Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems. We study six metadata-generation settings for RDF datasets, ranging from simple rewriting to profile-grounded and agentic graph-based generation, and evaluate them jointly for retrieval effectiveness and faithfulness. Unconstrained metadata rewriting delivers the strongest retrieval gains over the original metadata, but it is also the least faithful, showing that search improvements can be driven by unsupported semantic expansion. More grounded settings substantially improve faithfulness, and profile-grounded rewriting provides the most balanced trade-o...
| Authors | Riccardo Terrenzi, Serkan Ayvaz |
|---|---|
| Primary category | cs.IR |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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