Combined Structure-Weight Graph Similarity and its Application in E-Health
نویسندگان
چکیده
A combined structure-weight similarity approach for comparing directed (vertexand edge-)labeled (edge-) weighted graphs is presented. Vertex labels (as types) and edge labels (as attributes) embody semantic information. Edge weights express assessments regarding the (percentage-)relative importance of the attributes, a kind of pragmatic information. These graphs are uniformly represented and interchanged using a weighted extension of Object Oriented RuleML. We propose semantic-pragmatic information retrieval and clustering where a combination of structure and weight similarities between a query and stored graphs is calculated. The structure and weight similarity values are used as primary and secondary criteria, respectively, to rank the retrieved graphs. The proposed weight similarity algorithm refines the ranking of retrieved graphs that have identical or nearly identical querygraph structure similarity but have different edge weights. It is shown that our approach leads to higher precision compared to earlier approaches that did not incorporate the similarity of edge weights. The proposed approach of semanticpragmatic information retrieval and clustering can be applied, for example, in e-Learning, e-Business, social networks, and Health 3.0. In this paper, the application focus is in e-Health, specifically the retrieval of mental health records. Keywords-graph similarity; structure similarity; weight similarity; weighted Object Oriented RuleML; e-Health.
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