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AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model
arXiv - CS - Information Retrieval Pub Date : 2021-01-17 , DOI: arxiv-2101.06637
Rabia Azzi, Gayo Diallo

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.

中文翻译:

AMALGAM:采用知识图谱模型的公平表数据的匹配方法

在本文中,我们介绍了AMALGAM,这是一种使用知识图来使表格数据合理化的匹配方法。最终目标是提供一种快速有效的方法,以来自背景知识的实体对表格数据进行注释。该方法将查找和筛选服务与文本预处理技术结合在一起。在2020年语义网挑战赛中,对表格数据到知识图进行匹配,列类型注释和单元格类型注释任务均进行了实验,结果令人满意。
更新日期:2021-01-19
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