Uniting "a priori" and "a posteriori" knowledge: A research framework

The ability to perform machine classification is a critical component of an intelligent system. We propose to unite the logical, a priori approach to this problem with the empirical, a posteriori approach. We describe in particular how the a priori knowledge encoded in Cyc can be merged with technology for probabilistic inference using Markov logic networks. We describe two problem domains – the Whodunit Problem and noun phrase understanding – and show that Cyc’s commonsense knowledge can be fruitfully combined with probabilistic reasoning.