Demo

KBpedia exploits large-scale knowledge bases and semantic technologies for effective machine learning and data interoperability. Here are some example ways that KBpedia may power knowledge management-oriented Web services or APIs. Try it for yourself:

 
 
 

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KBpedia is an integrated and computable combination of six, large-scale, public knowledge bases — Wikipedia, Wikidata, OpenCyc, GeoNames, DBpedia and UMBEL. The KBpedia structure separately captures entities, atributes, relations and topics. These are classed into a natural and rich diversity of types, with their meaning and relationships coherently and logically organized.

 

KBpedia provides unique capabilities in many areas, but three of the most important are:

  • Pre-staging entity and relation type labels for supervised machine learning training sets and reference standards
  • Fine-grained entity and relation type taggers and extractors, and
  • Mapping to external data schema for structured, semi-structured and unstructured data integration and interoperability.

The use of KBpedia for knowledge-based artificial intelligence (KBAI) is designed to leverage your own domain and business data. The vetted and computable structure of KBpedia means these functions may be migrated to your own information with unprecedented speed and accuracy, leading to much reduced deployment costs.