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.
翻译:地理数据在各种网络、语义网站和机器学习应用中发挥着关键作用。OpenStreetMap和知识图是网上地理数据的重要补充来源,然而,数据真实性、地理和语义特征缺乏整合以及表述不全,严重限制了数据效用。核实、浓缩和语义代表对于使地理数据为语义网站和机器学习提供方便至关重要。这篇文章描述了我们最近为应对这些挑战制定的办法。