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Found 11 papers, 2 papers with code
Creating Knowledge Graphs for Geographic Data on the Web
Geographic data plays an essential role in various Web, Semantic Web and machine learning applications.
Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks
We formulate the Placement of Charging Stations problem as a non-linear integer optimisation problem that seeks the optimal positions for charging stations and the optimal number of charging piles of different charging types.
Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap
OpenStreetMap is a unique source of openly available worldwide map data, increasingly adopted in real-world applications.
Attention-Based Vandalism Detection in OpenStreetMap
This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data.
WorldKG: A World-Scale Geographic Knowledge Graph
We describe the WorldKG knowledge graph, including its ontology that builds the semantic dataset backbone, the extraction procedure of the ontology and geographic entities from OpenStreetMap, and the methods to enhance entity annotation.
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale
OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e. g., buildings and roads) worldwide.
Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting
Our model efficiently fuses dynamic and static information to facilitate accurate forecasting.
Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs
Our experiments performed to align OSM datasets for several countries with two of the most prominent openly available knowledge graphs, namely, Wikidata and DBpedia, demonstrate that the proposed approach outperforms the state-of-the-art schema alignment baselines by up to 53 percentage points in terms of F1-score.
Mining Topological Dependencies of Recurrent Congestion in Road Networks
The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and scheduling of public transportation services.
Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information
In this article, we propose OSM2KG - a novel link discovery approach to predict identity links between OSM nodes and geographic entities in a knowledge graph.
Inferring Missing Categorical Information in Noisy and Sparse Web Markup
Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties.
