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

Autor:	Azzi, Rabia, Diallo, Gayo
Rok vydání:	2021
Předmět:	Computer Science - Databases Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.06637 Zobrazit plný text záznamu View this record from Arxiv