YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy
This provides a cleaner, more structured knowledge base for information retrieval and automated reasoning tasks, though it is incremental as it builds on existing YAGO and Wikidata resources.
The paper tackled the problem of convoluted taxonomy in large knowledge bases like Wikidata by extending YAGO 4 with a rich, logically consistent taxonomy from Wikidata, resulting in YAGO 4.5, which adds informative classes while maintaining cleanliness.
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.