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Large Language Models and Data Quality for Knowledge Graphs
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AI
DOI：10.1016/j.ipm.2025.104281.png)
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Abstract
En 中文
Knowledge Graphs (KGs) have become essential for applications such as virtual assistants, web search, reasoning, and information access and management. Prominent examples include Wikidata, DBpedia, YAGO, and NELL, which large companies widely use for structuring and integrating data. Constructing KGs involves various AI-driven processes, including data integration, entity recognition, relation extraction, and active learning. However, automated methods often lead to sparsity and inaccuracies, making rigorous KG quality evaluation crucial for improving construction methodologies and ensuring reliable downstream applications. Despite its importance, large-scale KG quality assessment remains an underexplored research area.
Keywords:
Knowledge Graphs
Quality Evaluation
Entity Recognition
Relation Extraction
Data Integration
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