Knowledge Representation
Reading: Chapter 10.1-10.2
Classes offered in spring
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Vision/Robotics
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NLP/Speech
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4706 Spoken Language Processing (Hirschberg)
6998-3 Natural Language Processing for the Web
(McKeown)
http://cs.columbia.edu/~kathy/NLPforWeb.htm
Machine Learning
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6732 Computational Imaging (Nayar)
6735 Visual Databases (Kender)
6994-4 Computational Photography (Belhumeur)
Other
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4771 Machine Learning (Jebara)
4172 3D User Interfaces (Feiner)
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Homework
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What’s important (i.e., this will be used in determining your
grade):
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Finding features that make a difference
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You should expect to do some digging in the data
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Find a feature that requires manipulation of data
Reformatting of data to provide a more consistent feature (e.g., gender, profession)
Turn in a sample of your data file in ARFF format with the features
you ended up using
(5 instances only)
Turn in a Weka log documenting the series of steps you used to
arrive at your model
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We want the experimentation that backs up your claims in the report
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When you notice a cat in profound meditation,
The reason, I tell you is always the same:
His Mind is engaged in a rapt contemplation of the
Thought, of the thought, of the thought of his
name.
T.S. Eliot
Old Possum’s Book of Practical Cats
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Five Roles that KR plays
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A surrogate for some part of the
real world
A set of ontological commitments
A fragmentary theory of intelligent
reasoning
A medium for pragmatically efficient
computation
A medium of human expression
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KR as a surrogate
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Agents “reason” about models of
the world, rather than the world
itself
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Deduce properties without having to
directly gather information from the world
Predict consequences of potential actions
rather than performing the actions
directly
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We always have two universes of
discourse – call them “physical” and
“phenomenal”, or what you will – one
dealing with questions of quantitative
and formal structure, the other with
those qualities that constitute a
“world.” All of us have our own
distinctive mental worlds, our own
inner journeyings and landscapes, and
these, for most of us, require no clear
neurological “correlate.”
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Example
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Given a representation
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What are its semantics?
What is the meaning of its structures?
 What does it mean/refer to?
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Fidelity – how accurate is it?
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Areas of Activity
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Designing formats for expressing
information
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Encoding knowledge (knowledge
engineering)
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Mostly “general purpose” knowledge
representations (e.g., first order logic)
Mostly identifying and describing conceptual
vocabulary (ontologies)
Declarative representations are the focus
of KR technology
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Knowledge that is domain specific but task
independent
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Example of representations
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KRs are never a complete model
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When modeling the real world, KRs
are always imperfect
“Consequently, even with a sound
reasoning, incorrect conclusions are
inevitable”
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Ontological commitments
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A KR is a set of ontological
commitments
An ontology is a theory of what
exists in the world
Classes, objects, relations, attributes,
properties, constraints, special
individuals, etc.
 Provides a vocabulary for expressing
knowledge
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Example of KR structures
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A Vocabulary for the World
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A KR makes a commitment to a
particular ontology
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To describing the world with particular
terms
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Taxonomy of the world
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Promiscuity vs. perspicacity
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Example of a Vocabulary for the World
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OMEGA
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http://omega.isi.edu:8007/index
http://omega.is.edu/doc/browsers.h
tml
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Ontological Commitments
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“The commitments are in effect a strong
pair of glasses that determine what we
can see, bringing some part of the world
into sharp focus, at the expense of
blurring other parts.”
A KR is not just a data structure
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“Part of what makes a language
representational is that it carries meaning, I.e.,
there is a correspondence between its
constructs and things in the external world.”
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KR as a theory of reasoning
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Many knowledge representations
offer fragmentary theories of
intelligent reasoning
Humans employ multiple strategies
for representing and reasoning
about the world
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Impact of reasoning theory
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The selected theory affects methods
and possible inference
Only certain facts can be inferred
 Some methods of inference are
“sanctioned” or illegal
 A better method of reasoning than
undirected search
 Theory provides “recommendations” for
strategies of inference
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Efficient Computation
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Some work has focused on
knowledge content and what could,
in principle, be derived from it
without concern for efficiency
Sound
 Complete
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Tradeoff between efficiency and
expressiveness
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Heuristic Adequacy
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Providing a representation that
supports adequately efficient
problem solving
Early heuristic systems
 Any-time computations
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KR as a medium for human
expression
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An intelligent system must have
KRs that can be understood by
humans
We need to be able to encode knowledge
in the knowledge base without significant
effort
 We need to be able to understand what
the system knows and how it draws it
conclusions
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Open Issues for KR
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How can a reasoning mechanism
generate implicit knowledge?
How can a system use knowledge to
influence its behavior?
How is incomplete or noisy
knowledge handled?
How can practical results be
obtained when reasoning is
intractable?
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Different Forms of Knowledge
Representation
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Logical representation schemes
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Procedural representation schemes
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Network representation schemes
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Structured representation schemes
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