2021/01/17 by Rabia Azzi, Azzi, Rabia, Gayo Diallo +1
Computer Science · Decision Sciences · #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Privacy-Preserving Technologies in Data #cs.DB #cs.IR
paper · pdf · doi:10.48550/arxiv.2101.06637
10 pages
arxiv created 2021/01/17 · openalex publication_date 2021/01/17 · arxiv updated 2021/01/19 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.