Grid-Based Projection of Spatial Data into Knowledge Graphs

• URL: http://arxiv.org/abs/2411.02309v1
• Date: Mon, 04 Nov 2024 17:35:41 GMT
• Title: Grid-Based Projection of Spatial Data into Knowledge Graphs
• Authors: Amin Anjomshoaa, Hannah Schuster, Axel Polleres,
• Abstract summary: 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.
• Score: 1.3723120574076129
• License:
• Abstract: The Spatial Knowledge Graphs (SKG) are experiencing growing adoption as a means to model real-world entities, proving especially invaluable in domains like crisis management and urban planning. Considering that RDF specifications offer limited support for effectively managing spatial information, it's common practice to include text-based serializations of geometrical features, such as polygons and lines, as string literals in knowledge graphs. Consequently, Spatial Knowledge Graphs (SKGs) often rely on geo-enabled RDF Stores capable of parsing, interpreting, and indexing such serializations. In this paper, we leverage grid cells as the foundational element of SKGs and demonstrate how efficiently the spatial characteristics of real-world entities and their attributes can be encoded within knowledge graphs. Furthermore, we introduce a novel methodology for representing street networks in knowledge graphs, diverging from the conventional practice of individually capturing each street segment. Instead, our approach is based on tessellating the street network using grid cells and creating a simplified representation that could be utilized for various routing and navigation tasks, solely relying on RDF specifications.

Related papers

• FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference [31.796411806840087]
  Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy. Previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally. We propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences.
  arXiv  Detail & Related papers  (2024-10-26T07:09:27Z)
• Next Level Message-Passing with Hierarchical Support Graphs [20.706469085872516]
  Hierarchical Support Graph (HSG) is a framework for enhancing information flow in graphs, independent of the specific MPNN layers utilized. We present a theoretical analysis of HSGs, investigate their empirical performance, and demonstrate that HSGs can surpass other methods augmented with virtual nodes.
  arXiv  Detail & Related papers  (2024-06-22T13:57:09Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
  We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• Neural networks adapting to datasets: learning network size and topology [77.34726150561087]
  We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a gradient-based training. The resulting network has the structure of a graph tailored to the particular learning task and dataset.
  arXiv  Detail & Related papers  (2020-06-22T12:46:44Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)
• Knowledge Embedding Based Graph Convolutional Network [35.35776808660919]
  This paper proposes a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN) KE-GCN combines the power of Graph Convolutional Network (GCN) in graph-based belief propagation and the strengths of advanced knowledge embedding methods. Our theoretical analysis shows that KE-GCN offers an elegant unification of several well-known GCN methods as specific cases.
  arXiv  Detail & Related papers  (2020-06-12T17:12:51Z)

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.