Basic Elements

Inference adds valid new propositions to a knowledge base or determines the truth of propositions not explicitly contained within a knowledge base.

Inference

First-Order Logic (FOL) is a flexible, well-understood, and computationally tractable meaning representation language. It provides a sound computational basis for the verifiability, inference, and expressiveness requirements, as well as a sound model-theoretic semantics.

University of Michigan - Ann Arbor

Computational Desiderata for Representations:

Verifiability:

Verifiability is a system’s ability to compare the state of affairs described by a representation to the state of affairs in some world as modeled in a knowledge base. 

Unambiguous Representations:

The meaning representations itself cannot be ambiguous. The representation of an input’s meaning should be free from any ambiguity, so that the system can reason over a representation that means either one thing or the other in order to decide how to answer. Vagueness: in which a meaning representation leaves some parts of the meaning underspecified. 

Canonical Form:

The doctrine of canonical form says that distinct inputs that mean the same thing
should have the same meaning representation. Canonical form does complicate the task of semantic parsing. 


Logical Representations of Sentence Meaning

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

Inference and Variables:

A system must be able to use inference—to draw valid conclusions based on the meaning representation of inputs and its background knowledge. Now consider the following request: I’d like to find a restaurant where I can get vegetarian food.  Answering this request requires the use of variables, using some representation like the following: Serves(x,VegetarianFood). Matching succeeds only if the variable x can be replaced by some object in the knowledge base in such a way that the entire proposition will then match.

Expressiveness:

A meaning representation scheme must be expressive enough to handle a
wide range of subject matter, ideally any sensible natural language utterance. 

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