[Objective] In order to fully get expert resources, the authors have carried out the information fusion research based on multiple-sensor expert features. [Methods] Firstly, in the view of working process of sensor, this paper brings out three methods based on knowledge sensor, Web sensor and social network sensor in sequence. Secondly, focusing on resource balancing degree, it designs the method of expert feature recognition based on multiple-sensor information to solve the conflict which three obtained eigenvectors give rise to. [Results] Matching the expert feature from C-DBLP, the degree of similarity is close to thirty-nine percent, which can be accepted among similar methods. [Limitations] On one hand, many experts identified are from universities and institutes, correspondingly, academic resources for feature recognition are of great account. On the other hand, the site collection for Web sensor can be extended further. [Conclusions] Under the circumstance of controlled relationship between keywords, this method can be applied to many aspects, such as the construction of expert teams, the recommendation and retrieval of experts, and so on.
李纲, 叶光辉. 多源专家特征信息融合研究[J]. 现代图书情报技术, 2014, 30(4): 27-33.
Li Gang, Ye Guanghui. Research on Information Fusion for Multiple-sensor Expert Features. New Technology of Library and Information Service, 2014, 30(4): 27-33.
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