Foundations; semiotics, library, cognitive and social science and information modeling

1. Foundations; semiotics, library, cognitive and social science and information modeling Peter Fox Xinformatics – ITEC, CSCI, ERTH 4400/6400 Week 4, February 24, 2015

2. Contents • Review of last class, reading • Foundations; semiotics, cognitive science and information modeling • Assignment 2 • Next classes

3. Reading Review • Information entropy • Information Is Not Entropy, Information Is Not Uncertainty! • More on entropy • Context • Abductive reasoning

4. Semiotics • Also called semiotic studies or semiology, is the study of sign processes (semiosis), or signification and communication, signs and symbols

5. A sign (Peirce and Eco 1979) • “A sign stands for something to the idea which it produces or modifies.... • That for which it stands is called its object, that which it conveys, its meaning; and the idea which it gives rise, its interpretant • ....[the sign creates in the mind] an equivalent sign, or perhaps a more developed sign.” (Peirce) “That sign which it creates I call the interpretant of the first sign. This sign stands for something, its object. 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)

6. Examples

7. Extended semiotic ‘triangle’ Of a Person?

8. Icons (Meaning based on similarity of appearance)

9. 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.

10. Symbol (meaning based on convention)

11. Semiotic model

14. 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!

15. Semantics • Relation between signs and the things to which they refer; their denotata • Study of meaning of … (anything?) • Mainly need to worry about failures

16. Pragmatics • Relation of signs to their impacts on those who use them • the ways in which context contributes to meaning, conveying and use

17. But in a digital world? • Oh, and you thought I would answer all your questions and doubts ;-)

19. 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.

20. 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.

21. 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.

22. 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?

23. 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

24. Classical view of knowledge

25. 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!

26. Quality & Bias 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 from the Aerosol Parameter Ontology

27. Metamodeling and Mindmaps

28. 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_Modeling_Language_tools • For estimating information uncertainty, yes some algorithms and software exist • Concept, topic, subject maps!! (try searching) • http://cmap.ihmc.us

29. 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 models

30. 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.

31. 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.

32. 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.

33. 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.

34. Physical models • E.g. for a database, these could include indexconstruction, 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.

35. 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.

36. 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).

37. 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.

38. Information Modeling • Conceptual • Logical • Physical

39. 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     ✓

40. 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)

41. Exercise!

42. 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

43. 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

44. Questions? • About semiotics • Cognitive science • Information Modeling

45. Reading for this week • Is retrospective but … relates to a coming assignment

46. 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