Bayesian Belief Network Model Using Sematic Concept for Expert Finding

Abstract

The Expert finding is a research hotspot in the area of entity retrieval. However, due to the small number of search terms, the retrieval effect will be poor due to the mechanical text matching. In view of the above shortcomings, we use Bayesian belief network as a model frame, and two expert finding models are proposed. One is a basic semantic belief network retrieval model, in which BERT and LDA models are used, and the other is a compound semantic belief network model. The compound model uses an effective data fusion technique to integrate the retrieval results of the two sub-models in this paper. The paper presents the topology and retrieval algorithm of two models proposed. The experiments verify the validity of the research content on Amine platform. Experimental results show that the semantic model can improve the MAP value, and the compound semantic model is better than the existing expert finding model on multiple evaluation indicators such as P@N, MAP and MRR, and it can improve the performance of expert retrieval.