Transcript Foundations; semiotics, library, cognitive and social science and information modeling Peter Fox Xinformatics – ITEC, CSCI, ERTH 4400/6400 Week 4, February 11, 2014

Foundations; semiotics,
library, cognitive and social
science and information
modeling
Peter Fox
Xinformatics – ITEC, CSCI, ERTH 4400/6400
Week 4, February 11, 2014
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Contents
• Review of last class, reading
• Foundations; semiotics, cognitive science
and information modeling
• Assignment 2
• Next classes
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Reading Review
• Information entropy
• Information Is Not Entropy, Information Is Not
Uncertainty!
• More on entropy
• Context
• Abductive reasoning
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Semiotics
• Also called semiotic studies or semiology, is
the study of sign processes (semiosis), or
signification and communication, signs and
symbols
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A sign (Peirce and Eco 1979)
1. “A sign stands for something to the idea which it
produces or modifies....
2. That for which it stands is called its object, that
which it conveys, its meaning; and the idea which it
gives rise, its interpretant
3. ....[the sign creates in the mind] an equivalent sign,
or perhaps a more developed sign.” (Peirce)
1. “That sign which it creates I call the interpretant of
the first sign.
2. This sign stands for something, its object.
3. It stands for that object, not in all respects, but in
reference to a sort of idea which I have sometimes
called the ground of that representation.” (Eco)
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Examples
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Extended semiotic ‘triangle’
Of a Person?
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Icons
(Meaning based
on similarity of
appearance)
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Index
• A sign related to an object
• Signifier <-> Signified
• Meaning based on cause and effect
relationships
• E.g. in a particular configuration, the letters
"E", "D" and "R" will form the sequence "R",
"E", "D".
• RED denotes a certain color, but neither the
letters individually nor their formal
combination into a word have anything to do
with redness.
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Symbol (meaning based on convention)
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Semiotic model
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Syntax
• Relation of signs to
each other in formal
structures
• … the term syntax is
also used to refer
directly to the rules and
principles that govern
the …
• But not the meaning or
the use!
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Semantics
• Relation between
signs and the
things to which
they refer; their
denotata
• Study of meaning
of … (anything?)
• Mainly need to
worry about
failures
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Pragmatics
• Relation of signs to their
impacts on those who use them
• the ways in which context
contributes to meaning,
conveying and use
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But in a digital world?
• Oh, and you thought I would answer all your
questions and doubts ;-)
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Cognitive Science
• Cognitive science is the interdisciplinary study of
the mind and intelligence
• It operates at the intersection of psychology,
philosophy, computer science, linguistics,
anthropology, and neuroscience.
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Mental Representation
• Thinking = representational structures +
procedures that operate on those structures.
• Data structures + mental representations+
algorithms +procedures= running programs
=thinking
• Methodological consequence: study the mind
by developing computer simulations of
thinking.
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What is an explanation of behavior?
– Programs that simulate cognitive processes
explain intelligent behavior by performing the
tasks whose performance they explain.
– Neurophysiological explanation is compatible
with computational explanation, but operates at a
different level.
– At the neural level, cognitive processes are
parallel, but at the symbolic level, the brain
behaves like a serial system.
– The human mind is an adaptive system, learning
to improve its performance in accomplishing its
goals.
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Nature of Expertise
• Manifests as cognition
– refers to an information processing view of an
individual's psychological functions
– Process of thought as ‘knowing’
• Indicates a level of knowing and action that is
above the non-expert
• Characterizing the expert versus the nonexpert (or specialist vs. non-) is very
important in information systems
• E.g. can a non-expert system be just as
easily used and exploited by an expert?
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Epistemology
• Theory of knowledge – and to do this
effectively you need to be concerned with:
– Truth, belief, and justification
– Means of production of knowledge
– Skepticism about different knowledge claims
• Recall the data-information-knowledge
ecosystem?
• Understanding what part this plays in your
modeling and architecture can be critical
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Classical view of knowledge
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Intuition
• This returns us to semiotics and to some
extent heuristics and abduction understanding without apparent effort
• Heuristics - experience-based techniques that
help in problem solving, learning and
discovery
• Abduction we’ve covered …
• So how do you eek out (technical term)
intuition?
– Use the cognitive process – drawing or mapping!
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Quality & Bias
from the Aerosol Parameter
FreeMind allows capturing
various relations between various
aspects of aerosol
measurements, algorithms,
conditions, validation, etc. The
“traditional” worksheets do not
support complex multiOntology
dimensional nature of the task
Metamodeling and Mindmaps
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Some tools
• For use case development – simple graphics
tools, e.g. graffle
• Mindmaps, e.g. Freemind
• For modeling (esp. UML):
– http://en.wikipedia.org/wiki/List_of_Unified_Model
ing_Language_tools
• For estimating information uncertainty, yes
some algorithms and software exist
• Concept, topic, subject maps!! (try searching)
– http://cmap.ihmc.us
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Information Models
• Conceptual models, sometimes called
domain models, are typically used to
explore domain concepts and often
created
– as part of initial requirements envisioning
efforts as they are used to explore the highlevel static business or science or medicine
structures and concepts
• Followed by logical and physical models
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Logical models
• A logical entity-relationship model is provable
in the mathematics of data science. Given the
current predominance of relational
databases, logical models generally conform
to relational theory.
