Tabular data in the form of CSV files is the common input format in a data analytics pipeline. However a lack of understanding of the semantic structure and meaning of the content may hinder the data analytics process. Thus gaining this semantic understanding will be very valuable for data integration, data cleaning, data mining, machine learning and knowledge discovery tasks. For example, understanding what the data is can help assess what sorts of transformation are appropriate on the data.
Tables on the Web may also be the source of highly valuable data. The addition of semantic information to Web tables may enhance a wide range of applications, such as web search, question answering, and knowledge base (KB) construction.
Tabular data to Knowledge Graph (KG) matching is the process of assigning semantic tags from Knowledge Graphs (e.g., Wikidata or DBpedia) to the elements of the table. This task however is often difficult in practice due to metadata (e.g., table and column names) being missing, incomplete, or ambiguous.
This challenge aims at benchmarking systems dealing with the tabular data to KG matching problem, so as to facilitate their comparison on the same basis and the reproducibility of the results.
The challenge includes the following tasks organised into several evaluation rounds:
- CTA Task: Assigning a semantic type (e.g., a KG class) to a column.
- CEA Task: Matching a cell to a KG entity.
- CPA Task: Assigning a KG property to the relationship between two columns.