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Knowledge Processing <ul><ul><ul><ul><ul><li>Computer Science Department </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul...
Acknowledgements Some of the material in these slides was developed for a lecture series sponsored by the  European Commun...
Use and Distribution of these Slides <ul><li>These slides are primarily intended for the students in classes I teach. In s...
Overview Knowledge Processing <ul><li>Motivation </li></ul><ul><li>Objectives </li></ul><ul><li>Chapter Introduction </li>...
Bridge-In Franz Kurfess: Knowledge Processing
Pre-Test Franz Kurfess: Knowledge Processing
Motivation <ul><li>the  representation and manipulation of knowledge  has been essential for the development of humanity a...
Objectives <ul><li>be familiar with the commonly used  knowledge representation  and  reasoning methods </li></ul><ul><li>...
Chapter Introduction <ul><li>Knowledge Processing as Core AI Paradigm </li></ul><ul><li>Relationship to KM </li></ul><ul><...
Relationship to KM Franz Kurfess: Knowledge Processing KP/AI KM <ul><ul><li>representation methods suited for KP by comput...
Knowledge Processes Human knowledge and networking Information databases and technical networking [Skyrme 1998] Franz Kurf...
Knowledge Cycles [Skyrme 1998] Franz Kurfess: Knowledge Processing Create Product/ Process Knowledge Repository Codify Emb...
Knowledge Representation <ul><li>Types of Knowledge </li></ul><ul><ul><li>Factual Knowledge </li></ul></ul><ul><ul><li>Sub...
Types of Knowledge <ul><li>The field that investigates knowledge types and similar questions is  epistemology </li></ul><u...
Factual Knowledge <ul><li>verifiable </li></ul><ul><ul><li>through experiments, formal methods, sometimes commonsense reas...
Subjective Knowledge <ul><li>relies on individuals </li></ul><ul><ul><li>insight, experience </li></ul></ul><ul><li>possib...
Heuristic Knowledge <ul><li>based on rules or guidelines that frequently help solving problems </li></ul><ul><li>often der...
Deep and Shallow Knowledge <ul><li>deep knowledge enables explanations and plausibility considerations  </li></ul><ul><ul>...
Other Types of Knowledge <ul><li>procedural knowledge </li></ul><ul><ul><li>knowing how to do something </li></ul></ul><ul...
Roles of Knowledge Representation (KR) <ul><li>KR as Surrogate </li></ul><ul><li>Ontological Commitments </li></ul><ul><li...
KR as Surrogate <ul><li>a substitute for the thing itself </li></ul><ul><li>enables an entity to determine consequences by...
Surrogate Aspects <ul><li>Identity </li></ul><ul><ul><li>correspondence between the surrogate and the intended referent in...
Surrogate Consequences <ul><li>perfect representation is impossible </li></ul><ul><ul><li>the only completely accurate rep...
Ontological Commitments <ul><li>terms (formalisms, methods, constructs) used to represent the world </li></ul><ul><li>by s...
Ontological Commitments Examples <ul><li>logic </li></ul><ul><ul><li>views the world in terms of individual entities and r...
KR and Reasoning <ul><li>a knowledge representation indicates an initial conception of intelligent inference </li></ul><ul...
KR for Reasoning <ul><li>a representation suggests answers to fundamental questions concerning reasoning: </li></ul><ul><u...
KR and Computation <ul><li>from the AI perspective, reasoning is a computational process </li></ul><ul><ul><li>machines ar...
Computational Medium <ul><li>computational environment for the reasoning process </li></ul><ul><li>reasonably efficient </...
KR for Human Expression <ul><li>a knowledge representation or expression method that can be used by humans to make stateme...
Knowledge Acquisition <ul><li>Incorporating Knowledge into a Repository </li></ul><ul><ul><li>human mind </li></ul></ul><u...
Acquisition of Knowledge <ul><li>Published Sources </li></ul><ul><ul><li>Physical Media </li></ul></ul><ul><ul><li>Digital...
Knowledge Elicitation <ul><li>knowledge is already present in humans, but needs to be converted into a form suitable for c...
Machine Learning <ul><li>extraction of higher-level information from raw data </li></ul><ul><li>based on statistical metho...
Knowledge Fusion <ul><li>integration of human-generated and machine-generated knowledge </li></ul><ul><ul><li>sometimes al...
Knowledge Representation Mechanisms <ul><li>Logic </li></ul><ul><li>Rules </li></ul><ul><li>Semantic Networks </li></ul><u...
