Knowledge Discovery from Linked Data
نویسندگان
چکیده
Linked Data has been increasing rapidly by publishing machine readable structured data. DBpedia and YAGO are cross-domain data sets, which provide semantic knowledge of things. Although both data sets contain millions of entities, there are still missing knowledge exist in each data set. In this paper, we analyze graph patterns of Linked Data entities to discover missing knowledge in the data sets. We apply word embedding method with traditional ontology matching method to integrate heterogeneous ontologies. By querying Linked Data with integrated ontology, we can discover missing knowledge in the data sets so that we can automatically extend the Linked Data.
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