Franz Kurfess: Knowledge Exchange Cal Poly SLO Computer Science Department Franz J. Kurfess Knowledge Exchange.

Slides:



Advertisements
Similar presentations
Dr. Leo Obrst MITRE Information Semantics Information Discovery & Understanding Command & Control Center February 6, 2014February 6, 2014February 6, 2014.
Advertisements

Chapter 11 user support. Issues –different types of support at different times –implementation and presentation both important –all need careful design.
Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2007) Learning for Semantic Parsing Advisor: Hsin-His.
1 Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess CPE/CSC 580: Intelligent Agents 1.
Using the Semantic Web to Construct an Ontology- Based Repository for Software Patterns Scott Henninger Computer Science and Engineering University of.
1 Cal Poly SLO Computer Science Department Franz J. Kurfess Computers and Knowledge 1.
1 Cal Poly SLO Computer Science Department Franz J. Kurfess Computers and Knowledge 1.
Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess Knowledge Retrieval.
1 © Franz J. Kurfess Constrained Access Franz J. Kurfess Cal Poly SLO Computer Science Department.
© Franz J. Kurfess Knowledge Processing 1 CPE/CSC 580: Knowledge Management Dr. Franz J. Kurfess Computer Science Department Cal Poly.
© 2005 Franz J. Kurfess Expert System Examples 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly.
© 2001 Franz J. Kurfess Knowledge Processing 1 CPE/CSC 580: Knowledge Management Dr. Franz J. Kurfess Computer Science Department Cal Poly.
Help and Documentation zUser support issues ydifferent types of support at different times yimplementation and presentation both important yall need careful.
Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Multiagent Systems and Societies of Agents
© 2002 Franz J. Kurfess Introduction 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly.
1 Cal Poly SLO Computer Science Department Franz J. Kurfess Computers and Knowledge 1.
1 Knowledge Exchange Franz J. Kurfess Cal Poly SLO Computer Science Department.
Advanced Distributed Learning. Conditions Before SCORM  Couldn’t move courses from one Learning Management System to another  Couldn’t reuse content.
1 Knowledge Exchange Franz J. Kurfess Cal Poly SLO Computer Science Department.
© 2002 Franz J. Kurfess Introduction 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly.
© 2001 Franz J. Kurfess Introduction 1 CPE/CSC 580: Knowledge Management Dr. Franz J. Kurfess Computer Science Department Cal Poly.
Creating Architectural Descriptions. Outline Standardizing architectural descriptions: The IEEE has published, “Recommended Practice for Architectural.
1 Information Retrieval and Extraction 資訊檢索與擷取 Chia-Hui Chang, Assistant Professor Dept. of Computer Science & Information Engineering National Central.
Information Retrieval and Extraction 資訊檢索與擷取 Chia-Hui Chang National Central University
Chapter 12: Intelligent Systems in Business
Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess Knowledge Processing.
Franz Kurfess: Knowledge Exchange Cal Poly SLO Computer Science Department Franz J. Kurfess Knowledge Exchange.
© 2001 Franz J. Kurfess Introduction 1 CPE/CSC 580: Knowledge Management Dr. Franz J. Kurfess Computer Science Department Cal Poly.
Statistical Natural Language Processing. What is NLP?  Natural Language Processing (NLP), or Computational Linguistics, is concerned with theoretical.
Semantic Web Technologies Lecture # 2 Faculty of Computer Science, IBA.
Lecture 1, 7/21/2005Natural Language Processing1 CS60057 Speech &Natural Language Processing Autumn 2005 Lecture 1 21 July 2005.
Some Thoughts to Consider 6 What is the difference between Artificial Intelligence and Computer Science? What is the difference between Artificial Intelligence.
{ Senate Hearing Project Kathryn Gustafson Farmington High School.
CONTI’2008, 5-6 June 2008, TIMISOARA 1 Towards a digital content management system Gheorghe Sebestyen-Pal, Tünde Bálint, Bogdan Moscaliuc, Agnes Sebestyen-Pal.
Overview of the Database Development Process
Lecture 12: 22/6/1435 Natural language processing Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess Knowledge Exchange.
Knowledge representation
11 C H A P T E R Artificial Intelligence and Expert Systems.
Chapter 4 – Slide 1 Effective Communication for Colleges, 10 th ed., by Brantley & Miller, 2005© Technology and Electronic Communication.
Information Systems & Semantic Web University of Koblenz ▪ Landau, Germany Semantic Web - Multimedia Annotation – Steffen Staab
1 Computational Linguistics Ling 200 Spring 2006.
Computing Fundamentals Module Lesson 19 — Using Technology to Solve Problems Computer Literacy BASICS.
RELATIONAL FAULT TOLERANT INTERFACE TO HETEROGENEOUS DISTRIBUTED DATABASES Prof. Osama Abulnaja Afraa Khalifah
1 Information Retrieval Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow)
 Copyright 2008 Digital Enterprise Research Institute. All rights reserved. Semantic on the Social Semantic Desktop.
Knowledge Representation of Statistic Domain For CBR Application Supervisor : Dr. Aslina Saad Dr. Mashitoh Hashim PM Dr. Nor Hasbiah Ubaidullah.
Object-Oriented Software Engineering using Java, Patterns &UML. Presented by: E.S. Mbokane Department of System Development Faculty of ICT Tshwane University.
10/24/09CK The Open Ontology Repository Initiative: Requirements and Research Challenges Ken Baclawski Todd Schneider.
SKOS. Ontologies Metadata –Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies –Provide.
Programming Languages and Design Lecture 3 Semantic Specifications of Programming Languages Instructor: Li Ma Department of Computer Science Texas Southern.
© 2002 Franz J. Kurfess Introduction 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly.
Of 33 lecture 1: introduction. of 33 the semantic web vision today’s web (1) web content – for human consumption (no structural information) people search.
Digital Libraries1 David Rashty. Digital Libraries2 “A library is an arsenal of liberty” Anonymous.
EEL 5937 Agent communication EEL 5937 Multi Agent Systems Lotzi Bölöni.
Information Retrieval
Metadata By N.Gopinath AP/CSE Metadata and it’s role in the lifecycle. The collection, maintenance, and deployment of metadata Metadata and tool integration.
Digital Video Library Network Supervisor: Prof. Michael Lyu Student: Ma Chak Kei, Jacky.
1 Open Ontology Repository initiative - Planning Meeting - Thu Co-conveners: PeterYim, LeoObrst & MikeDean ref.:
Software Reuse Course: # The Johns-Hopkins University Montgomery County Campus Fall 2000 Session 4 Lecture # 3 - September 28, 2004.
DANIELA KOLAROVA INSTITUTE OF INFORMATION TECHNOLOGIES, BAS Multimedia Semantics and the Semantic Web.
5. 2Object-Oriented Analysis and Design with the Unified Process Objectives  Describe the activities of the requirements discipline  Describe the difference.
Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall) Chapter 12: Artificial Intelligence and Expert Systems.
CHAPTER 1 Introduction BIC 3337 EXPERT SYSTEM.
IB Assessments CRITERION!!!.
MANAGING KNOWLEDGE FOR THE DIGITAL FIRM
Ontology-Based Approaches to Data Integration
Chapter 11 user support.
Information Retrieval
Presentation transcript:

