Posted:November 2, 2015

The Ten Premises of KBAI

Knowledge-based Artificial Intelligence Provides a Systematic Basis for Machine Learning

Inherent in Structured Dynamics‘ discussions about knowledge-based artificial intelligence (KBAI) have been some embedded premises. Some of my prior articles — and future ones to come — elaborate more fully on one or more of these points:

  1. The electronic availability of content-rich knowledge bases has been the most important catalyst for recent AI advances in natural language and information processing
  2. Wikipedia, and its DBpedia and now Wikidata derivatives, has been the most important source of concept and entity information for these purposes
  3. None of these sources is coherently organized; attempts to use lexical relationships (WordNet) or Wikipedia itself (DBpedia ontology) to re-organize the content are also not coherent
  4. Despite this incoherence, these knowledge bases have already been used to train many distant supervised machine learning applications; but, in efforts to date, each application has been manually trained, which is inefficient and time consuming
  5. Fortunately, these knowledge bases can be mapped to a coherent structure; there are perhaps options; we have chosen Cyc
  6. Once the potential role of KBs to inform machine learning is understood, the usefulness becomes obvious to re-express the KBs to maximize the features available for machine learning, including disjointedness assertions to enable selection of positive and negative training sets
  7. Specific aspects of the KBs for which such re-organization is appropriate include concepts, types, entities, relations, events, attributes and statements
  8. Therefore, a systematic re-organization of these KBs to support feature and training set generation can help automate and lower the cost of machine learning pipelines
  9. Once these features and aspects are established, the result becomes a grounding structure, which can facilitate mappings to other knowledge structures, data interoperability and information integration
  10. These same principles can be applied to existing or new knowledge bases, thereby increasing the scope and usefulness of the knowledge structure in a virtuous circle.

Precise definitions for all of the italicized terms are provided in the related glossary. Markup

The Ten Premises of KBAI

Knowledge-based Artificial Intelligence Provides a Systematic Basis for Machine Learning



This article presents ten premises that guide the use of knowledge bases for more automatic and cost-effective means to apply machine learning to natural language and information processing.

see above


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