基于知识图谱的在线商品问答研究

现阶段,针对商品的自动问答主要由意图识别和答案配置来实现,但问题答案的配置依赖人工且工作量巨大,容易造成答案质量不高。随着知识图谱技术的出现和发展,基于知识图谱的自动问答逐渐成为研究热点。目前,基于知识图谱的商品自动问答主要是通过规则解析的方法将文本形式问题解析为知识图谱查询语句来实现。虽然减少了人工配置工作,但其问答效果受限于规则的质量和数量,很难达到理想的效果。针对上述问题,该文提出一种基于知识图谱和规则推理的在线商品自动问答系统。主要贡献包括: ①构建一个基于LSTM的属性注意力网络SiameseATT(Siamese attention network)用于属性选择; ②引入了本体推理规则,通过规则推理使得知识图谱能动态生成大量三元组,使得同样数据下可以回答更多问题。在NIPCC-ICCPOL 2016 KBQA数据集上的实验显示,该系统具有很好的性能。相比一些更复杂的模型,该问答系统更适合电商的应用场景。

Online Commodity KBQA Based on Knowledge Graph

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.