# The CaLiGraph Ontology as a Challenge for OWL Reasoners

arXiv: https://arxiv.org/abs/2110.05028 · HTML: https://mlsanity.com/paper/2110.05028/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [Uncovering the Semantics of Wikipedia Categories](https://mlsanity.com/paper/1906.12089/) (2019)
- [Entity Extraction from Wikipedia List Pages](https://mlsanity.com/paper/2003.05146/) (2020)
- [Information Extraction From Co-Occurring Similar Entities](https://mlsanity.com/paper/2102.05444/) (2021)

## Built on by (most cited first, 5 of 5)

- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)
- [Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data](https://mlsanity.com/paper/2412.10121/) (2024)
- [Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges](https://mlsanity.com/paper/2308.04814/) (2023)
- [SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features](https://mlsanity.com/paper/2309.11791/) (2023)
- [Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models](https://mlsanity.com/paper/2404.17000/) (2024)

## Abstract (arXiv)

CaLiGraph is a large-scale cross-domain knowledge graph generated from Wikipedia by exploiting the category system, list pages, and other list structures in Wikipedia, containing more than 15 million typed entities and around 10 million relation assertions. Other than knowledge graphs such as DBpedia and YAGO, whose ontologies are comparably simplistic, CaLiGraph also has a rich ontology, comprising more than 200,000 class restrictions. Those two properties - a large A-box and a rich ontology - make it an interesting challenge for benchmarking reasoners. In this paper, we show that a reasoning task which is particularly relevant for CaLiGraph, i.e., the materialization of owl:hasValue constraints into assertions between individuals and between individuals and literals, is insufficiently supported by available reasoning systems. We provide differently sized benchmark subsets of CaLiGraph, which can be used for performance analysis of reasoning systems.