I hate to be negative, but if I understand you correctly, this approach simply won't work if your goal is AGI.
Natural language understanding has a long history of general failure, despite extensive research into various representations of natural language. Part of the problem is "the symbol grounding problem," another problem is the problem of implementing commonsense reasoning in a machine, which some people regard as *the* unsolved problem of AGI. Rather than reinventing the wheel regarding manipulating words and data structures, I'd recommend thinking of some clever new way to implement commonsense reasoning first, then everything else will fall into place.
P.S.--(1) I need to go through the "archives" on this site myself, since I'm new here, and (2) I don't happen to know C#.
----------
(p. 15)
Limitations of Symbolic Semantics
Symbolic expressions are supposed to denote things out there in the
(p. 16)
world, at least in some abstract sense.* This is how a cognitive system
is able to interact with the world. There are at least two problems
with this view of semantics: First, there is evidence that any represen-
tation not only re-presents reality but also interprets reality. It is
impossible to talk about semantics as a simple correspondence between
representations and an objective reality. This is Putnam's (1988)
thesis of internal realism. Second, it is unclear how the correspon-
dence between symbols and reality is supposed to arise. This is the
symbol grounding problem.
Dinsmore, John. 1991. Partitioned Representations: A Study in Mental Representation, Language Understanding and Linguistic Structure. Boston: Kluwer Academic Publishers.
----------
(p. 1)
In order for an intelligent creature to act sensibly in the real world,
it must know about that world and be able to use its knowledge effec-
tively. The common knowledge about the world that is possessed by
every schoolchild and the methods for making obvious inferences from
this knowledge are called common sense. Commonsense knowledge
and commonsense reasoning are involved in most types of intelligent
activities, such as using natural language, planning, learning, high-
level vision, and expert-level reasoning. How to endow a computer
program with common sense has been recognized as one of the cen-
tral problems of artificial intelligence since the inception of the field
[McCarthy 1959].
It is a very difficult problem. Common sense involves many subtle
modes of reasoning and a vast body of knowledge with complex inter-
actions.
(p. 2)
In short, most
of what we know and most of the conscious thinking we do has its
roots in common sense. Thus, a complete theory of common sense
would contain the fundamental kernel of a complete theory of human
knowledge and intelligence.
Davis, Ernest. 1990. Representations of Commonsense Knowledge. San Mateo, California: Morgan Kaufmann Publishers.
