Knowledge base augmentation using tabular data

Large linked data repositories have been built by leveraging semi-structured data in Wikipedia (e.g., DBpedia) and through extracting information from natural language text (e.g., YAGO). However, the Web contains many other vast sources of linked data, such as structured HTML tables and spreadsheets. Often, the semantics in such tables is hidden, preventing one from extracting triples from them directly. This paper describes a probabilistic method that augments an existing knowledge base with facts from tabular data by leveraging a Web text corpus and natural language patterns associated with relations in the knowledge base. A preliminary evaluation shows high potential for this technique in augmenting linked data repositories.