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YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy

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arxiv 2308.11884 v2 pith:HL4FFMD2 submitted 2023-08-23 cs.AI cs.IR

classification cs.AIcs.IR
keywords taxonomyyagolargewikidataclassesinformationknowledgeretrieval
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    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.

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