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。

用户查询引导目录丰富化

Guiding Catalogue Enrichment with User Queries

The paper presents a methodology for uncovering knowledge gaps on the
internet using the Retrieval Augmented Generation (RAG) model. By simulating
user search behaviour, the RAG system identifies and addresses gaps in
information retrieval systems. The study demonstrates the effectiveness of the
RAG system in generating relevant suggestions with a consistent accuracy of
93%. The methodology can be applied in various fields such as scientific
discovery, educational enhancement, research development, market analysis,
search engine optimisation, and content development. The results highlight the
value of identifying and understanding knowledge gaps to guide future
endeavours.

利用检索增强生成（RAG）模型揭示互联网上的知识空白的方法论，并通过模拟用户搜索行为，RAG 系统识别和解决信息检索系统中的空白。该研究展示了 RAG 系统在生成相关建议方面的有效性，准确率达到 93％。该方法可以应用于科学发现、教育增强、研究发展、市场分析、搜索引擎优化和内容开发等各个领域。研究结果突出了识别和理解知识空白以指导未来努力的价值。