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This paper introduces KGCQual, an interpretable metric for evaluating the quality of automatically constructed knowledge graphs by assessing entity and relation faithfulness to source text, and demonstrates its correlation with downstream performance.
This paper presents a reproducible pipeline for extracting structured user-preference triples from conversational data using lightweight LLMs for personal knowledge graph construction and evaluates the downstream utility in recommendation systems.