# Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

arXiv: https://arxiv.org/abs/1812.06280 · HTML: https://mlsanity.com/paper/1812.06280/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2018.

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

- [Journalistic Guidelines Aware News Image Captioning](https://mlsanity.com/paper/2109.02865/) (2021)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)
- [Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia](https://mlsanity.com/paper/2209.10474/) (2022)
- [Company2Vec -- German Company Embeddings based on Corporate Websites](https://mlsanity.com/paper/2307.09332/) (2023)
- [Survey on Semantic Interpretation of Tabular Data: Challenges and Directions](https://mlsanity.com/paper/2411.11891/) (2024)

## Abstract (arXiv)

The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io.