Community answer generation based on knowledge graph

Abstract

Community Question Answering (CQA) has become an indispensable way for modern people to share and acquire knowledge. It allows users to ask questions, which will be answered by experienced users enthusiastically. By recording user operation logs, CQA has accumulated a large amount of valuable and complex data. However, askers must wait (usually for a long time) until other expert users answer their questions on social platforms. This will seriously affect the user experience. In this paper, we propose a Community Answer Generation method based on the Knowledge Graph, called CAGKG, to generate natural language answers automatically. Firstly, we extract the core phrases of posts to represent their semantics relations. Then, we model the user's knowledge background based on their action records. Finally, we query knowledge entities in a knowledge graph based on user background and question semantics, then convert them into natural language answers. Besides, we proposed a Phrase-based Answers Semantic Similarity Evaluation indicator, called PASSE, which focuses on the semantic similarity between texts instead of literal matching. To the best of our knowledge, it is the first work that utilizes the user knowledge and text semantics to improve the performance of CQA. Experiments on four real datasets (Stack Overflow, Super User, Mathematics, and Quora) show that CAGKG is superior to the state-of-the-art question answering frameworks. Compared with other answer evaluation indicators, PASSE is a promising indicator for evaluating semantic similarity.