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A Framework for Quality Assessment of Semantic Annotations of Tabular Data

Avogadro, R., Cremaschi, M., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599 & Rula, A. (2021). A Framework for Quality Assessment of Semantic Annotations of Tabular Data. In: Lecture Notes in Computer Science. International Semantic Web Conference (ISWC), 24-28 Oct 2021, Online. doi: 10.1007/978-3-030-88361-4_31


Much information is conveyed within tables, which can be semantically annotated by humans or (semi)automatic approaches. Nevertheless, many applications cannot take full advantage of semantic annotations because of the low quality. A few methodologies exist for the quality assessment of semantic annotation of tabular data, but they do not automatically assess the quality as a multidimensional concept through different quality dimensions. The quality dimensions are implemented in STILTool 2, a web application to automate the quality assessment of the annotations. The evaluation is carried out by comparing the quality of semantic annotations with gold standards. The work presented here has been applied to at least three use cases. The results show that our approach can give us hints about the quality issues and how to address them.

Publication Type: Conference or Workshop Item (Paper)
Publisher Keywords: Data Quality; Semantic annotation; Tabular data; Semantic Table Interpretation
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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