Abstract
With the rise of knowledge graph (KG), question answering over knowledge base (KBQA) has attracted increasing attention in recent years. Despite much research has been conducted on this topic, it is still challenging to apply KBQA technology in industry because business knowledge and real-world questions can be rather complicated. In this paper, we present AliMe-KBQA, a bold attempt to apply KBQA in the E-commerce customer service field. To handle real knowledge and questions, we extend the classic “subject-predicate-object (SPO)” structure with property hierarchy, key-value structure and compound value type (CVT), and enhance traditional KBQA with constraints recognition and reasoning ability. We launch AliMe-KBQA in the Marketing Promotion scenario for merchants during the “Double 11” period in 2018 and other such promotional events afterwards. Online results suggest that AliMe-KBQA is not only able to gain better resolution and improve customer satisfaction, but also becomes the preferred knowledge management method by business knowledge staffs since it offers a more convenient and efficient management experience.
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Notes
- 1.
We use “QA” to refer to “question answering” and “question-answer” interchangeably.
- 2.
It is worth to mention that nearly a quarter of questions are vague or incomplete in practice.
- 3.
Entity linking is not performed as disambiguation is not necessary in our current scenario.
- 4.
We use an indicator to denote whether the domain (resp. range) of a property need to be inferred (yes:1, no:0), and how it will be inferred (e.g., by following the “member_of” property).
- 5.
The resolution rate rr is calculated as follows:
, where U denotes the number of unsolved sessions, which includes disliked sessions, no-answer sessions, and sessions that explicitly requests for staff service, and T stands for the number of total sessions. - 6.
Note that the slot “participated goods” is defaulted as “Yes”.
, where U denotes the number of unsolved sessions, which includes disliked sessions, no-answer sessions, and sessions that explicitly requests for staff service, and T stands for the number of total sessions.References
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Li, FL., Chen, W., Huang, Q., Guo, Y. (2019). AliMe KBQA: Question Answering over Structured Knowledge for E-Commerce Customer Service. In: Zhu, X., Qin, B., Zhu, X., Liu, M., Qian, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. CCKS 2019. Communications in Computer and Information Science, vol 1134. Springer, Singapore. https://doi.org/10.1007/978-981-15-1956-7_12
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