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mismatch problem and match irrelevance problem and fail to generate highly related results. To overcome these problems, we propose a novel approach to recommend articles to the researchers. In our approach we integrate three types of similarity measures: keyword similarity, journal similarity, and author similarity to measure
keyword. This graph is built using a carefully selected one-parameter set of keywords. By varying this parameter - the level of meaningfulness - we transition the document-representing graph from a trivial path graph into a large random graph. During such a conversion, as the parameter is varied over its range, the graph
importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for
activities, such as identification of growing researchers and supervisors. In previous paper we proposed a visualization system for co-authorship networks, which provides the function for identifying research areas and that for identifying temporal variation of both network structure and keyword distribution. This paper
' phrase in their title or keywords or abstracts and retrieve totally 4,579 publications. Spearman correlation rank test is used to examine the hypotheses. Analysis of collected data shows that publication's impact is significantly and positively associated with collaboration indicators based on authors' affiliations. However
materials to their final concept maps than in 2006. In the activity of making relations, the learners in 2007 used their own common concepts or keywords which were made at the midterm when they relate the learning materials. These results suggest that supporting constructing the learners' own integrated knowledge helped them
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
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