FactForge: Data Service or the Diversity of Inferred Knowledge over LOD

Linked Open Data movement is maturing. LOD cloud increases by billions of triples yearly. Technologies and guidelines about how to produce LOD fast, how to assure their quality, and how to provide vertical oriented data services are being developed (LOD2, LATC, baseKB). Little is said however about how to include reasoning in the LOD framework, and about how to cope with its diversity. This paper deals with this topic. It presents a data service --- FactForge --- the biggest body of general knowledge from LOD on which inference is performed. It has close to 16B triples available for querying, derived from about 2B explicit triples, after inference and some OWLIM repository specific optimization. We discuss the impacts of the reference layer of FactForge and inference on the diversity of the web of data, and argue for a new paradigm of data services based on linked data verticals, and on inferred knowledge.