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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Measures of Semantic Similarity of Nodes in a Social Network
verfasst von : Ahmad Rawashdeh, Mohammad Rawashdeh, Irene Díaz, Anca Ralescu
Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Verlag: Springer International Publishing
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Assessing the similarity between node profiles in a social network is an important tool in its analysis. Several approaches exist to study profile similarity, including semantic approaches and natural language processing. However, to date there is no research combining these aspects into a unified measure of profile similarity. Traditionally, semantic similarity is assessed using keywords, that is, formatted text information, with no natural language processing component. This study proposes an alternative approach, whereby the similarity assessment based on keywords is applied to the output of natural language processing of profiles. A
unified similarity measure
results from this approach. The approach is illustrated on a real data set extracted from Facebook and compared with other similarity measures for the same data.