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Web、语义网及机器学习等各类应用中扮演着关键角色。OpenStreetMap与知识图谱是Web上地理数据的重要互补来源。然而，数据真实性、地理与语义特征整合的缺失，以及表示不完整等问题严重制约了数据的可用性。验证、丰富化及语义表示对于使地理数据能够被语义网和机器学习所利用至关重要。本文介绍了我们近期为应对这些挑战而研发的方法。