Toward Efficient Semantic Annotation: A Semantic Cloud Generation Scheme from Linked Data
As a bridge for evolution of the current Web toward Semantic Web, semantic annotation plays an important role to turn regular Web contents into meaningful ones. However, existing semantic annotation methods mostly use semantic terms in ontology created by domain experts. Therefore, they cannot cover the various subjects of contents, some of which frequently change. To deal with this problem by alternating ontology to Linked Data, we propose a semantic cloud generation scheme that finds and merges relevant terms from Linked Data for a given request. To reduce the complexity of handling a large amount of RDF data, we locate essential points at which to start searching for relevant concepts in Linked Data; we then iteratively analyze potential merges of different semantic data. In this paper, we describe the challenges of forming semantic clouds out of Linked Data and the approach of effectively generating semantic clouds by using the similarity link analysis method.
KeywordsSimilarity link analysis Semantic cloud generation Linked Data Semantic annotation Semantic Web
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