YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913307865120768 |
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| author | Suchanek, Fabian Alam, Mehwish Bonald, Thomas Chen, Lihu Paris, Pierre-Henri Soria, Jules |
| author_facet | Suchanek, Fabian Alam, Mehwish Bonald, Thomas Chen, Lihu Paris, Pierre-Henri Soria, Jules |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_11884 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy Suchanek, Fabian Alam, Mehwish Bonald, Thomas Chen, Lihu Paris, Pierre-Henri Soria, Jules Artificial Intelligence Information Retrieval 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. |
| title | YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2308.11884 |