Machine Learning Use Cases

KBpedia, combined with your own schema and data, can provide a nearly automated foundation for creating trainng corpuses and training sets for deep learning, unsupervised learning, and supervised learning. Further, these same selection capabilities, combined with the logical basis of the KBpedia knowledge graph, also aid the creation of reference standards. Reference standards are essential for tuning analysis parameters to obtain the best results for your tagging or categorization efforts. Tuning parameters are integral to most forms of natural language processing and for supervised learning.

There is a bit more explanation of machine learning on this site. Also, here are some of the use cases we have conducted relevant to machine learning:

• Create Supervised Learning Training Sets
• Create Word Embedding Corpuses
• Create Graph Embedding Corpuses
• Classify Text
• Create 'Gold Standards' for Tuning Learners
• Disambiguate KG Concepts
• Dynamic Machine Learning Using the KG.