Generating Linked Data by Inferring the Semantics of Tables

Abstract

Vast amounts of information is 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. We 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. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (semantics) associated with a table. Using background knowledge from the Linked Open Data cloud, we jointly infer the semantics of column headers, table cell values (e.g.,strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples. We motivate the value of this approach using tables from the medical domain, discussing some of the challenges presented by these tables and describing techniques to tackle them.