# Graph-Embedding Empowered Entity Retrieval

arXiv: https://arxiv.org/abs/2005.02843 · HTML: https://mlsanity.com/paper/2005.02843/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 10 of 10)

- [Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval](https://mlsanity.com/paper/2312.10661/) (2023)
- [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021)
- [Entity-aware Transformers for Entity Search](https://mlsanity.com/paper/2205.00820/) (2022)
- [Unsupervised Matching of Data and Text](https://mlsanity.com/paper/2112.08776/) (2021)
- [DREQ: Document Re-Ranking Using Entity-based Query Understanding](https://mlsanity.com/paper/2401.05939/) (2024)
- [The X Types -- Mapping the Semantics of the Twitter Sphere](https://mlsanity.com/paper/2409.14584/) (2024)
- [Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking](https://mlsanity.com/paper/2606.15998/) (2026)
- [Explaining Graph Neural Networks for Node Similarity on Graphs](https://mlsanity.com/paper/2407.07639/) (2024)
- [Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints](https://mlsanity.com/paper/2508.13663/) (2025)
- [LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings](https://mlsanity.com/paper/2510.11584/) (2025)

## Abstract (arXiv)

In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. We demonstrate empirically that encoding information from the knowledge graph into (graph) embeddings contributes to a higher increase in effectiveness of entity retrieval results than using plain word embeddings. We analyze the impact of the accuracy of the entity linker on the overall retrieval effectiveness. Our analysis further deploys the cluster hypothesis to explain the observed advantages of graph embeddings over the more widely used word embeddings, for user tasks involving ranking entities.