People-focused knowledge management

Methodological Knowledge: Knowledge that provides our method-
ological approaches and reasoning strategies with the metaknowl-
edge for how to think and reason within particular contexts 
and situations, given information about the situations and the
background knowledge in terms of facts, data, perspectives, and
judgments.
Model-Based Expert System: A type of expert system, usually
intended for diagnostic purposes, which is based on a model of the
structure and behavior of the device or system it is designed to
“understand.”
Model-Based Reasoning: Complex reasoning strategies that allow
the use of mathematical or logical models as representations of the
domain knowledge.
Natural Language (NL): A language in common use by people to
communicate among themselves (Example: Chinese or English).
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Natural Language Generation: The part of natural language-
processing research that attempts to have computers present 
information to their users in a natural language.
Natural Language Interface (NLI): A computer program that allows
the user to communicate with a computer in a natural language.
An NLI may incorporate both natural language-understanding and
natural language-generation capabilities. An NLI is sometimes
called a natural language front end.
Natural Language Processing (NLP): An area of AI research that
allows computers to use a natural language. Natural language 
processing is divided into natural language understanding and
natural language generation.
Natural Language Understanding: The part of natural language-
processing research that investigates methods of allowing com-
puters to understand a natural language.
Neural Nets: A family of reasoning strategies and knowledge repre-
sentations that are patterned on the neural architecture of the
brain. Neural nets often consist of a large number of nodes con-
nected by links that attenuate, amplify, and transmit signals.
Neural nets must be “trained” using examples to modify the
strength of the couplings between nodes to change the net’s rea-
soning behavior. Neural nets are used in a number of applications
where the knowledge is amorphous and ill understood, like hand-
writing interpretation, seismic data interpretation, and so on.