AMALGAM: Making tabular dataset explicit with knowledge graph
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EN
Communication dans un congrès avec actes
Este ítem está publicado en
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
Resumen en inglés
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 ...Leer más >
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.< Leer menos
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