GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs

• URL: http://arxiv.org/abs/2504.17099v1
• Date: Wed, 23 Apr 2025 21:17:31 GMT
• Title: GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs
• Authors: Martin Boeckling, Heiko Paulheim, Sarah Detzler,
• Abstract summary: We introduce a variant of RDF2Vec that incorporates geometric information to learn location-aware embeddings of entities.<n>Our approach expands different nodes by flooding the graph from geographic nodes, ensuring that each reachable node is considered.
• Score: 1.6658912537684454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information.<n>Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information.<n>Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts [70.21017141742763]
  Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. Existing methods generally use a fixed number of GNN layers to generate representations for all graphs. We propose the depth adaptive mixture of expert (DA-MoE) method, which incorporates two main improvements to GNN.
  arXiv  Detail & Related papers  (2024-11-05T11:46:27Z)
• Grid-Based Projection of Spatial Data into Knowledge Graphs [1.3723120574076129]
  We show how efficiently the spatial characteristics of real-world entities can be encoded within knowledge graphs. We introduce a novel methodology for representing street networks in knowledge graphs, diverging from the conventional practice of individually capturing each street segment.
  arXiv  Detail & Related papers  (2024-11-04T17:35:41Z)
• A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid [1.6658912537684454]
  We propose a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap on individual h3 grid cells.
  arXiv  Detail & Related papers  (2024-05-24T09:22:20Z)
• Learning Object Placement via Dual-path Graph Completion [28.346027247882354]
  Object placement aims to place a foreground object over a background image with a suitable location and size. In this work, we treat object placement as a graph completion problem and propose a novel graph completion module (GCM) The foreground object is encoded as a special node that should be inserted at a reasonable place in this graph.
  arXiv  Detail & Related papers  (2022-07-23T08:39:39Z)
• Graph Neural Networks with Learnable Structural and Positional Representations [83.24058411666483]
  A major issue with arbitrary graphs is the absence of canonical positional information of nodes. We introduce Positional nodes (PE) of nodes, and inject it into the input layer, like in Transformers. We observe a performance increase for molecular datasets, from 2.87% up to 64.14% when considering learnable PE for both GNN classes.
  arXiv  Detail & Related papers  (2021-10-15T05:59:15Z)
• Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping [71.59494156155309]
  Bottom-up approaches for image-based multi-person pose estimation consist of two stages: keypoint detection and grouping. In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN) The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association.
  arXiv  Detail & Related papers  (2021-04-06T09:21:14Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• node2coords: Graph Representation Learning with Wasserstein Barycenters [59.07120857271367]
  We introduce node2coords, a representation learning algorithm for graphs. It learns simultaneously a low-dimensional space and coordinates for the nodes in that space. Experimental results demonstrate that the representations learned with node2coords are interpretable.
  arXiv  Detail & Related papers  (2020-07-31T13:14:25Z)
• SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting [9.949690056661218]
  We propose a location-aware KG embedding model called SE-KGE. It encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE.
  arXiv  Detail & Related papers  (2020-04-25T17:46:31Z)
• Geom-GCN: Geometric Graph Convolutional Networks [15.783571061254847]
  We propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs.
  arXiv  Detail & Related papers  (2020-02-13T00:03:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.