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Peter Fox

Xinformatics – ITEC, CSCI, ERTH 4400/6400

Week 4, February 24, 2015

Foundations; semiotics, library, cognitive and social

science and information modeling

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)

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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)

Examples

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Extended semiotic ‘triangle’

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Of a Person?

Icons

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(Meaning based on similarity of appearance)

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! 14

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. 20

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. 21

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 non-expert (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 Ontology

FreeMind allows capturing various relations between various aspects of aerosol measurements, algorithms, conditions, validation, etc. The “traditional” worksheets do not support complex multi-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 high-level static business or science or medicine structures and concepts

• Followed by logical and physical models29

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 well-

conceived 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 cross-

references 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

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Feature Conceptual Logical PhysicalEntity Names ✓ ✓  Entity Relationships ✓ ✓  Attributes   ✓  Primary Keys  ✓ ✓Foreign Keys  ✓ ✓Table Names    ✓Column Names     ✓Column Data Types     ✓

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 Mar 10th

• Assignment 3 available Mar 3rd due Mar 17th.47

What is next• March 3 – Week 6 – Information architectures

theory and practice/ design (Internet, Web, Grid, Cloud)

• March 10 – TBD (guest lecture)

• March 17 – Class presentations

• Spring break

• March 31 – Class presenations

• April 7– Week 9 – information Integration, Life-cycle and Visualization

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