تقرير
YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy
| العنوان: | YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy |
|---|---|
| المؤلفون: | Suchanek, Fabian, Alam, Mehwish, Bonald, Thomas, Chen, Lihu, Paris, Pierre-Henri, Soria, Jules |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Information Retrieval |
| الوصف: | 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. Comment: Published at SIGIR 2024, cite that paper in scientific articles |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.11884 |
| رقم الأكسشن: | edsarx.2308.11884 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |