In previous articles I have covered multiple ways to create training corpuses for unsupervised learning and positive and negative training sets for supervised learning 1 , 2 , 3 using Cognonto and KBpedia. Different structures inherent to a knowledge graph like KBpedia can lead to quite different corpuses and sets. Each of these corpuses or sets may yield different predictive powers depending on the task at hand.
So far we have covered two ways to leverage the KBpedia Knowledge Graph to automatically create positive and negative training corpuses:
- Using the links that exist between each KBpedia reference concept and their related Wikipedia pages
- Using the linkages between KBpedia reference concepts and external vocabularies to create training corpuses out of
Now we will introduce a third way to create a different kind of training corpus:
- Using the KBpedia aspects linkages.
Aspects are aggregations of entities that are grouped according to their characteristics different from their direct types. Aspects help to group related entities by situation, and not by identity nor definition. It is another way to organize the knowledge graph and to leverage it. KBpedia has about 80 aspects that provide this secondary means for placing entities into related real-world contexts. Not all aspects relate to a given entity.