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

Fabian Suchanek, Mehwish Alam, Thomas Bonald, Lihu Chen, Pierre-Henri Paris +1 more
8/23/2023
cs.AIcs.IR

No official code found. Best implementation: yago-naga/yago-4.5 (72 stars, CC-BY-4.0, last commit 2026-05-04).

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.

View on arXivView PDF

Code Implementations(1)

yago-naga/yago-4.5Community100%
CC-BY-4.0

The YAGO version that integrates the Wikidata taxonomy

7214HTML, ShellApr 24, 20235 months agoCC-BY-4.0

Similar Papers

A Multi-Source Retrieval Question Answering Framework Based on RAG

78% match2024

Reorganizing Educational Institutional Domain using Faceted Ontological Principles

78% match2023

Synergistic Interplay between Search and Large Language Models for Information Retrieval

77% match1 repo372023

5W1H+Which: Context-Valid Semantic Indexing with Progressive Ontology Binding

77% match2026

Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation

77% match2024

Science and Technology Ontology: A Taxonomy of Emerging Topics

77% match2023

Cite this paper

@article{suchanek2023yago,
  title  = {YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy},
  author = {Fabian Suchanek and Mehwish Alam and Thomas Bonald and Lihu Chen and Pierre-Henri Paris and Jules Soria},
  year   = {2023},
  eprint = {2308.11884},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.11884v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.11884.svg)](https://codeofpaper.com/paper/2308.11884)

Discussion