Logic <ul><li>syntax: well-formed formula </li></ul><ul><ul><li>a formula or sentence often expresses a fact or a statemen...
KR Roles and Logic <ul><li>surrogate </li></ul><ul><ul><li>very expressive, not very suitable for many types of knowledge ...
Rules <ul><li>syntax:  if … then … </li></ul><ul><li>semantics: interpretation of rules </li></ul><ul><ul><li>usually reas...
KR Roles and Rules <ul><li>surrogate </li></ul><ul><ul><li>reasonably expressive, suitable for some types of knowledge </l...
Semantic Networks <ul><li>syntax: graphs, possibly with some restrictions and enhancements </li></ul><ul><li>semantics: in...
KR Roles and Semantic Nets <ul><li>surrogate </li></ul><ul><ul><li>limited to reasonably expressiveness, suitable for some...
Frames, Scripts <ul><li>syntax: templates with slots and fillers </li></ul><ul><li>semantics: interpretation of the slots/...
KR Roles and Frames <ul><li>surrogate </li></ul><ul><ul><li>suitable for well-structured knowledge </li></ul></ul><ul><li>...
Knowledge Manipulation <ul><li>Reasoning </li></ul><ul><li>KQML </li></ul>Franz Kurfess: Knowledge Processing
Reasoning <ul><li>generation of new knowledge items from existing ones </li></ul><ul><li>frequently identified with  logic...
KQML <ul><li>stands for Knowledge Query and Manipulation Language </li></ul><ul><li>language and protocol for exchanging i...
KQML Performatives <ul><li>basic query performatives  </li></ul><ul><ul><li>evaluate, ask-if, ask-about, ask-one, ask-all ...
KQML Example 1 <ul><li>query </li></ul><ul><li>(ask-if  </li></ul><ul><li>:sender A  </li></ul><ul><li>:receiver B  </li><...
KQML Example 2 <ul><li>query </li></ul><ul><li>(stream-about :language KIF :ontology motors `:reply-with q1  </li></ul><ul...
Post-Test Franz Kurfess: Knowledge Processing
Evaluation <ul><li>Criteria </li></ul>Franz Kurfess: Knowledge Processing
KP/KM Activity <ul><li>select a domain that requires significant human involvement for dealing with knowledge </li></ul><u...
KP/KM Activity Outcomes 2007 <ul><li>Images with Metadata </li></ul><ul><li>Extracting contact information from text </li>...
Important Concepts and Terms <ul><li>automated reasoning </li></ul><ul><li>belief network </li></ul><ul><li>cognitive scie...
Summary Knowledge Processing <ul><li>there are different types of knowledge </li></ul><ul><li>knowledge acquisition can be...
Franz Kurfess: Knowledge Processing
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Transcript of "Knowledge processing"

  1. 1. Knowledge Processing <ul><ul><ul><ul><ul><li>Computer Science Department </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>California Polytechnic State University </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>San Luis Obispo, CA, U.S.A. </li></ul></ul></ul></ul></ul>Franz J. Kurfess
  2. 2. Acknowledgements Some of the material in these slides was developed for a lecture series sponsored by the European Community under the BPD program with Vilnius University as host institution
  3. 3. Use and Distribution of these Slides <ul><li>These slides are primarily intended for the students in classes I teach. In some cases, I only make PDF versions publicly available. If you would like to get a copy of the originals (Apple KeyNote or Microsoft PowerPoint), please contact me via email at [email_address] . I hereby grant permission to use them in educational settings. If you do so, it would be nice to send me an email about it. If you’re considering using them in a commercial environment, please contact me first. </li></ul>Franz Kurfess: Knowledge Processing
  4. 4. Overview Knowledge Processing <ul><li>Motivation </li></ul><ul><li>Objectives </li></ul><ul><li>Chapter Introduction </li></ul><ul><ul><li>Knowledge Processing as Core AI Paradigm </li></ul></ul><ul><ul><li>Relationship to KM </li></ul></ul><ul><ul><li>Terminology </li></ul></ul><ul><li>Knowledge Acquisition </li></ul><ul><ul><li>Knowledge Elicitation </li></ul></ul><ul><ul><li>Machine Learning </li></ul></ul><ul><ul><li>Knowledge Representation </li></ul></ul><ul><ul><ul><li>Logic </li></ul></ul></ul><ul><ul><ul><li>Rules </li></ul></ul></ul><ul><ul><ul><li>Semantic Networks </li></ul></ul></ul><ul><ul><ul><li>Frames, Scripts </li></ul></ul></ul><ul><ul><li>Knowledge Manipulation </li></ul></ul><ul><ul><ul><li>Reasoning </li></ul></ul></ul><ul><ul><ul><li>KQML </li></ul></ul></ul><ul><li>Important Concepts and Terms </li></ul><ul><li>Chapter Summary </li></ul>Franz Kurfess: Knowledge Processing
