Controlling Search in Very large Commonsense Knowledge Bases: A Machine
  Learning Approach
  Very large commonsense knowledge bases (KBs) often have thousands to millions
of axioms, of which relatively few are relevant for answering any given query.
A large number of irrelevant axioms can easily overwhelm resolution-based
theorem provers. Therefore, methods that help the reasoner identify useful
inference paths form an essential part of large-scale reasoning systems. In
this paper, we describe two ordering heuristics for optimization of reasoning
in such systems. First, we discuss how decision trees can be used to select
inference steps that are more likely to succeed. Second, we identify a small
set of problem instance features that suffice to guide searches away from
intractable regions of the search space. We show the efficacy of these
techniques via experiments on thousands of queries from the Cyc KB. Results
show that these methods lead to an order of magnitude reduction in inference
time.
