文章基本信息

标题：Social network search based on semantic analysis and learning
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作者：Feifei Kou ; Junping Du ; Yijiang He 等
期刊名称：CAAI Transactions on Intelligence Technology
电子版ISSN：2468-2322
出版年度：2016
卷号：1
期号：4
页码：293-302
DOI：10.1016/j.trit.2016.12.001
出版社：IET Digital Library
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 the prospect and further work of social network search.
关键词：Semantic analysis; Semantic learning; Cross-modal; Social network search