Umberto Straccia.
How Much Knowledge is in a Knowledge Base? Introducing Knowledge
Measures (Preliminary Report).
In Proceedings of the 24th European Conference on Artificial
Intelligence (ECAI-20), Frontiers in Artificial
Intelligence and Applications 325, IOS Press, pages 905-912, 2020.
Abstract:
In this work we address the following question: can we measure how
much knowledge a knowledge base represents? We answer to this
question (i) by describing properties (axioms) that a knowledge
measure we believe should have in measuring the amount of
knowledge of a knowledge base (kb); and (ii) provide a concrete
example of such a measure, based on the notion of entropy. We also
introduce related kb notions such as (i) accuracy;
(ii) conciseness; and (iii) Pareto optimality.
Informally, they address the following questions: (i) how precise is
a kb in describing the actual world? (ii) how succinct is a kb
w.r.t. the knowledge it represents? and (iii) can we increase
accuracy without decreasing conciseness, or vice-versa?