  In this introductory article we present the basics of an approach to
implementing computational interpreting of natural language aiming to model the
meanings of words and phrases. Unlike other approaches, we attempt to define
the meanings of text fragments in a composable and computer interpretable way.
We discuss models and ideas for detecting different types of semantic
incomprehension and choosing the interpretation that makes most sense in a
given context. Knowledge representation is designed for handling
context-sensitive and uncertain / imprecise knowledge, and for easy
accommodation of new information. It stores quantitative information capturing
the essence of the concepts, because it is crucial for working with natural
language understanding and reasoning. Still, the representation is general
enough to allow for new knowledge to be learned, and even generated by the
system. The article concludes by discussing some reasoning-related topics:
possible approaches to generation of new abstract concepts, and describing
situations and concepts in words (e.g. for specifying interpretation
difficulties).
