text: 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.
  @cite_11: The growing need of sharing data and digital resources within and across organizations has produced a novel attention on issues related to ontology and instance matching. After an introductory classification of the main techniques and tools for ontology matching, the chapter focuses on instance matching by providing an accurate classification of the matching techniques proposed in the literature, and a comparison of the recent instance matching tools according to the results achieved in the OAEI 2009 contest. Ontology and instance matching solutions developed in the BOEMIE project for multimedia resource management and ontology evolution are finally presented.
summary: The Semantic Web community has studied the problem of matching entities across knowledge graphs, for example, the Ontology Alignment Evaluation Initiative (OAEI) on ontology matching in knowledge graphs. However, the benchmarks used in these evaluations are quite small. For example, the benchmark @cite_11 has a total of only 1800 instances and 50,000 triples.