TabEAno: Table to Knowledge Graph Entity Annotation

TL;DR

TabEAno is a novel method that semantically annotates table rows with knowledge graph entities using a two-cells lookup strategy, effectively addressing ambiguity and heterogeneity in web table data.

Contribution

It introduces a simple yet effective two-cells lookup approach for entity annotation that outperforms existing methods on multiple datasets.

Findings

Outperforms state-of-the-art in T2D and Limaye datasets

Effective on large-scale Wikipedia tables

Addresses ambiguity and heterogeneity in web tables

Abstract

In the Open Data era, a large number of table resources have been made available on the Web and data portals. However, it is difficult to directly utilize such data due to the ambiguity of entities, name variations, heterogeneous schema, missing, or incomplete metadata. To address these issues, we propose a novel approach, namely TabEAno, to semantically annotate table rows toward knowledge graph entities. Specifically, we introduce a "two-cells" lookup strategy bases on the assumption that there is an existing logical relation occurring in the knowledge graph between the two closed cells in the same row of the table. Despite the simplicity of the approach, TabEAno outperforms the state of the art approaches in the two standard datasets e.g, T2D, Limaye with, and in the large-scale Wikipedia tables dataset.