• Thus a logical model contains only fully
normalized entities. Some of these may
represent logical domains rather than
potential physical tables.
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Information models - bad
• It's very easy to tell when a Web site you're trying to
navigate has no underlying Information Model. Here
are the tell-tale characteristics:
– You can't tell how to get from the home page to the
information you're looking for.
– You click on a promising link and are unpleasantly
surprised at what turns up.
– You keep drilling down into the information layer after
layer until you realize you're getting farther away from
your goal rather than closer.
– Every time you try to start over from the home page, you
end up in the same wrong place.
– You scroll through a long alphabetic list of all the articles
ever written on a particular subject with only the title to
guide you.
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Information models – good
• Oddly enough, you generally don't notice a wellconceived Information Model because it simply
doesn't get in the way of your search.
– On the home page, you notice promising links right away.
– Two or three clicks get you to exactly what you wanted.
– The information seems designed just for you because
someone has anticipated your needs.
– You can read a little or ask for more - the crossreferences are in the right places.
– Right away you feel that you're on familiar ground similar types of information start looking the same.
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Physical models
• A physical model is a single logical model
instantiated in a specific information system
(e.g., relational database, RDF/XML
document, etc.) in a specific installation.
• The physical model specifies implementation
details which may be features of a particular
product or version, as well as configuration
choices for that instance.
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Physical models
• E.g. for a database, these could include index
construction, alternate key declarations,
modes of referential integrity (declarative or
procedural), constraints, views, and physical
storage objects such as tablespaces.
• E.g. for RDF/XML, this would include
namespaces, declarative relations, etc.
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Object oriented design
• Object-oriented modeling is a formal way of
representing something in the real world
(draws from traditional set theory and
classification theory). Some basics to keep in
mind in object-oriented modeling are that:
– Instances are things.
– Properties are attributes.
– Relationships are pairs of attributes.
– Classes are types of things.
– Subclasses are subtypes of things.
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Object model
• Class: a means of grouping all the objects which
share the same set of attributes and methods.
• An object must belong to only one class as an
instance of that class (instance-of relationship).
• A class is similar to an abstract data type.
• Class hierarchy and inheritance: derive a new class
(subclass) from an existing class (superclass)
– subclass inherits all the attributes and methods of the
existing class and may have additional attributes and
methods
– single inheritance (class hierarchy) vs. multiple
inheritance (class lattice).
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Core object models consist of:
• object and object identifier: Any real world entity is
uniformly modeled as an object (associated with a
unique id: used to pinpoint an object to retrieve).
• attributes and methods: every object has a state
(the set of values for the attributes of the object) and
a behavior (the set of methods - program code which operate on the state of the object).
• the state and behavior encapsulated in an object
are accessed or invoked from outside the object.
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Information Modeling
• Conceptual
• Logical
• Physical
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For example for relational DBs
Feature
Conceptual Logical Physical
Entity Names
✓
✓
Entity Relationships ✓
✓
Attributes
✓
Primary Keys
✓
✓
Foreign Keys
✓
✓
Table Names
✓
Column Names
✓
Column Data Types
✓
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Steps in modeling
•
•
•
•
•
•
•
Identify objects (entity) and their types
Identify attributes
Apply naming conventions
Identify relationships
Apply model patterns (if known)
Assign relationships
Normalize to reduce redundancy (this is
called refactoring in software engineering)
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Exercise!
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Not just an isolated set of models
• Most important for handling errors,
evolution, extension, restriction, …
where to do that:
–To the physical model? NO
–To the logical model? MAYBE
–To the conceptual model? YES IF
POSSIBLE
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Not just an isolated set of models
• To relate to and/ or integrate with
other information models:
–General rule – integrate at the highest
level you can (i.e. more abstract)
–Remember the cognitive aspects!
Less detail is easier to understand
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Questions?
• About semiotics
• Cognitive science
• Information Modeling
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Reading for this week
• Is retrospective but … relates to a coming
assignment
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Assignment 2
• Assessing information uncertainty in different
aspects of the use case and determine
possible ways to condition the system to
reduce uncertainty in achieving the goals of
<your> use case from Assignment 1.
• Due on Feb 25th – write up and presentations
• Assignment 3 available Feb 25th due Mar
18th.
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What is next
• February 18 – no class, Tuesday follows
Monday schedule
• February 25 – Week 5 – Information
architectures theory and practice/ design
(Internet, Web, Grid, Cloud)
• March 4 – Week 6 – information Integration,
Life-cycle and Visualization
• Then spring break
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