多源专家特征信息融合研究

Research on Information Fusion for Multiple-sensor Expert Features

【目的】为全面获取专家资源,探究多源专家特征信息融合方法。【方法】从传感器工作过程出发,依次论述基于知识传感器、Web传感器和社会网络传感器的专家特征识别方法。鉴于三种方法获取的专家特征向量存在冲突,围绕资源均衡度设计基于多源信息融合的专家特征识别方法。【结果】与C-DBLP统计专家特征进行匹配,相似度达到38.97%,与同类型方法比较,结果在正常范围内。【局限】识别对象多来自高校及科研院所,用于特征识别的资源也多为学术资源,同时Web传感器采集网址集合还有待扩展。【结论】在语词关系控制情形下,该方法可用于科研团队构建、专家推荐、专家检索等方面。

[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.