  5. 5. Bridge-In Franz Kurfess: Knowledge Processing
  6. 6. Pre-Test Franz Kurfess: Knowledge Processing
  7. 7. Motivation <ul><li>the representation and manipulation of knowledge has been essential for the development of humanity as we know it </li></ul><ul><li>the use of formal methods and support from machines can improve our knowledge representation and reasoning abilities </li></ul><ul><li>intelligent reasoning is a very complex phenomenon , and may have to be described in a variety of ways </li></ul><ul><li>a basic understanding of knowledge representation and reasoning is important for the organization and management of knowledge </li></ul>Franz Kurfess: Knowledge Processing
  8. 8. Objectives <ul><li>be familiar with the commonly used knowledge representation and reasoning methods </li></ul><ul><li>understand different roles and perspectives of knowledge representation and reasoning methods </li></ul><ul><li>examine the suitability of knowledge representations for specific tasks </li></ul><ul><li>evaluate the representation methods and reasoning mechanisms employed in computer-based systems </li></ul>Franz Kurfess: Knowledge Processing
  9. 9. Chapter Introduction <ul><li>Knowledge Processing as Core AI Paradigm </li></ul><ul><li>Relationship to KM </li></ul><ul><li>Terminology </li></ul>Franz Kurfess: Knowledge Processing
  10. 10. Relationship to KM Franz Kurfess: Knowledge Processing KP/AI KM <ul><ul><li>representation methods suited for KP by computers </li></ul></ul>representation of knowledge in formats suitable for humans <ul><ul><li>reasoning performed by computers </li></ul></ul>essential reasoning performed by humans <ul><ul><li>mostly limited to symbol manipulation </li></ul></ul>support from computers <ul><ul><li>very demanding in terms of computational power </li></ul></ul>emphasis often on documents <ul><ul><li>can be used for “grounded” systems </li></ul></ul>larger granularity <ul><ul><li>interpretation (“meaning”) typically left to humans </li></ul></ul>mainly intended for human use
  11. 11. Knowledge Processes Human knowledge and networking Information databases and technical networking [Skyrme 1998] Franz Kurfess: Knowledge Processing Chaotic knowledge processes Systematic information and knowledge processes
  12. 12. Knowledge Cycles [Skyrme 1998] Franz Kurfess: Knowledge Processing Create Product/ Process Knowledge Repository Codify Embed Diffuse Identify Classify Access Use/Exploit Collect Organize/ Store Share/ Disseminate
  13. 13. Knowledge Representation <ul><li>Types of Knowledge </li></ul><ul><ul><li>Factual Knowledge </li></ul></ul><ul><ul><li>Subjective Knowledge </li></ul></ul><ul><ul><li>Heuristic Knowledge </li></ul></ul><ul><ul><li>Deep and Shallow Knowledge </li></ul></ul><ul><li>Knowledge Representation Methods </li></ul><ul><ul><li>Rules, Frames, Semantic Networks </li></ul></ul><ul><ul><li>Blackboard Representations </li></ul></ul><ul><ul><li>Object-based Representations </li></ul></ul><ul><ul><li>Case-Based Reasoning </li></ul></ul><ul><li>Knowledge Representation Tools </li></ul>Franz Kurfess: Knowledge Processing
  14. 14. Types of Knowledge <ul><li>The field that investigates knowledge types and similar questions is epistemology </li></ul><ul><li>Factual Knowledge </li></ul><ul><li>Subjective Knowledge </li></ul><ul><li>Heuristic Knowledge </li></ul><ul><li>Deep and Shallow Knowledge </li></ul><ul><li>Other Types of Knowledge </li></ul>Franz Kurfess: Knowledge Processing
  15. 15. Factual Knowledge <ul><li>verifiable </li></ul><ul><ul><li>through experiments, formal methods, sometimes commonsense reasoning </li></ul></ul><ul><ul><li>often created by authoritative sources </li></ul></ul><ul><li>typically not under dispute in the domain community </li></ul><ul><li>often incorporated into reference works, textbooks, domain standards </li></ul>Franz Kurfess: Knowledge Processing
