Chinese Knowledge Base Question Answering by Attention-Based Multi-Granularity Model

Chinese knowledge base question answering (KBQA) is designed to answer the questions with the facts contained in a knowledge base. This task can be divided into two subtasks: topic entity extraction and relation selection. During the topic entity extraction stage, an entity extraction model is built to locate topic entities in questions. The Levenshtein Ratio entity linker is proposed to conduct effective entity linking. All the relevant subject-predicate-object (SPO) triples to topic entity are searched from the knowledge base as candidates. In relation selection, an attention-based multi-granularity interaction model (ABMGIM) is proposed. Two main contributions are as follows. First, a multi-granularity approach for text embedding is proposed. A nested character-level and word-level approach is used to concatenate the pre-trained embedding of a character with corresponding embedding on word-level. Second, we apply a hierarchical matching model for question representation in relation selection tasks, and attention mechanisms are imported for a fine-grained alignment between characters for relation selection. Experimental results show that our model achieves a competitive performance on the public dataset, which demonstrates its effectiveness.