Guiding Catalogue Enrichment with User Queries
Yupei Du, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan

TL;DR
This paper proposes a method to improve knowledge graph enrichment by guiding predictions with user query data, enhancing relevance and correctness for commercial catalogues.
Contribution
It introduces a novel approach that leverages user queries to guide knowledge graph completion, addressing relevance issues in catalogue enrichment.
Findings
Query-guided predictions improve correctness
Enhanced relevance in knowledge graph enrichment
Significant performance gains on DBPedia and YAGO 4
Abstract
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…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Semantic Web and Ontologies · Library Science and Information Systems
MethodsFocus
