# GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs

arXiv: https://arxiv.org/abs/2504.17099 · HTML: https://mlsanity.com/paper/2504.17099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?](https://mlsanity.com/paper/1910.12507/) (2019)
- [SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting](https://mlsanity.com/paper/2004.14171/) (2020)
- [WorldKG: A World-Scale Geographic Knowledge Graph](https://mlsanity.com/paper/2109.10036/) (2021)
- [Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs](https://mlsanity.com/paper/2009.04404/) (2020)
- [On a Generalized Framework for Time-Aware Knowledge Graphs](https://mlsanity.com/paper/2207.09964/) (2022)
- [Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings](https://mlsanity.com/paper/2004.04423/) (2020)
- [Do Similar Entities have Similar Embeddings?](https://mlsanity.com/paper/2312.10370/) (2023)
- [A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid](https://mlsanity.com/paper/2405.15375/) (2024)

## Abstract (arXiv)

Many knowledge graphs contain a substantial number of spatial entities, such as cities, buildings, and natural landmarks. For many of these entities, exact geometries are stored within the knowledge graphs. However, most existing approaches for learning entity representations do not take these geometries into account. In this paper, we introduce a variant of RDF2Vec that incorporates geometric information to learn location-aware embeddings of entities. Our approach expands different nodes by flooding the graph from geographic nodes, ensuring that each reachable node is considered. Based on the resulting flooded graph, we apply a modified version of RDF2Vec that biases graph walks using spatial weights. Through evaluations on multiple benchmark datasets, we demonstrate that our approach outperforms both non-location-aware RDF2Vec and GeoTransE.