AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model
This addresses the need for efficient entity annotation in tabular data for semantic web applications, but appears incremental as it builds on existing matching techniques.
The paper tackles the problem of annotating tabular data with entities from a knowledge graph, presenting AMALGAM as a matching approach that combines lookup, filtering, and text pre-processing. Experiments in the 2020 Semantic Web Challenge showed promising results for column and cell type annotation tasks.
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.