Concept Maps are very effective for language-free expression and communication of concepts visually. The fundamental structures, which are not all graphic, are also very elegant for encoding knowledge for machine processing. The building blocks of knowledge (Nodes and Links) are NOT sufficiently "expressive & precise". HyperPlex fills this need. See the PPT by that name in https://www.slideshare.net/putchavn Both the concepts are explained with examples. Good for general use and a prerequisite for knowing what is knowledge and how to represent it. Leave a comment.

1. Concept Maps &
Knowledge Encoding
Putcha V. Narasimham
Knowledge Enabler Systems
Concept Maps & Knowledge Encoding
06 JAN 14
1

2. KEY SECTIONS & TOPICS
Section 1
Section 3
Graphic Representation
Knowledge Encoding
 Concepts, Ovals
 Relations or Links, Arrow lines
Section 2
 Essential nature of concepts
Principles of Concept Modeling
 Monads, Dyads, Triads
 Examples: Mother, Child, Motherhood,
Impact, Commerce, System, Reasoning
Concept Maps & Knowledge Encoding
 Human & machine compatibility
 Concept expression and
communication
 Knowledge encoding and
processing, HyperPlex
Appendix: Formal Concept Analysis
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3. GRAPHIC REPRESENTATION OF CONCEPTS
SECTION 1
Concept Maps & Knowledge Encoding
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4. WHAT ARE CONCEPT MAPS
Concept 4
 Graphical or Visual
 Representations of
concepts (in ovals)
 And their relations
(arrow lines with labels)
Concept 1
Relation 3
Relation 2
Relation 1
Concept 3
Concept 2
Concept Maps & Knowledge Encoding
Relation 4
Concept 5
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5. CONCEPT MAPS WITH BLOCK ARROWS
Concept 1
Relation 1C
 Concepts in ovals
 And their relations
in Block Arrows
Concept 2
Concept Maps & Knowledge Encoding
Concept 4
Relation
4
Concept 3
Concept 5
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6. WHAT CONCEPT MAPS ARE NOT
 Topic Maps
 Very close;
 Mind Maps
 Hierarchy of concepts
 Associations are not labeled
 Ontology—very close
 Occurrences are added
 Biological or Artificial
Neural Networks
 ISO standard for knowledge
Interchange
Concept Maps & Knowledge Encoding
 Images of brain
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7. ORIGIN OF CONCEPT MAPS
 Invented in 1972
 By Novak & Cañas et al
 To enable children to
build concepts of
science
Concept Maps & Knowledge Encoding
 At Cornell University
 In collaboration with Florida
Institute for Human and Machine
Cognition
 http://cmap.ihmc.us/publications/resear
chpapers/originsofconceptmappingtool.
pdf
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8. ELEGANT FOR HUMANS & MACHINES
 Graphic Concept Maps
 Help clear
 Visualizing, expression
and communication
 By humans
Concept Maps & Knowledge Encoding
 More importantly
 The principles of Concept
Maps also help
 Precise representation of
knowledge
 For Machine Processing
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9. PRINCIPLES OF CONCEPT MODELING
SECTION 2
Concept Maps & Knowledge Encoding
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10. WHAT IS CONCEPT?
 An idea or a thought
 A set of related thoughts
 A concept is an idea,
something that is conceived in
the human mind--Wikipedia
Concept Maps & Knowledge Encoding
 These are colloquial
definitions or meanings
 See separate PPT for
Fundamentals of Thinking,
Brain, Mind &
Consciousness for details
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11. CONCEPTS ARE FORMED IN MIND ABOUT
Stand-alone
1. Entities, existing or
imagined objects
2. Phenomena
3. Sensations,
1….5
4. Emotions
5. Actions
6. Relations among 1….5
 What and where is MIND?