  16. 16. Subjective Knowledge <ul><li>relies on individuals </li></ul><ul><ul><li>insight, experience </li></ul></ul><ul><li>possibly subject to interpretation </li></ul><ul><li>more difficult to verify </li></ul><ul><ul><li>especially if the individuals possessing the knowledge are not cooperative </li></ul></ul><ul><li>different from belief </li></ul><ul><ul><li>both are subjective, but beliefs are not verifiable </li></ul></ul>Franz Kurfess: Knowledge Processing
  17. 17. Heuristic Knowledge <ul><li>based on rules or guidelines that frequently help solving problems </li></ul><ul><li>often derived from practical experience working in a domain </li></ul><ul><ul><li>as opposed to theoretical insights gained from deep thoughts about a topic </li></ul></ul><ul><li>verifiable through experiments </li></ul>Franz Kurfess: Knowledge Processing
  18. 18. Deep and Shallow Knowledge <ul><li>deep knowledge enables explanations and plausibility considerations </li></ul><ul><ul><li>possibly including formal proofs </li></ul></ul><ul><li>shallow knowledge may be sufficient to answer immediate questions, but not for explanations </li></ul><ul><ul><li>heuristics are often an example of shallow knowledge </li></ul></ul>Franz Kurfess: Knowledge Processing
  19. 19. Other Types of Knowledge <ul><li>procedural knowledge </li></ul><ul><ul><li>knowing how to do something </li></ul></ul><ul><li>declarative knowledge </li></ul><ul><ul><li>expressed through statements that can be shown to be true or false </li></ul></ul><ul><ul><li>prototypical example is mathematical logic </li></ul></ul><ul><li>tacit knowledge </li></ul><ul><ul><li>implicit, unconscious knowledge that can be difficult to express in words or other representations </li></ul></ul><ul><li>a priori knowledge </li></ul><ul><ul><li>independent on experience or empirical evidence </li></ul></ul><ul><ul><li>e.g. “everybody born before 1983 is older than 20 years” </li></ul></ul><ul><li>a posteriori knowledge </li></ul><ul><ul><li>dependent of experience or empirical evidence </li></ul></ul><ul><ul><li>e.g. “X was born in 1983” </li></ul></ul>Franz Kurfess: Knowledge Processing
  20. 20. Roles of Knowledge Representation (KR) <ul><li>KR as Surrogate </li></ul><ul><li>Ontological Commitments </li></ul><ul><li>Fragmentary Theory of Intelligent Reasoning </li></ul><ul><li>Medium for Computation </li></ul><ul><li>Medium for Human Expression </li></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  21. 21. KR as Surrogate <ul><li>a substitute for the thing itself </li></ul><ul><li>enables an entity to determine consequences by thinking rather than acting </li></ul><ul><ul><li>reasoning about the world through operations on the representation </li></ul></ul><ul><li>reasoning or thinking are inherently internal processes </li></ul><ul><li>the objects of reasoning are mostly external entities (“things”) </li></ul><ul><ul><li>some objects of reasoning are internal, e.g. concepts, feelings, ... </li></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  22. 22. Surrogate Aspects <ul><li>Identity </li></ul><ul><ul><li>correspondence between the surrogate and the intended referent in the real world </li></ul></ul><ul><li>Fidelity </li></ul><ul><ul><li>Incompleteness </li></ul></ul><ul><ul><li>Incorrectness </li></ul></ul><ul><ul><li>Adequacy </li></ul></ul><ul><ul><ul><li>Task </li></ul></ul></ul><ul><ul><ul><li>User </li></ul></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  23. 23. Surrogate Consequences <ul><li>perfect representation is impossible </li></ul><ul><ul><li>the only completely accurate representation of an object is the object itself </li></ul></ul><ul><li>incorrect reasoning is inevitable </li></ul><ul><ul><li>if there are some flaws in the world model, even a perfectly sound reasoning mechanism will come to incorrect conclusions </li></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  24. 24. Ontological Commitments <ul><li>terms (formalisms, methods, constructs) used to represent the world </li></ul><ul><li>by selecting a representation a decision is made about how and what to see in the world </li></ul><ul><ul><li>like a set of glasses that offer a sharp focus on part of the world, at the expense of blurring other parts </li></ul></ul><ul><ul><li>necessary because of the inevitable imperfections of representations </li></ul></ul><ul><ul><li>useful to concentrate on relevant aspects </li></ul></ul><ul><ul><li>pragmatic because of feasibility constraints </li></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  25. 