Dynamic Weight-Based Individual Similarity Calculation for Information Searching in Social Computing
Abstract— Dynamic Weight-Based Individual Similarity Calculation for Information Searching in Social Computing. In the social computing environment, the complete information about an individual is usually distributed in heterogeneous social networks, which are presented as linked data. Synthetically recognizing and integrating these distributed and heterogeneous data for efficiently information searching is an important but challenging work. In this paper, a dynamic weight < Final Year Projects 2016 > based similarity calculation is proposed to recognize and integrate similar individuals from distributed data environments. First, each link of an individual is weighted by applying DW. Then, a semantic similarity metric is proposed to combine the DW into similarity calculation. Then, a searching system framework for a similarity-based individual is designed and tested in real-world data sets.
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