Dynamic Updating of the Knowledge Base for a Large-Scale Question Answering System

Abstract

Today, the knowledge base question answering (KB-QA) system is promising to achieve a large-scale high-quality reply in the e-commerce industry. However, there exist two major challenges to efficiently support large-scale KB-QA systems. On the one hand, it is difficult to serve tens of thousands of online stores (i.e., constrained by the tuning and deployment time), and it would perform poorly if the systems start without a sufficient number of chat records. On the other hand, current KB-QA systems cannot be updated in an efficient way due to the high cost of knowledge base (KB) updating. In this article, we propose an automatic learning scheme for KB-QA systems, called ALKB-QA , using a vector modeling method to address the preceding two main challenges. The ALKB-QA system provides online stores with basic KB templates that are suitable for many common occasions, and this feature enables the ability to deploy chatbots for a large number of online stores in a short time. Then, the KBs are further updated automatically to adapt to their own businesses (meet different specific needs), leading to increased reply accuracy. Our work has three main contributions. First, the proposed ALKB-QA system has a good business model in the e-commerce industry (serving tens of thousands of online stores with low cost), breaking the scalability limitations of existing KB-QA systems. Second, we assess the reply accuracy of the proposed ALKB-QA system using human evaluations, and the results show that it outperforms human annotation-base approaches. Third, we launched our ALKB-QA system as a real-world business application, and it supports tens of thousands of online stores.