

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As the explosive growth of online linked data, the task of mining link patterns attracts more and more attention. A practical issue is how to perform mining efficiently in large-scale linked data. Existing pattern mining algorithms usually assume that the dataset can fit into the main memory, while linked data of billion triples is far beyond the memory(More)
Ontology search engines facilitate the retrieval of web ontologies based on linguistic information. But the ability of searching the structure information in web ontologies hasn't been fully investigated. This paper proposes ONTRUSE, which is a novel search engine for searching ontological structures in massive web ontologies. We introduce CRSentence and(More)
Existing pattern mining algorithms typically assume that the dataset can fit into the main memory, while large graph datasets cannot satisfy this condition. Thus mining patterns in large-scale linked data is still a challenge. In this paper we propose a new partition-based approach for pattern mining in linked data which is composed of three steps: dividing(More)
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