Amalgam: A Matching Approach To Fairfy Tabular Data With Knowledge Graph Model

In this paper we present AMALGAM, a matching approach to fairfy tabular data with the use of a knowledge graph. The ultimate goal is to provide fast and efficient approach to annotate tabular data with entities from a background knowledge. The approach combines lookup and filtering services combined with text pre-processing techniques. Experiments conducted in the context of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) 2020 with both Column Type Annotation and Cell Type Annotation tasks showed promising results.