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Knowledge technologies for network Organisations

Knowledge technologies for network Organisations. Mitja Jermol Jozef Stefan Institute. Outline of the talk. Basic concepts Networked organisations Knowledge management Knowledge technologies KT for NO: theoretical background Examples of using KT in NOI Knowledge discovery

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Knowledge technologies for network Organisations

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  1. Knowledge technologies for network Organisations Mitja Jermol Jozef Stefan Institute

  2. Outline of the talk • Basic concepts • Networked organisations • Knowledge management • Knowledge technologies • KT for NO: theoretical background • Examples of using KT in NOI • Knowledge discovery • Knowledge mapping • Knowledge sharing • Knowledge formalisation: ontologies, Cyc • Knowledge use

  3. Organisation – Business process view MANAGEMENT, & STRATEGIC DECISIONS strategic OPERATIONAL PLANNING & MANAGING management RESEARCH & DEVELOPMENT ADDITIONAL SERVICES MARKETING & MARKET ANALYSIS KNOWLEDGE RESOURCE MANAGEMENT TRADING BUSINESS AND DATA UNDERSTAND. DATA PREPARATION MODELING DEPLOYMENT operational ADAPTING & CORRECTING ADAPTING & CORRECTING EVALUATION SUPPORT

  4. Organisation – knowledge view MANAGEMENT, & STRATEGIC DECISIONS strategic RESEARCH & DEVELOPMENT MARKETING & MARKET ANALYSIS OPERATIONAL PLANNING & MANAGING ADDITIONAL SERVICES KNOWLEDGE RESOURCE MANAGEMENT TRADING management BUSINESS AND DATA UNDERSTAND. operational DEPLOYMENT DATA PREPARATION MODELING ADAPTING & CORRECTING SUPPORT ADAPTING & CORRECTING EVALUATION

  5. Networked organisations Collaborative Networks (CN) Collaborative Networked Organizations (CNO) Virtual Laboratory (VL) Virtual Organization (VO) Virtual Enterprise (VE) Professional Virtual Community (PVC) Extended Enterprise CNO Breeding Environment

  6. Knowledge Technologies Knowledge technologies • …are information technologies including data mining, machine learning, decision support, language technologies, knowledge sharing tools and other information technologies that support acquisition, retrieval, reuse, share, maintenance and modeling of knowledge. • …adds a layer of intelligence to information technology, to filter appropriate information and deliver it when it is needed. • …provide a means for computers to understand the knowledge that is being presented by knowledge integrated into documents and services on the Internet (Semantic Web). • Among knowledge technologies are knowledge extraction tools, ontologies, knowledge representation formalisms, intelligent topic maps, blogs, groupware, document management, expertise locators, latent semantic analysis, semantic networks, social networking engines, wikis,…

  7. Kt to support Knowledge management • "Knowledge Management caters to the critical issues of organizational adaptation, survival and competence in face of increasingly discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings." Y. Malhotra at http://www.brint.com/km/whatis.htm

  8. paradigms in KM • From K-resource to K-process • From deterministic to complex – “A few rules and a lot of freedom” • From cognitive factors to social factors – knowledge networks and social networks • From know-how to creativity and innovation • Creativity: the crazy things that work • Innovation is the conversion of knowledge and ideas into a benefit.

  9. Self-organisational Complexity Predetermined Holistic view on KM Societylevel Corporatelevel Resourcelevel Physical level mitja.jermol@ijs.si

  10. KM in relation to other systems KM Strategic level Vision, strategy management ERP CRM SCM FMS TQM Organisational level Organisation, culture, models Operativelevel Tools, methods, production, IS

  11. KT for NO:conceptual background

  12. KM in no

  13. KT aproachto KM in no

  14. Kt role Business process Simulations Analysing Modeling Predictions Trend detection

  15. modeling Value chain experts BP1 Business process level BP2 BP3 Top-down modeling Business process MP1 Microprocess process level MP2 MP3 Bottom-up modeling Microprocesses AC1 Activity level AC2 AC3 data

  16. Business process Measuring Disturbances (environment) Data Tools In Out (set of results) Business process Methods Knowledge Control Reference (set of goals) Assessment Modeling