25. Ontological Commitments Examples <ul><li>logic </li></ul><ul><ul><li>views the world in terms of individual entities and relationships between the entities </li></ul></ul><ul><ul><li>enforces the assignment of truth values to statements </li></ul></ul><ul><li>rules </li></ul><ul><ul><li>entities and their relationships expressed through rules </li></ul></ul><ul><li>frames </li></ul><ul><ul><li>prototypical objects </li></ul></ul><ul><li>semantic nets </li></ul><ul><ul><li>entities and relationships displayed as a graph </li></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  26. 26. KR and Reasoning <ul><li>a knowledge representation indicates an initial conception of intelligent inference </li></ul><ul><ul><li>often reasoning methods are associated with representation technique </li></ul></ul><ul><ul><ul><li>first order predicate logic and deduction </li></ul></ul></ul><ul><ul><ul><li>rules and modus ponens </li></ul></ul></ul><ul><ul><li>the association is often implicit </li></ul></ul><ul><ul><li>the underlying inference theory is fragmentary </li></ul></ul><ul><ul><ul><li>the representation covers only parts of the association </li></ul></ul></ul><ul><ul><ul><li>intelligent reasoning is a complex and multi-faceted phenomenon </li></ul></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  27. 27. KR for Reasoning <ul><li>a representation suggests answers to fundamental questions concerning reasoning: </li></ul><ul><ul><li>What does it mean to reason intelligently? </li></ul></ul><ul><ul><ul><li>implied reasoning method </li></ul></ul></ul><ul><ul><li>What can possibly be inferred from what we know? </li></ul></ul><ul><ul><ul><li>possible conclusions </li></ul></ul></ul><ul><ul><li>What should be inferred from what we know? </li></ul></ul><ul><ul><ul><li>recommended conclusions </li></ul></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  28. 28. KR and Computation <ul><li>from the AI perspective, reasoning is a computational process </li></ul><ul><ul><li>machines are used as reasoning tools </li></ul></ul><ul><li>without efficient ways of implementing such computational process, it is practically useless </li></ul><ul><ul><li>e.g. Turing machine </li></ul></ul><ul><li>most representation and reasoning mechanisms are modified for efficient computation </li></ul><ul><ul><li>e.g. Prolog vs. predicate logic </li></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  29. 29. Computational Medium <ul><li>computational environment for the reasoning process </li></ul><ul><li>reasonably efficient </li></ul><ul><li>organization and representation of knowledge so that reasoning is facilitated </li></ul><ul><li>may come at the expense of understandability by humans </li></ul><ul><ul><li>unexpected outcomes of the reasoning process </li></ul></ul><ul><ul><li>lack of transparency of the reasoning process </li></ul></ul><ul><ul><ul><li>even though the outcome “makes sense”, it is unclear how it was achieved </li></ul></ul></ul>Franz Kurfess: Knowledge Processing
  30. 30. KR for Human Expression <ul><li>a knowledge representation or expression method that can be used by humans to make statements about the world </li></ul><ul><ul><li>expression of knowledge </li></ul></ul><ul><ul><ul><li>expressiveness, generality, preciseness </li></ul></ul></ul><ul><ul><li>communication of knowledge </li></ul></ul><ul><ul><ul><li>among humans </li></ul></ul></ul><ul><ul><ul><li>between humans and machines </li></ul></ul></ul><ul><ul><ul><li>among machines </li></ul></ul></ul><ul><ul><li>typically based on natural language </li></ul></ul><ul><ul><li>often at the expense of efficient computability </li></ul></ul>[Davis, Shrobe, Szolovits, 1993] Franz Kurfess: Knowledge Processing
  31. 31. Knowledge Acquisition <ul><li>Incorporating Knowledge into a Repository </li></ul><ul><ul><li>human mind </li></ul></ul><ul><ul><li>human-readable </li></ul></ul><ul><ul><ul><li>book, magazine, etc </li></ul></ul></ul><ul><ul><li>computer-based </li></ul></ul><ul><li>Knowledge Acquisition Types </li></ul><ul><ul><li>Knowledge Elicitation </li></ul></ul><ul><ul><ul><li>conversion of human knowledge into a format suitable for computers </li></ul></ul></ul><ul><ul><li>Machine Learning </li></ul></ul><ul><ul><ul><li>extraction of knowledge from data </li></ul></ul></ul>Franz Kurfess: Knowledge Processing
