An Adaptive Clustering Approach for Accident Prediction

Dadwal, Rajjat; Funke, Thorben; Demidova, Elena

doi:10.1109/itsc48978.2021.9564564

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Geographic data plays an essential role in a range of real-world applications on the Web, including machine learning models estimating travel time or charging demand for electric vehicles, recommending points of interest and predicting traffic accidents (e.g., [2], [9]). Such applications rely on rich representations of a variety of geographic entities including monuments, roads and charging stations.…”

Section: Introductionmentioning

confidence: 99%

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Creating knowledge graphs for geographic data on the web

et al. 2022

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Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.

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Section: Introductionmentioning

confidence: 99%

“…-Geographic Web Information Sources such as OpenStreetMap (OSM 1 ) provide characteristics of geographic entities and their relationships. Today, OSM is an essential source of free and open geographic Web information created by voluntary effort, containing over 6.8 billion entities from 188 countries 2 .…”

Section: Introductionmentioning

confidence: 99%

Creating knowledge graphs for geographic data on the web

et al. 2022

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WorldKG: World-Scale Completion of Geographic Information

Dsouza,

Tempelmeier,

Gottschalk

et al. 2023

Volunteered Geographic Information

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Knowledge graphs provide standardized machine-readable representations of real-world entities and their relations. However, the coverage of geographic entities in popular general-purpose knowledge graphs, such as Wikidata and DBpedia, is limited. An essential source of the openly available information regarding geographic entities is OpenStreetMap (OSM). In contrast to knowledge graphs, OSM lacks a clear semantic representation of the rich geographic information it contains. The generation of semantic representations of OSM entities and their interlinking with knowledge graphs are inherently challenging due to OSM’s large, heterogeneous, ambiguous, and flat schema and annotation sparsity. This chapter discusses recent knowledge graph completion methods for geographic data, comprising entity linking and schema inference for geographic entities, to provide semantic geographic information in knowledge graphs. Furthermore, we present the WorldKG knowledge graph, lifting OSM entities into a semantic representation.

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An Adaptive Clustering Approach for Accident Prediction

Cited by 2 publications

References 15 publications

Creating knowledge graphs for geographic data on the web

Creating knowledge graphs for geographic data on the web

WorldKG: World-Scale Completion of Geographic Information

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