AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model

Rabia Azzi; Gayo Diallo

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arXiv · 2101.06637

AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model

Rabia Azzi·Gayo Diallo

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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 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.

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Rabia Azzi, Gayo Diallo. 2021-01-17. AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model. https://arxiv.org/abs/2101.06637

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