Knowledge-Driven Learning and Discovery

The goal of our current research is machine learning with the help and guidance of a knowledge base (KB). Rather than learning numerical models, our approach generates explicit symbolic hypotheses. These hypotheses are subject to the constraints of the KB and are easily human-readable and verifiable. Toward this end, we have implemented algorithms that hypothesize new relations and new types of entities in a KB by examining structural regularities in the KB that represent implicit knowledge. We evaluate these algorithms on a publications KB and a zoology KB.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Benjamin Lambert, Scott E. Fahlman
• Keywords: AAAI 2007, Explicit Symbolic Hypotheses, Intelligent Agents, Publications Kb, Zoology Kb