# Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

arXiv: https://arxiv.org/abs/2004.06842 · HTML: https://mlsanity.com/paper/2004.06842/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

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

- [WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia](https://mlsanity.com/paper/2111.08543/) (2021)
- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [OEKG: The Open Event Knowledge Graph](https://mlsanity.com/paper/2302.14688/) (2023)
- [LaSER: Language-Specific Event Recommendation](https://mlsanity.com/paper/2303.04712/) (2023)
- [Unlearning of Knowledge Graph Embedding via Preference Optimization](https://mlsanity.com/paper/2507.20566/) (2025)
- [Enhancing Academic Paper Recommendations Using Fine-Grained Knowledge Entities and Multifaceted Document Embeddings](https://mlsanity.com/paper/2601.19513/) (2026)

## Abstract (arXiv)

In this paper, we describe an embedding-based entity recommendation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each other, learns complementary entity representations from their topology and content, and combines them with a lightweight learning-to-rank approach to recommend related entities on Wikipedia. Through offline and online evaluations, we show that the resulting embeddings and recommendations perform well in terms of quality and user engagement. Balancing simplicity and quality, this framework provides default entity recommendations for English and other languages in the Yahoo! Knowledge Graph, which Wikipedia is a core subset of.