Query Parsing aims to extract product attributes, such as color, brand, and product type, from search queries. These attributes play a crucial role in search engines for tasks such as matching, ranking, and recommendation. There are two types of attributes: explicit attributes that are mentioned explicitly in the search query, and implicit attributes that are mentioned implicitly. Existing works on query parsing do not differentiate between explicit query parsing and implicit query parsing, which limits their performance in product search engines. In this work, we demonstrate the critical importance of implicit attributes in real-world product search engines. We then present our solution for implicit query parsing at an e-commerce product search engine, which is a unified framework combining recent advancements in knowledge graph technologies and customer behavior analysis. We demonstrate the effectiveness of our proposal through offline experiments on search log data. We also show how to use the framework on an e-commerce search engine to improve customers’ shopping experiences.
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