OpenStreetMap与知识图谱的结合--模式无关的地理信息链接发现
Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information
摘要：
在流行的知识图谱（如Wikidata和DBpedia）中捕获的地理实体的表示通常是不完整的。OpenStreetMap（OSM）是一个丰富的公开可用的、自愿提供的地理信息源，具有很大的潜力来补充这些表示。然而，知识图谱实体和OSM节点之间的身份链接仍然很少。在这些设置中的链接发现的问题是特别具有挑战性的，由于缺乏一个严格的模式和异构性的用户定义的节点表示在OSM。在这篇文章中，我们提出了OSM 2KG-一种新的链接发现方法来预测身份OSM节点和地理实体之间的链接在知识图谱。OSM 2KG方法的核心是OSM节点的一种新的潜在的紧凑表示，它在嵌入中捕获语义节点相似性。OSM 2KG采用这种潜在表示来训练用于链接预测的监督模型，并利用OSM和知识图谱之间的现有链接进行训练。我们在几个OSM数据集，以及Wikidata和DBpedia知识图谱上进行的实验表明，OSM 2KG可以可靠地发现身份链接。OSM 2KG在Wikidata上的F1得分为92.05%，在DBpedia上的平均得分为94.17%，与表现最好的基线相比，维基数据上的F1得分增加了21.82个百分点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