  17. Kt support for KM • Knowledge management in a business environment • Analysing, modeling, managing distributed networked organisations • Industry clusters, virtual organisations, virtual communities, living labs • Knowledge discovery and knowledge storage • Competence directories, Knowledge mapping, K-bases, media and methods repositories • Training and knowledge transfer • ICT supported training, virtual communities, distance learning, personalisation

  18. Examples of using knowledge technologies for networked organisations

  19. Tools and methods for k-processes Knowledge storage and development (Contentmanagement) tracking and workflow, reference materials, ontologies, semantics, BBS, yellow pages, content and media databases... Knowledge transfer(Training) (distance) learning, intelligent searching, portals, personalisation, machine learning, learning methods, didactics, pedagogy,… Knowledgegeneration and acquisition data analysis: x mining, statistic methods,... business intelligence: scenario planning, game theory,... content acquisition: digitalisation, classification, filtering, ranking,... Modeling, decision support, expert systems,... Knowledge use, customisation and resourcenetworking collaboration, community, teams, experts, virtual networks,... mitja.jermol@ijs.si

  20. Examples • Knowledge discovery • Textgarden set of tools • Searchpoint • Knowledge mapping • Partner finding tool (IST-World) • Knowledge map (Document atlas) • Knowledge sharing • Personalised Knowledge Transfer (videolectures.net) • Virtual Learning Communities (ECOLEAD VLC) • Knowledge formalisation (Ontologies) • Ontogen • Standards • Cyc • Knowledge use • Decision support systems (…intelligence, ) • Reasoning mechanisms (CyC) • Knowledge in business process (ACTIVE, PROLIX, TENCOMPETENCE)

  21. Knowledge discovery

  22. Knowledge discovery for no • Finding new knowledge, rules, patterns,… in structured and unstructured data • Examples:

  23. tools • Text garden (JSI) • Weka • S-Plus • R • Orange • Oracle Datamining (ODM), SQL DM, DB2 Intelligent miner,…

  24. Lexical text processing (tokenization, stop-words, stemming, n-grams, Wordnet) Named Entity Extraction (capitalization based) Unsupervised learning (KMeans, Hierarchical-KMeans, OneClassSVM) Cross Correlation (KCCA, matching text with other data) Semi-Supervised learning (Uncertainty sampling) Keyword Extraction (contrast, centroid, taxonomy based) Supervised learning (SVM, Winnow, Perceptron, NBayes) Large Taxonomies (dealing with DMoz, Medline) Dimensionality reduction (LSI, PCA) Crawling Web and Search Eng. (for large scale data acquisition) Visualization (Graph based, Tiling, Density based, …) Scalable Search (inverted index) Textgarden Functionality blocks

  25. Technical aspects • Text Garden is almost entirely written in portable C++ • …it compiles under Windows (Microsoft Visual C++, Borland C++) and Unix/Linux (GNU C) • …it runs under 32bit and 64bit platforms • …it consists of ~200.000 lines of code • Text-Garden has the following interfaces with the same API: • C/C++ - through simplified DLL & native C++ • Java – through JNI • .NET – e.g. accessible through C#, VB, … • Matlab – through standard Matlab interface • Python – through standard Python interface • Mathematica, Prolog, R – in preparation • API has ~40 classes and ~250 functions • Text-Garden is under LGPL licence • It is available from www.textmining.net

  26. Knowledge mapping

  27. Knowledge Mapping for NO • Knowledge Mapping (PROCESS) • Knowledge Map (VISUALISATION TOOL) • Knowledge Repository (DATABASE) • Knowledge Space (MODEL) • Examples for NOs: • Competence maps, mind maps (network partners) • Concept and context maps (products, assets) • Knowledge landscape and dynamic knowledge fields (customer segmentation, market needs,…) • pattern languages, dialogue mapping, graphic facilitation, GIS, quantitative charts and graphs, … • semantic networks, relational diagrams, knowledge landscapes,…

  28. types • Knowledge map • Knowledge (Structure) Map • Knowledge (Connectivity) Map • Knowledge (Carrier - Flow) Map • K- (Development) Map – Knowledgespace • Knowledge (Asset) Map

  29. Tools • Document atlas • Contexter • Smartdraw • Freemind • Cmap • KnetMap • Intellix • ….

  30. Implementations

  31. Document atlas • Given a collection of documents (time stamps, if available) • eg., news articles, publication record • Visualize in 2D: • content of documents, • named entities, • relationships (social networks), • changes over time • Installation is publicly available in binaries at http://docatlas.ijs.si/