Franz Kurfess: Knowledge Exchange Cal Poly SLO Computer Science Department Franz J. Kurfess Knowledge Exchange

Franz Kurfess: Knowledge Retrieval Cal Poly SLO Computer Science Department Franz J. Kurfess Knowledge Exchange

Franz Kurfess: Knowledge Exchange This lecture series has been sponsored by the European Community under the BPD program with Vilnius University as host institution 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 Acknowledgements

5 Franz Kurfess: Knowledge Exchange Use and Distribution of these Slides ❖ 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 at I hereby grant permission to use them in educational settings. If you do so, it would be nice to send me an about it. If you’re considering using them in a commercial environment, please contact me 5

6 Franz Kurfess: Knowledge Exchange Overview Knowledge Exchange ❖ Introduction ❖ Knowledge Capture ❖ Explicit Capture ❖ Extraction From Text ❖ Case-based Reasoning ❖ Enhancement of Existing Documents ❖ Transfer of Knowledge ❖ Communication ❖ Basic Concepts ❖ Language and Communication ❖ Natural Language ❖ Formal Languages ❖ Communication Models ❖ Distribution of Knowledge ❖ Knowledge Repositories ❖ Distribution Models 6

7 Franz Kurfess: Knowledge Exchange Logistics 7

8 Preliminaries 8

9 Bridge-In ❖ How do you share and exchange your knowledge? ❖ direct human-to-human communication ❖ mediated exchange ❖ without computers ❖ computer-based 9

