  Rule-based machine translation is more data efficient than the big data-based
machine translation approaches, making it appropriate for languages with low
bilingual corpus resources -- i.e., minority languages. However, the rule-based
approach has declined in popularity relative to its big data cousins primarily
because of the extensive training and labour required to define the language
rules. To address this, we present a semantic representation that 1) treats all
bits of meaning as individual concepts that 2) modify or further specify one
another to build a network that relates entities in space and time. Also, the
representation can 3) encapsulate propositions and thereby define concepts in
terms of other concepts, supporting the abstraction of underlying linguistic
and ontological details. These features afford an exact, yet intuitive semantic
representation aimed at handling the great variety in language and reducing
labour and training time. The proposed natural language generation, parsing,
and translation strategies are also amenable to probabilistic modeling and thus
to learning the necessary rules from example data.