NOT discussed here
 We will discuss simple and
complex concepts using 1…5
and 6
Linking Concepts
Concept Maps & Knowledge Encoding
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12. STAND-ALONE CONCEPT --- MONAD
 It can be defined directly
without reference to any
other concept
 Monads
 Have their own properties
Mountain
 Self-sufficient
 Some nouns are monads
 And some are NOT
Concept Maps & Knowledge Encoding
Man
Neuron
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13. TWO FUNDAMENTAL BUILDING BLOCKS
Stand-alone
Concept
Monad
And
Mutually
Exclusive
Linking
Concept
 Defined in the previous slide
 Is also a concept
 Can be a Subject or Object
 Connects two concepts
 In Subject-Predicate-Object
structure of RDF standard
 Shows their relation
 Has many sub-types
 Has many sub-types
Concept Maps & Knowledge Encoding
 Also called predicate
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14. LINKING CONCEPT: A LABELED ARROW
Concept 2
 That is the form used in the
original proposal
 It is mistaken as a pointer
 Block arrow shows that
LINK is a solid, full-fledged
object
Concept Maps & Knowledge Encoding
Relation 1
Concept 1
Relation 2
Concept 3
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15. CONCEPT MAP OF CONCEP MAP
 Concept Map is a graphical representation of
 A compound concept
Is it a class or
composition
diagram?
Concept
 In terms of monads (or Nodes) & Links
 This is the basis of
 UML Class & Composition Diagrams
 Semantic Web &
 RDF Resource Description Framework
Concept Maps & Knowledge Encoding
Stand-alone
Concept
Monad
Linking
Concept
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16. RECIPROCAL RELATION
 Every BINARY relation has direction
 Every relation R1 has a reciprocal R2
 B is friend of A
 Different (asymmetrical)
 P is father of Q
 But Q cannot be father of P
Concept Maps & Knowledge Encoding
Has relation
R1 with
 A is friend of B &
Has relation
R2 with
 R1 & R2 may be the same (symmetrical)
Monad
Concept 1
Monad
Concept 2
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17. Mutually
dependent
DYAD—INVOLVES TWO CONCEPTS
 Neither can be defined by itself
 Child is NOT just small man (boy)
or woman (girl)
Mother
 Mother is NOT just any woman
 The two concepts arise together
Child
 Necessary for each other
Concept Maps & Knowledge Encoding
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18. DYADS—MOTHER & CHILD AND RELATION
Concept
Type
Mother is a woman who
Dyad
Gives birth to
Relation
Mother
A child (male or female) Dyad
Concept Maps & Knowledge Encoding
Child
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19. DYAD —IMPACT IS A PHENOMENON
 What happens when
 TWO bodies
Moving
Body 1
IMPACT
 At least one of which
is moving
 Come into contact
with the other
Concept Maps & Knowledge Encoding
Moving or
stationary Body 2
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20. TRIAD—RELATES TWO OR MORE CONCEPTS
Concept Maps & Knowledge Encoding
Motherhood
Mother
 A total concept of
a woman giving
birth to a child
and nurturing the
child
Child
 Motherhood
Is childhood a
reciprocal concept?
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21. TRIAD—AVIATION
Aviation
Concept Maps & Knowledge Encoding
Passengers
Planes
Aviation
 A relation
between
 Mode of travel by
air and
 The passengers &
cargo
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22. MORE THAN A TRIAD --- COMMERCE
Concept Maps & Knowledge Encoding
Goods /
Services
Seller
Buyer
Money
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23. Consists of
Consists of
Is a part of
Concept Maps & Knowledge Encoding
Elements
Environment
MORE THAN A TRIAD --- SYSTEM
Interrelated &
interacting
Is a part of
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24. HOW ABOUT “REASONING”
 This came up in the
discussions during
 The IEEE Seminar on
Semantic Networks
at Muffakhram Jah
College of
Engineering and
Technology, Hydrabad
 on 14 DEC 13
1. It falls under item 5 Actions
2. In humans, the action is mental
3. Expression of 2 is in some natural language
4. Reasoning involves application of rules of logic
5. To observations, statements, conclusions
6. It is more than a triad
7. Send your concept map to putchavn@yahoo.com
06 JAN 14
Concept Maps & Knowledge Encoding
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25. KNOWLEDGE ENCODING
USING CONCEPT MAPS
SECTION 3
Concept Maps & Knowledge Encoding
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26. THE ESSENTIAL NATURE OF CONCEPTS
 Essentially the Concept
Maps seem to exist in
 Human minds or
 Text & speech or
 Computers
 To represent & process
knowledge
Concept Maps & Knowledge Encoding
 The exact form
 Of concept maps in
 Humans & Machines varies
 But recognition of the
essential nature of
knowledge is profound
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27. HUMAN EXPRESSION & COMMUNICATION
 Expression is explicit statement
for communication
 Can be observed & interpreted
 If standard conventions,
grammar, lexicon are
followed
 Expressions can be physiological  The expressions clearly
communicate the concepts
changes, gestures, utterances,
speech, linguistic, mathematical,  Some negotiation may be
graphic..