  32. 32. Acquisition of Knowledge <ul><li>Published Sources </li></ul><ul><ul><li>Physical Media </li></ul></ul><ul><ul><li>Digital Media </li></ul></ul><ul><li>People as Sources </li></ul><ul><ul><li>Interviews </li></ul></ul><ul><ul><li>Questionnaires </li></ul></ul><ul><ul><li>Formal Techniques </li></ul></ul><ul><ul><li>Observation Techniques </li></ul></ul><ul><li>Knowledge Acquisition Tools </li></ul><ul><ul><li>automatic </li></ul></ul><ul><ul><li>interactive </li></ul></ul>Franz Kurfess: Knowledge Processing
  33. 33. Knowledge Elicitation <ul><li>knowledge is already present in humans, but needs to be converted into a form suitable for computer use </li></ul><ul><li>requires the collaboration between a domain expert and a knowledge engineer </li></ul><ul><ul><li>domain expert has the domain knowledge, but not necessarily the skills to convert it into computer-usable form </li></ul></ul><ul><ul><li>knowledge engineer assists with this conversion </li></ul></ul><ul><ul><li>this can be a very lengthy, cumbersome and error-prone process </li></ul></ul>Franz Kurfess: Knowledge Processing
  34. 34. Machine Learning <ul><li>extraction of higher-level information from raw data </li></ul><ul><li>based on statistical methods </li></ul><ul><li>results are not necessarily in a format that is easy for humans to use </li></ul><ul><li>the organization of the gained knowledge is often far from intuitive for humans </li></ul><ul><li>examples </li></ul><ul><ul><li>decision trees </li></ul></ul><ul><ul><li>rule extraction from neural networks </li></ul></ul>Franz Kurfess: Knowledge Processing
  35. 35. Knowledge Fusion <ul><li>integration of human-generated and machine-generated knowledge </li></ul><ul><ul><li>sometimes also used to indicate the integration of knowledge from different sources, or in different formats </li></ul></ul><ul><li>can be both conceptually and technically very difficult </li></ul><ul><ul><li>different “spirit” of the knowledge representation used </li></ul></ul><ul><ul><li>different terminology </li></ul></ul><ul><ul><li>different categorization criteria </li></ul></ul><ul><ul><li>different representation and processing mechanisms </li></ul></ul><ul><ul><ul><li>e.g. graph-oriented vs. rules vs. data base-oriented </li></ul></ul></ul>Franz Kurfess: Knowledge Processing
  36. 36. Knowledge Representation Mechanisms <ul><li>Logic </li></ul><ul><li>Rules </li></ul><ul><li>Semantic Networks </li></ul><ul><li>Frames, Scripts </li></ul>Franz Kurfess: Knowledge Processing
  37. 37. Logic <ul><li>syntax: well-formed formula </li></ul><ul><ul><li>a formula or sentence often expresses a fact or a statement </li></ul></ul><ul><li>semantics: interpretation of the formula </li></ul><ul><ul><li>“meaning” is associated with formulae </li></ul></ul><ul><ul><li>often compositional semantics </li></ul></ul><ul><li>axioms as basic assumptions </li></ul><ul><ul><li>generally accepted within the domain </li></ul></ul><ul><li>inference rules for deriving new formulae from existing ones </li></ul>Franz Kurfess: Knowledge Processing
  38. 38. KR Roles and Logic <ul><li>surrogate </li></ul><ul><ul><li>very expressive, not very suitable for many types of knowledge </li></ul></ul><ul><li>ontological commitments </li></ul><ul><ul><li>objects, relationships, terms, logic operators </li></ul></ul><ul><li>fragmentary theory of intelligent reasoning </li></ul><ul><ul><li>deduction, other logical calculi </li></ul></ul><ul><li>medium for computation </li></ul><ul><ul><li>yes, but not very efficient </li></ul></ul><ul><li>medium for human expression </li></ul><ul><ul><li>only for experts </li></ul></ul>Franz Kurfess: Knowledge Processing
  39. 39. Rules <ul><li>syntax: if … then … </li></ul><ul><li>semantics: interpretation of rules </li></ul><ul><ul><li>usually reasonably understandable </li></ul></ul><ul><li>initial rules and facts </li></ul><ul><ul><li>often capture basic assumptions and provide initial conditions </li></ul></ul><ul><li>generation of new facts, application to existing rules </li></ul><ul><ul><li>forward reasoning: starting from known facts </li></ul></ul><ul><ul><li>backward reasoning: starting from a hypothesis </li></ul></ul>Franz Kurfess: Knowledge Processing
