TabEAno: Table to Knowledge Graph Entity Annotation
This addresses the challenge of utilizing open table data for researchers and practitioners in data integration, though it appears incremental as it builds on existing lookup strategies.
The paper tackles the problem of ambiguous and heterogeneous table data by proposing TabEAno, a method for semantically annotating table rows to knowledge graph entities, which outperforms state-of-the-art approaches on standard datasets like T2D and Limaye and a large-scale Wikipedia dataset.
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.