关键词: 专家发现方法, 贝叶斯网络, 查询术语

Abstract:

Expert finding is a research hotspot in the field of entity retrieval. Aiming at the shortcomings of the classical expert discovery model, such as the assumption of indexing term independence and the low retrieval performance, and an expert discovery method of Bayesian network with query semantic extension is proposed. The model adopts four-layer network structure, which can realize graphical probabilistic inference, and the semantic extension of query terms can be realized by word vector technology. Experimental results show that the new model is superior to the classical expert discovery model in terms of multiple evaluation indexes, indicating that the new model can effectively extend the semantics of query terms and improve the performance of expert retrieval.

Key words: expert finding method, Bayesian network, query term