A Study of Expert Finding Methods for Multi-Granularity Encoded Community Question Answering by Fusing Graph Neural Networks

expert finding in community question answering sites aims to match target questions with experts who are most likely to provide satisfactory answers. Network embedding techniques have been highly successful in expert finding. Nevertheless, most network embedding techniques generate only a single feature vector for experts based on historically answered question to match the target question, often ignoring multi-granularity linguistic matching information. As a result, these methods cannot fully capture the similarity between questions and experts. To tackle these challenges, this study proposes a multi-granularity encoded community question answering expert finding model incorporating graph neural networks, i.e., LG-ERMG: 1) LG-ERMG constructs a relationship graph based on the experts and their history of answered questions and utilizes a lightweight graph convolutional network to capture the potential connections among the experts, which can help to enhance the representation of expert expertise; and 2) multi-granularity coding technique is used to learn different granularity of semantic matching information between target questions and experts' historical answer questions. In this study, experiments on two real community question answering datasets are carried out to demonstrate the effectiveness of this approach.