Cognonto today published two new use cases on how to further leverage KBpedia, its knowledge structure that integrates six major knowledge bases (Wikipedia, Wikidata, OpenCyc, GeoNames, DBpedia and UMBEL), plus mappings to another 20 leading knowledge vocabularies. KBpedia provides a foundation for knowledge-based artificial intelligence (KBAI) by supporting the (nearly) automatic creation of training corpuses and positive and negative training sets and feature sets for deep, unsupervised and supervised machine learning.
The two new use cases are in: 1) dynamic tests and refinements of machine learners enabled by KBpedia’s fast creation of training sets and corpuses and reference (‘gold’) standards; and 2) KBpedia’s unique aspects that provide context for various entity types. With these two additions, Cognonto has now published six diverse use cases:
- Document-specific word2vec Training Corpuses
- Text Classification Using ESA and SVM
- Dynamic Machine Learning Using the KBpedia Knowledge Graph
- Leveraging KBpedia ‘Aspects’ To Generate Training Sets Automatically
- Benefits from Extending KBpedia with Private Datasets
- Mapping External Data and Schema.
Each use case is summarized according to the problem and our approach to solving it and the benefits that result. The use cases themselves present general workflows and code snippets for how the use case was tackled.
We will continue to publish use cases using Cognonto’s technologies and KBpedia as they arise.