KBpedia is a comprehensive knowledge structure for promoting data interoperability and knowledge-based artificial intelligence, or KBAI. The KBpedia knowledge structure combines six (6) public knowledge bases — Wikipedia, Wikidata, OpenCyc, GeoNames, DBpedia and UMBEL — into an integrated whole. KBpedia's upper structure, or knowledge graph, is the KBpedia Knowledge Ontology. KKO is based on the universal categories and knowledge representation theories of the great 19th century American logician, polymath and scientist, Charles Sanders Peirce.

KBpedia includes 54,000 reference concepts, about 30 million entities, and 3,000 relations and properties, all organized according to about 80 modular typologies that can be readily substituted or expanded. Items added to KBpedia are subjected to a rigorous suite of logic and consistency tests — and best practices — prior to acceptance. The result is a flexible and computable knowledge graph that can be sliced-and-diced and configured for all sorts of machine learning tasks, including supervised, unsupervised and deep learning.

KBpedia, KKO and its mapped information can drive multiple use cases include creating word embedding models, fine-grained entity recognition and tagging, relation and sentiment extractors, and categorization. Knowledge-based AI models may be set up and refined with unprecedented speed and accuracy by leveraging the integrated KBpedia structure.

To learn more, try out the KBpedia demo or explore the KBpedia knowledge graph.



Concept Tagging

Expand KBpedia's more than 50,000 general concepts with ones relevant to your own business and domain, and then tag all forms of document and text input

Entity Tagging

Add your own specific data to the more than 30 million entires already in KBpedia to tag specific entities of interest and to disambiguate specific references


Mapping is essential to bring in new knowledge bases and to integrate your own existing vocabularies, schema and instance data to work within the KBpedia structure

Data Integration

The consistent and coherent scaffolding provided by KBpedia is a computable basis for incorporating new data, ensuring that your data integration efforts are fast and logical

Semantic Search

Information grounded in a knowledge graph means you can now go beyond labels to deal with what things mean, and to broaden search by inference and semsets

Machine Learning

The rich set of features and structure in KBpedia translates into fast set ups and nearly automatic support for all leading AI machine learning techniques