Concept Maps & Knowledge Encoding

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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.

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Concept Maps & Knowledge Encoding

  1. 1. Concept Maps & Knowledge Encoding Putcha V. Narasimham Knowledge Enabler Systems Concept Maps & Knowledge Encoding 06 JAN 14 1
  2. 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 06 JAN 14 2
  3. 3. GRAPHIC REPRESENTATION OF CONCEPTS SECTION 1 Concept Maps & Knowledge Encoding 06 JAN 14 3
  4. 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 06 JAN 14 4
  5. 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 06 JAN 14 5
  6. 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 06 JAN 14 6
  7. 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 06 JAN 14 7
  8. 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 06 JAN 14 8
  9. 9. PRINCIPLES OF CONCEPT MODELING SECTION 2 Concept Maps & Knowledge Encoding 06 JAN 14 9
  10. 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 06 JAN 14 10
  11. 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 06 JAN 14 11
  12. 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 06 JAN 14 12
  13. 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 06 JAN 14 13
  14. 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 06 JAN 14 14
  15. 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 06 JAN 14 15
  16. 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 06 JAN 14 16
  17. 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 06 JAN 14 17
  18. 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 06 JAN 14 18
  19. 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 06 JAN 14 19
  20. 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? 06 JAN 14 20
  21. 21. TRIAD—AVIATION Aviation Concept Maps & Knowledge Encoding Passengers Planes Aviation  A relation between  Mode of travel by air and  The passengers & cargo 06 JAN 14 21
  22. 22. MORE THAN A TRIAD --- COMMERCE Concept Maps & Knowledge Encoding Goods / Services Seller Buyer Money 06 JAN 14 22
  23. 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 06 JAN 14 23
  24. 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 24
  25. 25. KNOWLEDGE ENCODING USING CONCEPT MAPS SECTION 3 Concept Maps & Knowledge Encoding 06 JAN 14 25
  26. 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 06 JAN 14 26
  27. 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 06 JAN 14 27
  28. 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 06 JAN 14 28
  29. 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 06 JAN 14 29
  30. 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 06 JAN 14 30
  31. 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 06 JAN 14 Concept Maps & Knowledge Encoding 31
  32. 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 32
  33. 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 06 JAN 14 33
  34. 34. FORMAL CONCEPT ANALYSIS & CONCEPT LATTICES APPENDIX Concept Maps & Knowledge Encoding 06 JAN 14 34
  35. 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 06 JAN 14 Concept Maps & Knowledge Encoding 35
  36. 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 06 JAN 14 Concept Maps & Knowledge Encoding 36
  37. 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 06 JAN 14 Concept Maps & Knowledge Encoding 37
  38. 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 06 JAN 14 Concept Maps & Knowledge Encoding 38
  39. 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 06 JAN 14 39

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