First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion.
The number of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing.
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions.
Modern large-scale knowledge graphs, such as DBpedia, are datasets which require large computational resources to serve and process.
In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised learning for ontology and instance matching.
In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching EvaLuation Toolkit (MELT).
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.