Uncovering the Semantics of Wikipedia Categories
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Abstract (arXiv)
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like YAGO or Probase, and has been used extensively for tasks like entity disambiguation or semantic similarity estimation. Wikipedia's categories are a rich source of taxonomic as well as non-taxonomic information. The category 'German science fiction writers', for example, encodes the type of its resources (Writer), as well as their nationality (German) and genre (Science Fiction). Several approaches in the literature make use of fractions of this encoded information without exploiting its full potential. In this paper, we introduce an approach for the discovery of category axioms that uses information from the category network, category instances, and their lexicalisations. With DBpedia as background knowledge, we discover 703k axioms covering 502k of Wikipedia's categories and populate the DBpedia knowledge graph with additional 4.4M relation assertions and 3.3M type assertions at more than 87% and 90% precision, respectively.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2019.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 4; 2024: 0; 2025: 1; 2026: 2.
10 papers.
- 2023Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Modelscited by 15
- 2021Neural Class Expression Synthesiscited by 6
- 2023KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Taskscited by 6
- 2021The CaLiGraph Ontology as a Challenge for OWL Reasonerscited by 5
- 2023NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linkercited by 5
- 2025What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasetscited by 1
- 2021Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpediacited by 0
- 2023SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Featurescited by 0
- 2026Semantics-Aware Caching for Concept Learningcited by 0
- 2026LLM Agents Factory: Retrieval of Domain-Specific LLM Agentscited by 0
- 2026LLM Agents Factory: Retrieval of Domain-Specific LLM Agents20 May 2026
- 2026Semantics-Aware Caching for Concept Learning6 Mar 2026
- 2025What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasets17 Feb 2025
- 2023SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features21 Sep 2023
- 2023KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks21 Aug 2023
- 2023NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker8 Mar 2023
- 2023Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models13 Feb 2023
- 2021Neural Class Expression Synthesis16 Nov 2021
- 2021The CaLiGraph Ontology as a Challenge for OWL Reasoners11 Oct 2021
- 2021Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia3 May 2021