KBpedia exploits large-scale knowledge bases and semantic technologies for effective machine learning and data interoperability. KBpedia can power knowledge management-oriented Web services and APIs. KBpedia is a re-factoring of public knowledge bases — Wikipedia, Wikidata, OpenCyc, DBpedia and UMBEL — into an integrated whole. This logically coherent knowledge structure makes the entire system computable. The combined knowledge sources within KBpedia provide rich and unparalleled feature sets upon which to train machine learners and conduct artificial intelligence. KBpedia thus provides unique capabilities in:

  • Integrating domain data
  • Fine-grained entity identification, extraction and tagging
  • Faceted, semantic search and retrieval
  • Mapping and integration of external datasets
  • Natural language processing and computational linguistics
  • Knowledge graph creation, extension and maintenance
  • Tailored filtering, slicing-and-dicing, and extraction of domain knowledge structures
  • Data harvesting, transformation and ingest
  • Data interoperability, re-use of existing content and data assets, and knowledge discovery
  • Supervised, semi-supervised and distant supervised machine learning for:
    • Typing, classification, extraction, and tagging of entities, attributes and relations
  • Unsupervised and deep learning.