Using Linked Data to Interpret Tables
Vast amounts of information are available in structured forms like spreadsheets, database relations, and tables found in documents and on the Web. The Authors describe an approach that uses linked data to interpret such tables and associate their components with nodes in a reference linked data collection. The proposed framework assigns a class (i.e. type) to table columns, links table cells to entities, and inferred relations between columns to properties. The resulting interpretation can be used to annotate tables, confirm existing facts in the linked data collection, and propose new facts to be added. The implemented prototype uses DBpedia as the linked data collection and Wikitology for background knowledge.