论文标题
将OpenStreetMap与知识图链接 - 链接发现模式 - 不合时式的志愿地理信息
Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information
论文作者
论文摘要
Wikidata和Dbpedia等流行知识图中捕获的地理实体的表示通常不完整。 OpenStreetMap（OSM）是公开可用的志愿地理信息的丰富来源，具有很高的补充这些表示形式的潜力。但是，知识图实体和OSM节点之间的身份链接仍然很少见。由于缺乏严格的模式和OSM中用户定义的节点表示形式的异质性，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，我们提出了OSM2KG-一种新颖的链接发现方法，以预测知识图中OSM节点和地理实体之间的身份联系。 OSM2KG方法的核心是OSM节点的新型潜在，紧凑的表示，可捕获嵌入中语义节点相似性。 OSM2KG采用这种潜在表示来训练有监督的模型进行链接预测，并利用OSM和知识图之间的现有链接进行培训。我们的实验在几个OSM数据集以及Wikidata和DBPedia知识图上进行了，表明OSM2KG可以可靠地发现身份链接。 OSM2KG在Wikidata上的F1得分为92.05％，平均达到DBPEDIA的94.17％，与表现最佳底层相比，Wikidata的F1得分的F1得分增加了21.82个百分点。
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.
