TL;DR
CaLiGraph is a large, complex knowledge graph with a rich ontology that challenges current OWL reasoners, especially in handling owl:hasValue constraints, and we provide benchmark datasets for performance evaluation.
Contribution
The paper introduces benchmark subsets of CaLiGraph to evaluate reasoning systems' performance on complex ontologies and constraints.
Findings
Current reasoners struggle with owl:hasValue constraints in CaLiGraph.
Benchmark datasets are provided for performance analysis.
CaLiGraph's complexity exceeds the capabilities of existing reasoning tools.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Natural Language Processing Techniques
