Researchers propose 24 quality dimensions for knowledge graphs used by LLMs
A new paper on arXiv outlines how knowledge graphs (KGs) interact with large language models (LLMs) and introduces a framework for assessing their quality. The authors argue that KGs—structured data used to ground LLM responses—must meet criteria like accuracy, completeness, and traceability. They identify 24 quality dimensions and propose a customizable framework for evaluating KGs across…
Key points
- Proposes 24 quality dimensions for knowledge graphs used by LLMs, including accuracy and traceability
- Introduces a customizable framework for assessing KG quality across domains or tasks
- No benchmarks or metrics included; paper focuses on conceptual framework and literature review
The work builds on existing literature about data and linked data quality but focuses on KGs specifically. While no benchmarks or metrics are provided, the paper aims to guide developers and researchers in designing trustworthy KGs for LLM applications. The authors do not claim this is a definitive solution but present it as a starting point for further discussion and refinement.
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