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, schema.org, DBpedia, GeoNames, OpenCyc, 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:
A computable overlay for either Wikipedia or Wikidata
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.
KBpedia exploits large-scale knowledge bases and semantic technologies for machine learning, data interoperability and mapping, and fact extraction and tagging.