AMALGAM: A Matching Approach to Fairfy TabuLar Data with KnowledGe GrAph Model
Language
EN
Chapitre d'ouvrage
This item was published in
Advances in Intelligent Systems and Computing. 2021-03-29, vol. 1366, p. 76-86
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Tabular data
Knowledge graph
Entity linking