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SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Hassanzadeh, O., Efthymiou, V. , Chen, J. & Srinivas, K. (2020). SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems. In: The Semantic Web. ESWC 2020. Lecture Notes in Computer Science. (pp. 514-530). Cham: Springer. ISBN 978-3-030-49460-5 doi: 10.1007/978-3-030-49461-2_30


Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: The final authenticated version is available online at
Publisher Keywords: Tabular data, Knowledge Graphs, Matching
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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