Active Learning for Product Type Ontology Enhancement in E-commerce
This work addresses the costly process of ontology building for e-commerce search, but it is incremental as it applies an existing active learning approach to a specific domain.
The paper tackles the problem of constructing a product type ontology for e-commerce semantic search by proposing an active learning framework to reduce human effort, showing improved quality and coverage in experiments.
Entity-based semantic search has been widely adopted in modern search engines to improve search accuracy by understanding users' intent. In e-commerce, an accurate and complete product type (PT) ontology is essential for recognizing product entities in queries and retrieving relevant products from catalog. However, finding product types (PTs) to construct such an ontology is usually expensive due to the considerable amount of human efforts it may involve. In this work, we propose an active learning framework that efficiently utilizes domain experts' knowledge for PT discovery. We also show the quality and coverage of the resulting PTs in the experiment results.