%0 Journal Article %A LU Qi %A PAN Zhisong %A XIE Jun %T Bidirectional Attention Question Answering Model Combining Knowledge Representation Learning %D 2021 %R 10.3778/j.issn.1002-8331.2103-0478 %J Computer Engineering and Applications %P 171-177 %V 57 %N 23 %X

Question Answering over Knowledge Graph(KGQA) is one of the research hotspots in the field of natural language processing and has received extensive attention in recent years. KGQA faces challenges such as multi-hop problems that need to combine multiple triples for reasoning and incomplete knowledge graphs. To solve these problems, a KR-BAT model which combines knowledge representation and bidirectional attention mechanism is proposed. It introduces knowledge representation learning to improve the global modeling ability and deals with incomplete knowledge graph; the bidirectional attention model captures the rich interactive information between candidate answers and questions, and give answers after analysis and reasoning. Experiments are conducted on the MetaQA dataset and compared with baseline models such as VRN, KV-MemNN, GraftNet. Results show that KR-BAT achieves very competitive performance on the complete knowledge graph, and is further improved than the baseline model on the incomplete knowledge graph.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2103-0478