WANG Siyu1, QIU Jiangtao1, HONG Chuanyang1, JIANG Ling2
1.School of Information Engineering, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China;
2.Chengdu XiaoDuo Technology Co. Ltd., Chengdu, Sichuan 610041
Abstract：In general, Question Answering System (QAS) for the commodity is mainly built via the intention identification and answer configuration. However, the configuration of answers of questions depends on manual labor, which easily results in poor quality of answers. With the introduction and development of Knowledge Graph (KG) technology, the KG-based QAS has gradually become a hot research topic. At present, the KG-based QAS for commodity is mainly implemented by employing rules to transform questions to queries in the KG. Although the manual configuration work is reduced, the performance of QAS is limited by the quality and quantity of the rules. In order to solve above problems, this paper proposes a question answering method for online commodities based on KG and rule reasoning. The main contributions include: (1) we built an LSTM-based property attention network named SiameseATT(Siamese Attention Network) for attribute selection; (2) we employed KG to infer rules, consequently generate a large number of triples to respond more questions. Finally, experiments on the NLPCC-ICCPOL 2016 dataset show that the model obtains good performance. Our QAS is more suitable for e-commerce applications.
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2020, Vol. 34 
