AMALGAM: Making tabular dataset explicit with knowledge graph
Language
EN
Communication dans un congrès avec actes
This item was published in
CEUR Workshop Proceedings, Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2020, 2020-11-05, Virtual. 2020-11-05, vol. 2775, p. 9-16
English Abstract
In this paper we present AMALGAM, a matching approach to
annotate tabular dataset with the use of a knowledge graph, developed in the context of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab ...Read more >
In this paper we present AMALGAM, a matching approach to
annotate tabular dataset with the use of a knowledge graph, developed in the context of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020). The ultimate goal is to provide fast and e cient 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 SemTab 2020 with both Column Type Annotation and Cell Type Annotation tasks showed promising results.Read less <