Techniques for knowledge graph (KGs) enrichment have been increasingly crucial for commercial applications that rely on evolving product catalogues. However, because of the huge search space of potential enrichment, predictions from KG completion (KGC) methods suffer from low precision, making them unreliable for real-world catalogues. Moreover, candidate facts for enrichment have varied relevance to users. While making correct predictions for incomplete triplets in KGs has been the main focus of KGC method, the relevance of when to apply such predictions has been neglected. Motivated by the product search use case, we address the angle of generating relevant completion for a catalogue using user search behaviour and the users property association with a product. In this paper, we present our intuition for identifying enrichable data points and use general-purpose KGs to show-case the performance benefits. In particular, we extract entity-predicate pairs from user queries, which are more likely to be correct and relevant, and use these pairs to guide the prediction of KGC methods. We assess our method on two popular encyclopedia KGs, DBPedia and YAGO 4. Our results from both automatic and human evaluations show that query guidance can significantly improve the correctness and relevance of prediction.

翻译：知识图谱（KG）增强技术对于依赖动态产品目录的商业应用日益关键。然而，由于潜在增强的搜索空间巨大，知识图谱补全（KGC）方法的预测精度较低，使其难以在实际目录中可靠应用。此外，用于增强的候选事实对用户具有不同的相关性。尽管KGC方法的主要焦点一直是正确预测知识图谱中的不完整三元组，但何时应用此类预测的相关性问题却被忽视了。受产品搜索用例的启发，我们探讨了利用用户搜索行为及用户与产品的属性关联来为目录生成相关补全的视角。本文提出了识别可增强数据点的直观方法，并利用通用知识图谱展示其性能优势。具体而言，我们从用户查询中提取实体-谓词对——这些配对更可能正确且相关——并利用这些配对指导KGC方法的预测。我们在两个流行的百科全书知识图谱DBPedia和YAGO 4上评估了该方法。自动评估与人工评估的结果均表明，查询指导能显著提升预测的正确性与相关性。