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Semi-Automatic Trend Detection in Scholarly Repository Using Semantic Approach
Abstract:Currently WWW is the first solution for scholars in finding information. But, analyzing and interpreting this volume of information will lead to researchers overload in pursuing their research. Trend detection in scientific publication retrieval systems helps scholars to find relevant, new and popular special areas by visualizing the trend of input topic. However, there are few researches on trend detection in scientific corpora while their proposed models do not appear to be suitable. Previous works lack of an appropriate representation scheme for research topics. This paper describes a method that combines Semantic Web and ontology to support advance search functions such as trend detection in the context of scholarly Semantic Web system (SSWeb).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331505Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1247
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