智能技术学报

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稿件标题: Social network search based on semantic analysis and learning
稿件作者: Feifei Kou, Junping Du, Yijiang He, Lingfei Ye
关键字词: Semantic analysis; Semantic learning; Cross-modal; Social network search
文章摘要: Because of everyone's involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to
收录刊物: 2016年1卷4期
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