With this release of version 1.60, we are pleased to announce the open-source availability of KBpedia — its upper ontology (KKO), full knowledge graph, mappings to major leading knowledge bases, and logical concept groupings according to 70 largely disjoint typologies.

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

KBpedia, written primarily in OWL 2, includes 55,000 reference concepts, about 30 million entities, and 5,000 relations and properties, all organized according to about 70 modular typologies that can be readily substituted or expanded. We test candidates added to KBpedia using a rigorous (but still fallible) suite of logic and consistency tests — and best practices — before 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 such as providing a computable framework over Wikipedia and Wikidata, 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, explore the KBpedia knowledge graph, or download resources.

 
 

Uses

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 specific data to the more than 30 million entries already in KBpedia to tag chosen entities of interest and to disambiguate references

Mapping

Mapping is essential to bring in new knowledge bases and to integrate your 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 setups and nearly automatic support for all leading AI machine learning techniques