10 Franz Kurfess: Knowledge Exchange Motivation and Objectives 10

11 Franz Kurfess: Knowledge Exchange Motivation 11

12 Franz Kurfess: Knowledge Exchange Objectives 12

13 Franz Kurfess: Knowledge Exchange Evaluation Criteria 13

14 Franz Kurfess: Knowledge Exchange Introduction 14

15 Franz Kurfess: Knowledge Exchange ((( )) ()))) Richer representations More ambiguous More versatile (defconcept bridge ())) More formal More concrete More introspectible Knowledge Base Introductory texts, expert hints, explanations, dialogues, comments, examples, exceptions,... Info. extraction templates, dialogue segments and pegs, filled-out forms, high-level connections,... Alternative formalizations (KIF, MELD, CML,…), alternative views of the same notion (e.g., what is a threat) Descriptions augmented with prototypical examples & exceptions, problem-solving steps and substeps,... WWW [Gil 2000] The Need for Knowledge Exchange 15

16 Franz Kurfess: Knowledge Exchange [Gil 2000] Knowledge Mobility ❖ multiple views and versions of the same information ❖ need to provide tools that establish connections among alternative versions/views of the same information ❖ hyper-connectivity ❖ need to provide tools that suggest further connections to related sources when users compose documents ❖ need to annotate hyperlinks ❖ basis to support information morphing ❖ how one or more knowledge sources are used for ❖ alternative purposes ❖ track alternative knowledge transformations ❖ various renderings and implementations of a knowledge source 16

17 Franz Kurfess: Knowledge Exchange Knowledge Capture ❖ before knowledge can be shared, it must be captured ❖ in and encoding and representation that is suitable for the sender and recipient of the knowledge ❖ the representation format must be suitable for transmission via a communication channel between sender and recipient ❖ for human-to-human knowledge exchange, natural language in written or spoken form is often suitable and convenient 17

18 Franz Kurfess: Knowledge Exchange Knowledge Capture Methods ❖ Explicit Capture ❖ Extraction from Documents ❖ text ❖ other formats ❖ Case-based Reasoning ❖ Enhancement of Existing Documents 18

19 Franz Kurfess: Knowledge Exchange Explicit Capture ❖ conventional techniques for knowledge acquisition ❖ interviews with experts, knowledge engineers ❖ advantages ❖ carefully constructed ❖ suitable knowledge representation methods ❖ usually common-sense evaluation ❖ sometimes formal evaluation ❖ consistency checks, other formal aspects 19

20 Franz Kurfess: Knowledge Exchange Extraction From Text ❖ syntactic level ❖ keywords, descriptive features ❖ construction of an index, meta data ❖ semantic level ❖ document structure ❖ requires information about structure (tags, DDT, RDF) ❖ sentence structure ❖ natural language processing (NLP) ❖ pragmatic level ❖ context (thesaurus, ontology, NLP) 20

21 Franz Kurfess: Knowledge Exchange Case-based Reasoning ❖ solutions to a problem in a specific context are collected ❖ represented in a structured format ❖ problem, context, solution ❖ usable by a computer-based system ❖ cases are often represented through frames or similar mechanisms ❖ new cases are matched against existing ones ❖ patterns in the frames provide the basis for matching ❖ the suitability of the solution is judged by the user 21

22 Franz Kurfess: Knowledge Exchange Enhancement of Existing Documents ❖ in addition to the methods mentioned above, collections of documents can be enhanced ❖ addition of meta-knowledge ❖ integration into an existing framework/ontology ❖ manually through categorization ❖ automatically through keyword extraction ❖ indirectly through statistical correlations with other documents 22

23 Franz Kurfess: Knowledge Exchange Transfer of Knowledge ❖ Communication ❖ Basic Concepts ❖ Language and Communication ❖ Natural Language ❖ Formal Languages ❖ Communication Models 23

24 Franz Kurfess: Knowledge Exchange Basic Concepts ❖ communication ❖ exchange of information ❖ requires a shared system of signs ❖ greatly enhanced by language ❖ speaker ❖ produces signs as utterances ❖ general: not only spoken language ❖ listener (hearer) ❖ perceives and interprets signs 24

25 Franz Kurfess: Knowledge Exchange Purpose of Communication ❖ sharing of information among agents or systems ❖ query other agents for information ❖ responses to queries ❖ requests or commands ❖ actions to be performed for another agent ❖ offer ❖ proposition for collaboration ❖ acknowledgement ❖ confirmation of requests, offers ❖ sharing ❖ of experiences, feelings 25

26 Franz Kurfess: Knowledge Exchange Communication Problems ❖ intention ❖ what is the expected outcome (speaker’s perspective) ❖ timing ❖ when is a communication act appropriate ❖ selection ❖ which act is the right one ❖ language ❖ what sign system should be used ❖ interpretation ❖ will the intended meaning be conveyed to the listener ❖ ambiguity ❖ can the intention be expressed without the possibility of misunderstandings 26

