Review of systems papers for tabular data annotation and published by SemTab@ISWC2022

Tabular datasets are constructed by extracting and organizing information from data sources. These data sources can be structured such as databases, Knowledge Graphs (e.g., Wikidata, DBpedia, ORKG), semi-structured such as tables or unstructured such as text in scientific literature. To increase the utility of tabular datasets, they are annotated by matching their content to Knowledge Graphs classes, properties and instances. This is called tabular data annotation or tabular data to knowledge graph matching.

Semantic Table to Knowledge Graph Matching (SemTab) challenge aims at providing a common framework to conduct a systematic evaluation of datasets and systems published by researchers. Thus, since 2019, SemTab@ISWC are publishing researchers' contributions to this research problem. The following comparison table presents the systems proposed by the SemTab community in 2022. In this table, the left panel contains the different comparison criterion used and the right panel the different contributions of the authors.