# Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction Source: [Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction]() Collection index: [llms.txt]() Data updated: 2026-01-01T14:45:23.605Z Authors: Keiji Shinzato, Naoki Yoshinaga, Yandi Xia, Wei-Te Chen Published: 2022-06-28T19:43:57.000Z ## Abstract A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Attribute Value Extraction on AE-pub cleaned (test)](): Attribute Value Extraction; AE-pub cleaned (test); Macro Precision: 47.74. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()