  40. 40. KR Roles and Rules <ul><li>surrogate </li></ul><ul><ul><li>reasonably expressive, suitable for some types of knowledge </li></ul></ul><ul><li>ontological commitments </li></ul><ul><ul><li>objects, rules, facts </li></ul></ul><ul><li>fragmentary theory of intelligent reasoning </li></ul><ul><ul><li>modus ponens, matching, sometimes augmented by probabilistic mechanisms </li></ul></ul><ul><li>medium for computation </li></ul><ul><ul><li>reasonably efficient </li></ul></ul><ul><li>medium for human expression </li></ul><ul><ul><li>mainly for experts </li></ul></ul>Franz Kurfess: Knowledge Processing
  41. 41. Semantic Networks <ul><li>syntax: graphs, possibly with some restrictions and enhancements </li></ul><ul><li>semantics: interpretation of the graphs </li></ul><ul><li>initial state of the graph </li></ul><ul><li>propagation of activity, inferences based on link types </li></ul>Franz Kurfess: Knowledge Processing
  42. 42. KR Roles and Semantic Nets <ul><li>surrogate </li></ul><ul><ul><li>limited to reasonably expressiveness, suitable for some types of knowledge </li></ul></ul><ul><li>ontological commitments </li></ul><ul><ul><li>nodes (objects, concepts), links (relations) </li></ul></ul><ul><li>fragmentary theory of intelligent reasoning </li></ul><ul><ul><li>conclusions based on properties of objects and their relationships with other objects </li></ul></ul><ul><li>medium for computation </li></ul><ul><ul><li>reasonably efficient for some types of reasoning </li></ul></ul><ul><li>medium for human expression </li></ul><ul><ul><li>easy to visualize </li></ul></ul>Franz Kurfess: Knowledge Processing
  43. 43. Frames, Scripts <ul><li>syntax: templates with slots and fillers </li></ul><ul><li>semantics: interpretation of the slots/filler values </li></ul><ul><li>initial values for slots in frames </li></ul><ul><li>complex matching of related frames </li></ul>Franz Kurfess: Knowledge Processing
  44. 44. KR Roles and Frames <ul><li>surrogate </li></ul><ul><ul><li>suitable for well-structured knowledge </li></ul></ul><ul><li>ontological commitments </li></ul><ul><ul><li>templates, situations, properties, methods </li></ul></ul><ul><li>fragmentary theory of intelligent reasoning </li></ul><ul><ul><li>conclusions are based on relationships between frames </li></ul></ul><ul><li>medium for computation </li></ul><ul><ul><li>ok for some problem types </li></ul></ul><ul><li>medium for human expression </li></ul><ul><ul><li>ok, but sometimes too formulaic </li></ul></ul>Franz Kurfess: Knowledge Processing
  45. 45. Knowledge Manipulation <ul><li>Reasoning </li></ul><ul><li>KQML </li></ul>Franz Kurfess: Knowledge Processing
  46. 46. Reasoning <ul><li>generation of new knowledge items from existing ones </li></ul><ul><li>frequently identified with logical reasoning </li></ul><ul><ul><li>strong formal foundation </li></ul></ul><ul><ul><li>very restricted methods for generating conclusions </li></ul></ul><ul><li>sometimes expanded to capture various ways to draw conclusions based on methods employed by humans </li></ul><ul><li>requires a formal specification or implementation to be used with computers </li></ul>Franz Kurfess: Knowledge Processing
  47. 47. KQML <ul><li>stands for Knowledge Query and Manipulation Language </li></ul><ul><li>language and protocol for exchanging information and knowledge </li></ul>Franz Kurfess: Knowledge Processing
  48. 48. KQML Performatives <ul><li>basic query performatives </li></ul><ul><ul><li>evaluate, ask-if, ask-about, ask-one, ask-all </li></ul></ul><ul><li>multi-response query performatives </li></ul><ul><ul><li>stream-about, stream-all </li></ul></ul><ul><li>response performatives </li></ul><ul><ul><li>reply, sorry </li></ul></ul><ul><li>generic informational performatives </li></ul><ul><ul><li>tell, achieve, deny, untell, unachieve </li></ul></ul><ul><li>generator performatives </li></ul><ul><ul><li>standby, ready, next, rest, discard, generator </li></ul></ul><ul><li>capability-definition performatives </li></ul><ul><ul><li>advertise, subscribe, monitor, import, export </li></ul></ul><ul><li>networking performatives </li></ul><ul><ul><li>register, unregister, forward, broadcast, route. </li></ul></ul>Franz Kurfess: Knowledge Processing
