Study of Chinese Knowledge Base Question Answering System Based on Multi-Label Strategy

Most of the existing Chinese Knowledge Base Question Answering (CKBQA) system focus on simple questions that need a single triplet query, but cannot solve complex questions involving multiple entities and relations. To address the problem, this paper proposes a CKBQA system for answer search based on multi-label strategy. The system mainly consists of two parts: Question processing and answer search.In the question processing part, a new model framework is constructed based on the pre-trained language model to perform entity mention recognition, entity linking and relation extraction for the questions. By setting three classification labels, the questions are divided into simple questions, chain questions and multi-entity questions. In the answer search part, different processing methods are given for the above three kinds of classification questions. The experimental results show that the average F1 value of the proposed system reaches 66.76% on the validation set of evaluation data, CCKS2019-CKBQA.