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.

翻译：在电子商务中,准确和完整的产品类型(PT)本体学对于在查询中识别产品实体和从目录中检索相关产品至关重要,然而,发现产品类型(PTs)建造这种本体学通常费用昂贵,因为可能涉及大量的人力工作。在这项工作中,我们提议建立一个积极的学习框架,有效利用域专家的知识进行PT的发现。我们还在实验结果中显示了由此产生的PT的质量和覆盖面。