Inferencing in the Large - Characterizing Semantic Integration of Open Tabular Data

Tables are a natural and ubiquitous way of representing related information. Actionable insight is usually gleaned from tabular datasets through data mining techniques assisted by domain experts. These techniques however, do not harness the semantics or the contextual reference underlying the datasets. Tabular datasets, especially the ones created as part of open data initiatives often contain information about entities fragmented across several datasets implicitly connected through some semantics thus giving them a contextual reference. Our work deals with harnessing this context (Thematic Framework) in which they can be reasoned further. This thesis aims at creating algorithmic support for a human to semantically integrate a collection of tabular data using ontologies from publicly available knowledge bases in Linked Open Data. The overall objectives of our work called “Inferencing in the Large” aims to go further than this, to enrich the mapped ontology with inferencing rules and generate enriched RDF (Schematic Framework), to enable the use of semantic reasoners.