  In this paper we present AMALGAM, a matching approach to fairify 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 the
2020 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching with
both Column Type Annotation and Cell Type Annotation tasks showed promising
results.
