GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908335741075456 |
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| author | Boeckling, Martin Paulheim, Heiko Detzler, Sarah |
| author_facet | Boeckling, Martin Paulheim, Heiko Detzler, Sarah |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_17099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs Boeckling, Martin Paulheim, Heiko Detzler, Sarah Machine Learning Social and Information Networks 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. |
| title | GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2504.17099 |