necessary to disambiguate
Concept Maps & Knowledge Encoding
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28. HUMAN & MACHINE COMPATIBILITY
See
Data & Information:
Knuth’s Definitions
 Concept Maps
graphically represent
knowledge
 The explicit
 Using Nodes & Links
 Is also well-suited for
machine processing
 For use by humans
Concept Maps & Knowledge Encoding
 Information & data
 Relating to Nodes & Links
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29. CONCEPT MAPS FOR MACHINE PROCESSING
 The explicit Nodes
& Links of Concept
Maps
 Help knowledge
representation for
 Humans &
Machines
Concept Maps & Knowledge Encoding
 Information is in the microstructures of templates of
See
Nodes & Links
HyperPlex
 Data are in
 The populated Nodes & Links +
 The specific configurations of
populated Nodes and Links
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30. HIGH PRECISION QUERY-RESPONSE
 By defining
microstructures of
Nodes and Links
 All those details can be precisely
EVALUATED to generate specific
responses for action
 We can encode
 Not like thousands of hits of search
many more details
engines
of concepts
See
 See HyperPlex
HyperPlex
precisely
Concept Maps & Knowledge Encoding
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31. FORMAL CONCEPT ANALYSIS
 So far we have used  Rudolf Wille’s proposal of
linguistic description
Concept Lattices & Formal
of concepts
Concept Analysis in 1982 is
generally accepted as very
 Traditional Logic is
significant
applied to concept
analysis
 See the Appendix on this
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Concept Maps & Knowledge Encoding
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32. LINKS TO REFERENCES CITED
 http://www.slideshare.net
/putchavn/knuthsdefinitions-of-data-andinformation-04-mar13
 http://www.slideshare.net
/putchavn/hyper-plexhigh-precisionqueryresponse-knowledgerepository-pdf
06 JAN 14
Concept Maps & Knowledge Encoding
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33. SUMMARY & CONCLUSION
 Concept Maps are simple
and profound for
 Knowledge representation,
communication and
processing
 KIF, RDF & UNL are some
standards for encoding
knowledge in machines
 HyperPlex is our proposal
for high precision queryresponse
 Both in humans & machines
Concept Maps & Knowledge Encoding
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34. FORMAL CONCEPT ANALYSIS & CONCEPT LATTICES
APPENDIX
Concept Maps & Knowledge Encoding
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35. PRECISION OF CONCEPT (MATH)
 http://en.wikipedia.org/wiki/A
ccuracy_and_precision
 This is informative but applies
to quantitative measurement
 See the notes below
 This does not apply to concept
 Formal Concept Analysis is a
branch of mathematics
 Deals with concepts and
context in terms of Objects,
their attributes and
interrelations between them
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Concept Maps & Knowledge Encoding
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36. FORMAL CONCEPT ANALYSIS (INFORMATION SCIENCE)
a principled way of
deriving a concept
hierarchy or
formal ontology from
a collection
of objects and
their properties.
 Each concept in the hierarchy represents
the set of objects sharing the same values
for a certain set of properties; and
 each sub-concept in the hierarchy
contains a subset of the objects in the
concepts above it
 Fits with INTRA Class Diagram of OOAD
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37. TENTATIVE VIEW OF PRECISION OF CONCEPT
 It is best to apply Formal Concept
Analysis and Concept Lattices
 The class-subclass hierarchy of
OOAD is sound and applicable
 PRECISION of CONCEPT may be
taken as 1/n TENTATIVELY, where n
is the number of all sub-classes of
the concept class
Precision of a
concept is NOT
fineness of
concept but its
distinction from
similar concepts
of the class
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38. A COMPREHENSIVE AND EXCELLENT SOURCE
 INTRODUCTION TO FORMAL CONCEPT
ANALYSIS (2008)
 RADIM BˇELOHL´AVEK
 Department of Computer Science Palacky
University, Olomouc
 It is highly
mathematical
 Needs to be studied
for modeling and
software
development
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39. ORDERED SETS
 http://logcom.oxfor
djournals.org/conte
nt/12/1/137.short
 http://golem.ph.ute
xas.edu/category/2
013/09/formal_con
cept_analysis.html
Concept Maps & Knowledge Encoding
schroeder, ordered sets, first
chapter.pdf - Louisiana Tech
University
Schröder, Bernd S. W. 1966Ordered sets : an introduction
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