融合全局—局部特征的多粒度关系检测模型.

Relation detection for knowledge base question answering (KBQA) aims to select the best relation path that matches the question expressed by natural language from the candidate relations in the knowledge base and retrieve the answer to the question. To solve the problem of semantic information loss and inadequate attention interaction in existing relation detection methods, this paper proposed a multi-granularity relation detection model incorporating global-local features. The model firstly used bi-directional long short-term memory (Bi-LSTM) networks to encode the question and relation and modeled relations from multiple granularity, such as word-level and relation-level representation. Then, the model introduced a bi-directional attention mechanism to implement attentive interaction of the question and relation. Finally, it extracted global and local features by the aggregation operation and word-level interaction, respectively, to calculate the semantic similarity of the question and candidate relation. Experiments show that the proposed model achieves 93.5 % and 84.13% accuracy on SimpleQuestions and WebQuestionsSP datasets, respectively, improving the effect of relation detection. [ABSTRACT FROM AUTHOR]