# Entity Extraction from Wikipedia List Pages

arXiv: https://arxiv.org/abs/2003.05146 · HTML: https://mlsanity.com/paper/2003.05146/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Resolving Knowledge Conflicts in Large Language Models](https://mlsanity.com/paper/2310.00935/) (2023)
- [NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval](https://mlsanity.com/paper/2310.14282/) (2023)
- [Entity Type Prediction Leveraging Graph Walks and Entity Descriptions](https://mlsanity.com/paper/2207.14094/) (2022)
- [The CaLiGraph Ontology as a Challenge for OWL Reasoners](https://mlsanity.com/paper/2110.05028/) (2021)
- [NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker](https://mlsanity.com/paper/2303.04426/) (2023)
- [SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features](https://mlsanity.com/paper/2309.11791/) (2023)

## Abstract (arXiv)

When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime source of information on the web. DBpedia and YAGO, as large cross-domain knowledge graphs, encode a subset of that knowledge by creating an entity for each page in Wikipedia, and connecting them through edges. It is well known, however, that Wikipedia-based knowledge graphs are far from complete. Especially, as Wikipedia's policies permit pages about subjects only if they have a certain popularity, such graphs tend to lack information about less well-known entities. Information about these entities is oftentimes available in the encyclopedia, but not represented as an individual page. In this paper, we present a two-phased approach for the extraction of entities from Wikipedia's list pages, which have proven to serve as a valuable source of information. In the first phase, we build a large taxonomy from categories and list pages with DBpedia as a backbone. With distant supervision, we extract training data for the identification of new entities in list pages that we use in the second phase to train a classification model. With this approach we extract over 700k new entities and extend DBpedia with 7.5M new type statements and 3.8M new facts of high precision.