  The inherent difficulty of knowledge specification and the lack of trained
specialists are some of the key obstacles on the way to making intelligent
systems based on the knowledge representation and reasoning (KRR) paradigm
commonplace. Knowledge and query authoring using natural language, especially
controlled natural language (CNL), is one of the promising approaches that
could enable domain experts, who are not trained logicians, to both create
formal knowledge and query it. In previous work, we introduced the KALM system
(Knowledge Authoring Logic Machine) that supports knowledge authoring (and
simple querying) with very high accuracy that at present is unachievable via
machine learning approaches. The present paper expands on the question
answering aspect of KALM and introduces KALM-QA (KALM for Question Answering)
that is capable of answering much more complex English questions. We show that
KALM-QA achieves 100% accuracy on an extensive suite of movie-related
questions, called MetaQA, which contains almost 29,000 test questions and over
260,000 training questions. We contrast this with a published machine learning
approach, which falls far short of this high mark.
