TL;DR
This paper discusses recent methods for creating and enhancing geographic knowledge graphs on the Web, addressing data veracity, integration, and completeness to improve accessibility for Semantic Web and machine learning applications.
Contribution
It introduces novel approaches for verifying, enriching, and semantically representing geographic data to overcome existing limitations.
Findings
Improved methods for geographic data verification.
Enhanced integration of semantic and geographic information.
Better completeness and utility of geographic knowledge graphs.
Abstract
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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