[Submitted on 3 Oct 2026]
Title:Knowledge Graphs Quality Assessment in the Era of LLMs: A Conceptual Framework
View a PDF of the paper titled Knowledge Graphs Quality Assessment in the Era of LLMs: A Conceptual Framework, by Elwin Huaman
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Abstract:Knowledge Graphs (KGs) have been popularized during the last few decades and are increasingly constructed and consumed by Large Language Models (LLMs). Various applications therefore rely on KGs to provide concise, complete, accurate, faithful, fresh, and traceable answers. However, what is the quality of those KGs? In which cases should a KG be used, and can it be trusted to ground an LLM? How might it be evaluated? We reviewed the literature on quality assessment (QA) of data, information, linked data, KGs, and the intersection of KGs and LLMs. We extended the current state-of-the-art frameworks to a catalog of 24 quality dimensions (QDs) and various quality metrics (QMs). Furthermore, we propose a general-purpose, customizable to a domain or task, and practical QA framework for assessing the quality of KGs.
Comments: 8 pages, 1 figure, KGSWC 2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.31684 [cs.CL]
(or arXiv:2610.31684v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.31684
arXiv-issued DOI via DataCite
Submission history
From: Elwin Huaman [view email] [v1] Sat, 3 Oct 2026 19:41:14 UTC (1,808 KB)
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