忠实还是可查找？评估用于RDF数据集搜索的大语言模型生成的元数据
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
AI总结：
研究RDF数据集六种元数据生成设置，评估其检索有效性与忠实性。发现无约束重写检索增益强但忠实性低，更有根据的设置提高了忠实性，基于配置文件的重写权衡最佳，将合成元数据定位为需综合评估多方面的系统级信息检索问题。
摘要：
数据集搜索严重依赖元数据，这使得大语言模型生成的元数据成为检索系统中合成内容的重要形式。我们研究了RDF数据集的六种元数据生成设置，从简单重写到基于配置文件和基于智能体的图形生成，并联合评估它们的检索有效性和忠实性。无约束的元数据重写带来了比原始元数据更强的检索增益，但忠实性最低，表明搜索改进可能由无根据的语义扩展驱动。更有根据的设置显著提高了忠实性，基于配置文件的重写在检索有效性和根据性之间提供了最平衡的权衡。这些发现将合成元数据定位为一个系统级的信息检索问题，其中有效性、来源和信任必须一起评估。
英文摘要：
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-off between retrieval effectiveness and grounding. These findings position synthetic metadata as a system-level IR problem in which effectiveness, provenance, and trust must be evaluated together.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
