Social network mashup: Ontology-based social network integration for statistic learning

摘要: The proliferation of online social websites results in the accumulation a large volume real-world data capturing networks diversified application domains. However, are always separated with each other that causes isolated island phenomenon, which becomes impedance to implementing complex analysis requires comprehensive stored several networks. In this paper, we present network mashup approach uses Semantic Web technology integrate heterogeneous contain richer semantics. Secondly, propose statistic learning learns Probabilistic Model (PSM) from semantic structures This framework can utilize these accumulated and integrated without losing Lastly, our is evaluated by real-life combines LinkedIn DBLP predict collaborative colleague relation.