  49. 49. KQML Example 1 <ul><li>query </li></ul><ul><li>(ask-if </li></ul><ul><li>:sender A </li></ul><ul><li>:receiver B </li></ul><ul><li>:language Prolog </li></ul><ul><li>:ontology foo </li></ul><ul><li>:reply-with id1 </li></ul><ul><li>:content ``bar(a,b)'' ) </li></ul><ul><li>reply </li></ul><ul><li>(sorry </li></ul><ul><li>:sender B </li></ul><ul><li>:receiver A </li></ul><ul><li>:in-reply-to id1 </li></ul><ul><li>:reply-with id2 ) </li></ul>agent A (:sender) is querying the agent B (:receiver), in Prolog (:language) about the truth status of ``bar(a,b)'' (:content) Franz Kurfess: Knowledge Processing
  50. 50. KQML Example 2 <ul><li>query </li></ul><ul><li>(stream-about :language KIF :ontology motors `:reply-with q1 </li></ul><ul><li>:content motor1) </li></ul><ul><li>reply </li></ul><ul><li>(tell :language KIF :ontology motors :in-reply-to q1 </li></ul><ul><li>: content (= (val (torque motor1) (sim-time 5) (scalar 12 kgf)) </li></ul><ul><li>(tell :language KIF :ontology structures :in-reply-to q1 </li></ul><ul><li>: content (fastens frame12 motor1)) </li></ul><ul><li>(eos :in-repl-to q1) </li></ul>agent A asks agent B to tell all it knows about motor1. B replys with a sequence of tells terminated with a sorry. Franz Kurfess: Knowledge Processing
  51. 51. Post-Test Franz Kurfess: Knowledge Processing
  52. 52. Evaluation <ul><li>Criteria </li></ul>Franz Kurfess: Knowledge Processing
  53. 53. KP/KM Activity <ul><li>select a domain that requires significant human involvement for dealing with knowledge </li></ul><ul><li>identify at least two candidates for </li></ul><ul><ul><li>knowledge representation </li></ul></ul><ul><ul><li>reasoning </li></ul></ul><ul><li>evaluate their suitability </li></ul><ul><ul><li>human perspective </li></ul></ul><ul><ul><ul><li>understandable and usable for humans </li></ul></ul></ul><ul><ul><li>computational perspective </li></ul></ul><ul><ul><ul><li>storage, processing </li></ul></ul></ul>Franz Kurfess: Knowledge Processing
  54. 54. KP/KM Activity Outcomes 2007 <ul><li>Images with Metadata </li></ul><ul><li>Extracting contact information from text </li></ul><ul><li>Qualitative and quantitative knowledge about cheese making </li></ul><ul><li>Visualization of astronomy data </li></ul><ul><li>Surveillance/security KM </li></ul><ul><li>Marketing </li></ul><ul><li>Face recognition </li></ul><ul><li>Visual marketing </li></ul>Franz Kurfess: Knowledge Processing
  55. 55. Important Concepts and Terms <ul><li>automated reasoning </li></ul><ul><li>belief network </li></ul><ul><li>cognitive science </li></ul><ul><li>computer science </li></ul><ul><li>deduction </li></ul><ul><li>frame </li></ul><ul><li>human problem solving </li></ul><ul><li>inference </li></ul><ul><li>intelligence </li></ul><ul><li>knowledge acquisition </li></ul><ul><li>knowledge representation </li></ul><ul><li>linguistics </li></ul><ul><li>logic </li></ul><ul><li>machine learning </li></ul><ul><li>natural language </li></ul><ul><li>ontology </li></ul><ul><li>ontological commitment </li></ul><ul><li>predicate logic </li></ul><ul><li>probabilistic reasoning </li></ul><ul><li>propositional logic </li></ul><ul><li>psychology </li></ul><ul><li>rational agent </li></ul><ul><li>rationality </li></ul><ul><li>reasoning </li></ul><ul><li>rule-based system </li></ul><ul><li>semantic network </li></ul><ul><li>surrogate </li></ul><ul><li>taxonomy </li></ul><ul><li>Turing machine </li></ul>Franz Kurfess: Knowledge Processing
  56. 56. Summary Knowledge Processing <ul><li>there are different types of knowledge </li></ul><ul><li>knowledge acquisition can be conceptually difficult and time-consuming </li></ul><ul><li>popular knowledge representation methods for computers are based on mathematical logic, if ... then rules, and graphs </li></ul><ul><li>computer-based reasoning depends on the knowledge representation method, and can be computationally very challenging </li></ul>Franz Kurfess: Knowledge Processing
  57. 57. Franz Kurfess: Knowledge Processing
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