Cognonto's internal knowledge structure is KBpedia. It includes 39,000 reference concepts and about 20 million entities. The knowledge graph that organizes this computable framework is the KBpedia Knowledge Ontology (KKO). KBpedia and KKO provide a baseline system that works best when combined with your own enterprise and domain information using the Cognonto Mapper.
KBpedia, KKO and mapped information can drive machine learners, semantic technologies, and artificial intelligence applications. Example use cases include creating word embedding models, fine-grained entity recognition and tagging, relation and sentiment extractors, and categorization. AI applications include supervised, semi-supervised, and unsupervised machine learners, and deep learning. Knowledge-based AI models may be set up and refined with unprecedented speed and accuracy by leveraging the integrated KBpedia structure.
To explore the KKO knowledge graph, simply enter a possible concept into the search box above. Possible matching concepts are presented as you type. Once you enter the KKO graph, you can explore and navigate in many different ways. Alternatively, try one of these KKO concepts as a way to get started:
The KBpedia knowledge structure combines six (6) public knowledge bases — Wikipedia, Wikidata, OpenCyc, GeoNames, DBpedia and UMBEL — into an integrated whole. KBpedia is the entirety of the combined knowledge bases; KKO is the schema by which these combined sources are made coherent.