Dynamic Service Knowledge Base Construction at WeChat

Haoyang Li, Alexander Zhou, Fengmei Jin, Qing Li, Ziyuan Zhao, Hao Xin, Qiang Yan, Tiezheng Mao, Xueling Lin, Zijian Li, Lei Chen

Abstract: Service-oriented knowledge base (KB) has been applied to a variety of applications. In online platforms, the official accounts registered by companies display service mentions (e.g., train ticket booking) to provide services for users. To facilitate the downstream tasks, we propose to construct a dynamic service KB. Specifically, the service KB contains accounts in different service domains. Moreover, since these service mentions for the same service have different names, the service KB should store the canonicalized service mentions for each account. Despite the fruitiness, existing KB construction and NLP approaches rely on abundant annotated labels. However, in real-world applications, it is impractical and expensive to annotate abundant account and service mention labels, due to a large number of accounts and services. In this paper, we propose an end-to-end dynamic service KB construction system. First, we infer the service domain of each account under limited labeled accounts. Second, without service mention labels, we propose to adaptively separate service mentions into disjoint partitions, where service mentions inside the same partition provide the same service. Third, our system can be updated with dynamic scenarios naturally, including service changes, new accounts, and new service domains. The experiments on real-world datasets and a large-scale online A/B testing demonstrate the effectiveness and high practicality of our system.

External IDs:dblp:conf/adma/LiZJLZXYMLLC25