Understanding Unit of Measurements (UoM) is critical to ecommerce search engines. It is a challenging problem since numeric values and unit symbols are typically treated as regular text tokens. In this paper, we introduce a framework that utilizes Knowledge Graph (KG) and deep learning based Named Entity Recognition (NER) to provide better semantic understanding of UoMs.
Our approach for matching UoMs in text consists of three main components, (i) knowledge graph, (ii) entity resolution, (iii) query rewrite. Our KG integrates product data with real-world knowledge domains including brands, colors, materials, UoM, etc. The UoM sub-graph captures around 800 entities and relations between them, including label variants, physical quantities, SI base units, and
Qunzhi Zhou et al.
The proposed approach was evaluated in an o ine and online setting. In the o ine setting, a set of 10,000 random search queries were used to measure the change in recall and precision. The human evaluation with independent annotators showed considerable increase in recall, while the relevance remained the same. Secondly, we conducted an online A/B test, exposing the new experience to millions of users. For the treated tra c, we observed statistically signi cant drop in search abandonment rate and decrease in low recall search sessions. This matches well with the observed o ine recall increase of 5%+ for the random queries containing UoMs.
We have shown that through the use of KG and NER, we can improve UoM handling for query understanding and improve item ranking as a result. Given the genericness of the solution, our next step is to extend the application over domains such as sizes, colors and materials.
Acknowledgement. We thank Nadia V., Sathish K., Sneha K., Steven X., Simon F., and Alan P. for their contributions.