Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information that has a high potential to complement these representations. However, identity links between the knowledge graph entities and OSM nodes are still rare. The problem of link discovery in these settings is particularly challenging due to the lack of a strict schema and heterogeneity of the user-defined node representations in OSM. In this article, we propose OSM2KG - a novel link discovery approach to predict identity links between OSM nodes and geographic entities in a knowledge graph. The core of the OSM2KG approach is a novel latent, compact representation of OSM nodes that captures semantic node similarity in an embedding. OSM2KG adopts this latent representation to train a supervised model for link prediction and utilises existing links between OSM and knowledge graphs for training. Our experiments conducted on several OSM datasets, as well as the Wikidata and DBpedia knowledge graphs, demonstrate that OSM2KG can reliably discover identity links. OSM2KG achieves an F1 score of 92.05% on Wikidata and of 94.17% on DBpedia on average, which corresponds to a 21.82 percentage points increase in F1 score on Wikidata compared to the best performing baselines.

在诸如Wikidata和DBpedia之类的流行知识图中捕获的地理实体的表示形式通常是不完整的。OpenStreetMap（OSM）是开放获取的，自愿的地理信息的丰富来源，具有很大的潜力来补充这些表示形式。但是，知识图实体和OSM节点之间的身份链接仍然很少。由于缺少严格的架构和OSM中用户定义的节点表示的异构性，因此在这些设置中的链接发现问题尤其具有挑战性。在本文中，我们提出OSM2KG-一种新颖的链接发现方法，用于预测知识图中的OSM节点与地理实体之间的身份链接。OSM2KG的核心该方法是OSM节点的一种新颖的，潜在的，紧凑的表示形式，可在嵌入中捕获语义节点的相似性。OSM2KG采用这种潜在表示来训练监督模型进行链接预测，并利用OSM和知识图之间的现有链接进行训练。我们在多个OSM数据集以及Wikidata和DBpedia知识图中进行的实验表明，OSM2KG可以可靠地发现身份链接。OSM2KG在Wikidata上的F1分数平均达到92.05％，在DBpedia上平均达到94.17％，与性能最佳的基线相比，在Wikidata上F1分数提高了21.82个百分点。