# Creating Knowledge Graphs for Geographic Data on the Web

arXiv: https://arxiv.org/abs/2302.08823 · HTML: https://mlsanity.com/paper/2302.08823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation](https://mlsanity.com/paper/1905.08794/) (2019)
- [GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale](https://mlsanity.com/paper/2108.13092/) (2021)
- [WorldKG: A World-Scale Geographic Knowledge Graph](https://mlsanity.com/paper/2109.10036/) (2021)
- [Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information](https://mlsanity.com/paper/2011.05841/) (2020)
- [Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs](https://mlsanity.com/paper/2107.13257/) (2021)
- [Attention-Based Vandalism Detection in OpenStreetMap](https://mlsanity.com/paper/2201.10406/) (2022)
- [An Adaptive Clustering Approach for Accident Prediction](https://mlsanity.com/paper/2108.12308/) (2021)
- [Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting](https://mlsanity.com/paper/2108.12352/) (2021)

## Abstract (arXiv)

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.