27 Franz Kurfess: Knowledge Exchange Language and Communication ❖ Natural Language ❖ used by humans ❖ evolves over time ❖ moderately to highly ambiguous ❖ Formal Languages ❖ invented ❖ rigidly defined ❖ little ambiguity 27

28 Franz Kurfess: Knowledge Exchange Natural Language ❖ formal description is very difficult ❖ sometimes non-systematic, inconsistent, ambiguous ❖ mostly used for human communication ❖ easy on humans ❖ tough on computers ❖ context is critical ❖ situation, beliefs, goals 28

29 Franz Kurfess: Knowledge Exchange Formal Languages ❖ symbols ❖ terminal symbols ❖ finite set of basic words ❖ not: alphabet, characters ❖ non-terminal symbols ❖ intermediate structures composed of terminal or non-terminal symbols ❖ strings ❖ sequences of symbols ❖ phrases ❖ sub-strings grouping important parts of a string 29

30 Franz Kurfess: Knowledge Exchange Formal Languages Cont. ❖ sentences ❖ allowable strings in a language ❖ composed from phrases ❖ grammar ❖ rules describing correct sentences ❖ often captured as rewrite rules in BNF notation ❖ lexicon ❖ list of allowable vocabulary words 30

31 Franz Kurfess: Knowledge Exchange Communication Models ❖ encoded message model ❖ a definite proposition of the speaker is encoded into signs which are transmitted to the listener ❖ the listener tries to decode the signs to retrieve the original proposition ❖ errors are consequences of transmission problems ❖ situated language model ❖ the intended meaning of a message depends on the signals as well as the situation in which they are exchanged ❖ mis-interpretation may lead to additional problems 31

32 Franz Kurfess: Knowledge Exchange Communication Types ❖ telepathic communication ❖ speaker and listener have a shared internal representation ❖ communication through Tell/Ask directives ❖ language-based communication ❖ speaker performs actions that produce signs which other agents can perceive and interpret ❖ communication language is different from the internal representation ❖ more complex ❖ involves several mappings ❖ language needs to be generated, encoded, transmitted, decoded, and interpreted 32

33 Franz Kurfess: Knowledge Exchange Telepathic Communication [Russell & Norvig 1995] 33

34 Franz Kurfess: Knowledge Exchange Language-Based Communication [Russell & Norvig 1995] 34

35 Franz Kurfess: Knowledge Exchange Communication Steps: Speaker ❖ intention ❖ decision about producing a speech act ❖ generation ❖ conversion of the information to be transferred into the chosen language ❖ synthesis ❖ actions that produce the generated signs 35

36 Franz Kurfess: Knowledge Exchange Communication Steps: Listener ❖ perception ❖ reception of the signs produced by the speaker ❖ speech recognition, lip reading, character recognition ❖ analysis ❖ syntactic interpretation (parsing) ❖ semantic interpretation ❖ disambiguation ❖ selection of the most probable intended meaning ❖ incorporation ❖ the selected interpretation is added to the existing world model as additional piece of evidence 36

37 Franz Kurfess: Knowledge Exchange Communication Example [Russell & Norvig 1995] 37

38 Franz Kurfess: Knowledge Exchange Knowledge Exchange Perspectives 38

39 Franz Kurfess: Knowledge Exchange Different Perspectives ❖ Roles ❖ Scope ❖ Purpose 39

40 Franz Kurfess: Knowledge Exchange Roles ❖ knowledge creator ❖ source of the knowledge ❖ knowledge facilitator ❖ supports the exchange of knowledge between creator and user ❖ knowledge user ❖ sender ❖ initiates and conducts the transmission of knowledge ❖ may be creator or facilitator ❖ recipient ❖ typically the knowledge user 40

41 Franz Kurfess: Knowledge Exchange Scope of Knowledge Exchange ❖ number of people involved ❖ individuals, groups, organizations, humanity ❖ coherence ❖ domain knowledge, educational background, intellectual ability, familiarity with the environment,... ❖ spread ❖ geographical distribution 41

42 Franz Kurfess: Knowledge Exchange Individuals ❖ informal ❖ direct communication ❖ quick feedback ❖ low persistence tolerable ❖ clarification easy ❖ consistency issues easy to resolve 42

43 Franz Kurfess: Knowledge Exchange Groups ❖ informal ❖ direct communication ❖ coordination and synchronization required ❖ moderate persistence desirable ❖ clarification via discussion 43

44 Franz Kurfess: Knowledge Exchange Organization ❖ more formal repositories, exchange methods; systematic communication, coordination and synchronization necessary; persistence important; more structured approaches to clarification and consistency beneficial 44

