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.

通过使用用户搜索行为和用户与产品的属性关联，使用用户查询生成相关的目录完成，以指导知识图谱完成方法的预测，从而显著提高预测的正确性和相关性。