A gold standard dataset for large knowledge graphs matching

In the last decade, a remarkable number of Knowledge Graphs (KGs) were developed, such as DBpedia, NELL and Google knowledge graph. These KGs are the core of many web-based applications such as query answering and semantic web navigation. The majority of these KGs are semi-automatically constructed, which has resulted in a significant degree of heterogeneity. KGs are highly complementary; thus, mapping them can benefit intelligent applications that require integrating different KGs such as recommendation systems and search engines. Although the problem of ontology matching has been investigated and a significant number of systems have been developed, the challenges of mapping large-scale KGs remain significant. In 2018, OAEI has introduced a specific track for KG matching systems. Nonetheless, a major limitation of the current benchmark is their lack of representation of real-world KGs. In this work we introduce a gold standard dataset for matching the schema of large, automatically constructed, less-well structured KGs based on DBpedia and NELL. We evaluate OAEI's various participating systems on this dataset, and show that matching large-scale and domain independent KGs is a more challenging task. We believe that the dataset which we make public in this work makes the largest domain-independent gold standard dataset for matching KG classes.