45 Franz Kurfess: Knowledge Exchange Community ❖ formal “body of knowledge” ❖ well-structured, reasonably controlled vocabulary, established repositories of knowledge, procedures for validation (“peer review”) ❖ established exchange methods ❖ journals, official publications, books, conferences, portals ❖ professional organizations with controlled memberships ❖ established communication, coordination, and synchronization methods 45

46 Franz Kurfess: Knowledge Exchange Humanity ❖ no coherent “body of knowledge” ❖ communication, coordination, and synchronization of knowledge exchange across boundaries is difficult ❖ differences in vocabulary, methods, knowledge validation processes make exchange of knowledge difficult ❖ serious problems with clarification, resolution of inconsistencies are possible 46

47 Franz Kurfess: Knowledge Exchange Purpose ❖ personal enrichment ❖ better product ❖ better working conditions ❖ commercial advantage ❖ stronger community ❖ societal benefits 47

48 Franz Kurfess: Knowledge Exchange Knowledge Distribution 48

49 Franz Kurfess: Knowledge Exchange Distribution of Knowledge ❖ Knowledge Repositories ❖ Digital Libraries ❖ Distribution Models 49

50 Franz Kurfess: Knowledge Exchange Knowledge Repositories ❖ persistent storage of digital documents ❖ internal representation in the original format ❖ loss-less transformation may be acceptable ❖ transparent internal organization ❖ multiple presentation methods for various users and usage methods ❖ multiple access methods ❖ according to users’ needs and capabilities 50

51 Franz Kurfess: Knowledge Exchange Wikipedia ❖ collaborative effort to capture knowledge ❖ contributions by volunteers ❖ not restricted to “experts” ❖ liberal policy for entry modifications ❖ editorial policies to limit abuse 51

52 Franz Kurfess: Knowledge Exchange Scientific American “Edit This” ❖ public is invited to comment on some articles before they are published ❖ see “Science 2.0: Great New Tool, or Great Risk?” as an example ❖

53 Franz Kurfess: Knowledge Exchange Google Knols 53

54 Franz Kurfess: Knowledge Exchange Educational Repositories ❖ Open Course Ware Initiative ❖ Connexions ❖ Merlot 54

55 Franz Kurfess: Knowledge Exchange General Repositories ❖ Instructables.com ❖ Slideshare.com ❖ Blogs ❖ Youtube and similar sites 55

56 Franz Kurfess: Knowledge Exchange Digital Libraries ❖ collections of documents and artifacts stored and accessed via computers ❖ remotely accessible through networks ❖ enhanced functionality compared with paper- based libraries ❖ access methods ❖ organization principles ❖ duplication ❖ implementation and usage unclear 56

57 Franz Kurfess: Knowledge Exchange Digital Library In-A-Box 57 [Sweeney & Kurfess 1998]

58 Franz Kurfess: Knowledge Exchange Ausklang 58

59 Franz Kurfess: Knowledge Exchange Post-Test 59

60 Franz Kurfess: Knowledge Exchange Evaluation ❖ Criteria 60

61 Franz Kurfess: Knowledge Exchange KP/KM Activity ❖ select a domain that requires significant human involvement for dealing with knowledge ❖ identify at least two candidates for ❖ knowledge representation ❖ reasoning ❖ evaluate their suitability ❖ human perspective ❖ understandable and usable for humans ❖ computational perspective ❖ storage, processing 61

62 Franz Kurfess: Knowledge Exchange KP/KM Activity Outcomes 2007 ❖ Images with Metadata ❖ Extracting contact information from text ❖ Qualitative and quantitative knowledge about cheese making ❖ Visualization of astronomy data ❖ Surveillance/security KM ❖ Marketing ❖ Face recognition ❖ Visual marketing 62

63 Franz Kurfess: Knowledge Exchange Important Concepts and Terms 63 ❖ automated reasoning ❖ belief network ❖ cognitive science ❖ computer science ❖ deduction ❖ frame ❖ human problem solving ❖ inference ❖ intelligence ❖ knowledge acquisition ❖ knowledge representation ❖ linguistics ❖ logic ❖ machine learning ❖ natural language ❖ ontology ❖ ontological commitment ❖ predicate logic ❖ probabilistic reasoning ❖ propositional logic ❖ psychology ❖ rational agent ❖ rationality ❖ reasoning ❖ rule-based system ❖ semantic network ❖ surrogate ❖ taxonomy ❖ Turing machine

64 Franz Kurfess: Knowledge Exchange Summary Knowledge Exchange 64

65 Franz Kurfess: Knowledge Exchange 65