RelationalFactQA shows that LLMs are much worse at retrieving facts as multi-record tables than as single answers, with the best model reaching only 24.7% tuple similarity.
YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Knowledge Bases (KBs) find applications in many knowledge-intensive tasks and, most notably, in information retrieval. Wikidata is one of the largest public general-purpose KBs. Yet, its collaborative nature has led to a convoluted schema and taxonomy. The YAGO 4 KB cleaned up the taxonomy by incorporating the ontology of Schema.org, resulting in a cleaner structure amenable to automated reasoning. However, it also cut away large parts of the Wikidata taxonomy, which is essential for information retrieval. In this paper, we extend YAGO 4 with a large part of the Wikidata taxonomy - while respecting logical constraints and the distinction between classes and instances. This yields YAGO 4.5, a new, logically consistent version of YAGO that adds a rich layer of informative classes. An intrinsic and an extrinsic evaluation show the value of the new resource.
citation-role summary
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models
RelationalFactQA shows that LLMs are much worse at retrieving facts as multi-record tables than as single answers, with the best model reaching only 24.7% tuple similarity.