This paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world entity in another knowledge graph. We formalize this problem and present two large-scale datasets for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. Using a classification-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data. The contributions of our work are datasets for examining this problem and strong baselines on which future work can be based.

翻译：本文探讨了不同知识图中相匹配实体的问题。 在一个知识图中有一个查询实体, 我们希望在另一个知识图中找到相应的真实世界实体。 我们正式确定这一问题,并根据DBpedia和Wikigata之间脱离的跨意识形态联系,为这项任务提出两个大型数据集, 重点是几十万个模糊实体。 我们采用基于分类的方法, 发现基于RDF2Vec图中实体嵌入每个知识图的表述的简单多层概念足以达到高精度, 仅有少量的培训数据。 我们的工作贡献是用于审查这一问题的数据集和今后工作的坚实基线。