Generating Linked Data by Inferring the Semantics of Tables
Vast amounts of information are encoded in structured tables found in documents, on the web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning. Evidence for a table's meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. The authors represent a table's meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.