Artificial intelligence and machine learning are bringing a revolution to knowledge discovery, management and integration. Though improved algorithms and faster graphics chips have been contributors, arguably the most important factor in AI's recent renaissance has been the availability of massive electronic datasets upon which to train the machine learners. Wikipedia and statistical analysis of search engine databases have been notable contributors to knowledge management (KM) and natural language processing (NLP).[1]

Yet, despite the evidence for the importance of knowledge-based artificial intelligence to these recent advances, the actual approach to KBAI has been ad hoc and fragmented. What has been lacking is a systematic approach to stage knowledge bases for the explicit purpose of driving artificial intelligence applications.

That is, until now.

KBpedia re-purposes the information within seven (7) large-scale, public knowledge bases for KBAI with an architecture and suite of Web services and APIs for knowledge discovery and management. The resulting knowledge structure is an unparalleled resource for driving new knowledge management and data interoperability capabilities. KBpedia helps bring meaningful AI development into the hands of any organization.

1. Image and voice recognition have benefited from their own separate datasets, such as ImageNet.