Abstract
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.
Abstract (translated)
本文探讨了不同知识图之间的实体匹配问题。给定一个知识图中的查询实体，我们希望在另一个知识图中找到对应的现实实体。我们在现有dbpedia和wikidata之间的跨本体链接的基础上，将这个问题形式化，并为这个任务提供两个大规模的数据集，重点关注几十万个不明确的实体。采用基于分类的方法，我们发现基于RDF2VEC图的简单多层感知器，在每个知识图中嵌入实体，只需少量的训练数据，就可以达到高精度。我们工作的贡献是用于检查这个问题的数据集，以及未来工作可以基于的强大基线。
URL
https://arxiv.org/abs/1903.06607