  32. Knowledge sharing – personalised knowledge transfer

  33. Knowledge sharing • Facilitator of the knowledge flow within the organisation • …fostering innovation, creativity and social cohesion • Examples: • ICT based training in clusters • Distance learning and self-learning • Webinars and online trainings • Collaborative problem solving • COPs • …

  34. tools .LRN http://dotlrn.org/ Spaghettilearning.com http://www.spaghettilearning.com/ Moodle http://moodle.org/ Dokeos http://www.dokeos.com/ Atutor www.atutor.ca/ Bazaar 7.11 http://bazaar.athabascau.ca/ KEWL http://kewl.uwc.ac.za/ OLAT http://www.olat.org/ LON-CAPA 2.0.2 http://www.lon-capa.org/ Plugins: PloneSGS e-LECT (0.6) LTAssignmentBox LTOnlineTest Zschool PloneCampus by Reflab, CMFSyllabus Eduplone

  35. Knowledge transfer • Training is a challenge, because it is not somethingyougive; but a processbased on guidance • Personalization of methods, content, tools, guidance, control, motivation and goal.

  36. Personalisation

  37. ECOLEAD VLC training Active Virtual Learning Community 460 members 50 self learning courses 172 video seminars from 16 events, 100 authors, 89 lectures 122 prepared documents 2 training DVDs

  38. Vidreolectures.net Status 28.11.2007 122 events, 1854 authors, 2139 lectures, 3103 videos   App 2000 visits/day 267439 visits since 1.3.2007 1.000.000 pagevievs

  39. Knowledge formalisation

  40. Standards and initiatives • Cg – conceptual graphs - http://www.cs.uah.edu/~delugach/CG/ • CycL- http://www.cyc.com/cycdoc/ref/cycl-syntax.html, • DAML+OIL - http://www.daml.org/ • DQL- DAML query language - www.daml.org/dql/ • KIF - logic.stanford.edu/kif/kif.html • OKBC - http://www.ai.sri.com/~okbc/ • RuleML, http://www.ruleml.org/ • SHOE, http://www.cs.umd.edu/projects/plus/SHOE/ • SUO (IEEE P1600.1), http://suo.ieee.org/ • XTM Topic Maps, http://www.topicmaps.org/ • Ontologies

  41. The Semantic Web Layer Uniform Resource Identifier

  42. Topic Maps Thesauri Taxonomies Semantic Networks Extended ER-Models Predicate Logic Ontologies and Their Relatives Front-End Navigation Information Retrieval Sharing of Knowledge Query Expansion Ontologies Queries Consistency Checking EAI Mediation Reasoning Back-End

  43. Ontology development • Scenario 1 – Top-down: Expert building the ontology - manually • Scenario 2 – Bottom-up: Ontology build from documents, email logs, etc…

  44. Why to use ontologies in NO? • NO development and user community • E4 “Constitution” - a reference formal description of common understanding • a common vocabulary, rules and formulations • a background for an integrated architecture • multilinguality and multimodality support • … • Benefits to the user • Solving inconsistencies - Reference vocabulary • Sharing - Enabling the connectivity to NO community on the level of business processes, resources and BOM • Open and integrated - Connectivity to existing ontologies that are user defined and/or relevant for NO • …

  45. Overview of Existing Ontologies • Terminological ontologies: WordNet, VerbNet, FrameNet, Sensus • Domain ontologies: The Gene Ontology (GO), PSL (Process Specification Language Ontology), The CEO project • Upper ontologies: SUMO (Suggested Upper Merged Ontology), Mikrokosmos, OpenCyc, Sowa’s top-level ontology • Ontologies with common-sense knowledge: Cyc, ConceptNet • Business-oriented Ontologies: The AIAI enterprise ontology, The TOronto Virtual Enterprise’s ontology (TOVE), The BPMO (Business Process Management Ontology)

  46. ECOLEAD ontology

  47. advanced services (e4 case)

  48. OntoGen • Semi-Automatic • Text-mining methods provide suggestions and insights into the domain • The user can interact with parameters of text-mining methods • All the final decisions are taken by the user • Data-Driven • Most of the aid provided by the system is based on some underlying data provided by the system • Instances are described by features extracted from the data (e.g. bag-of-words vectors) Installation package is publicly available in binaries at ontogen.ijs.si

  49. Concept hierarchy Sub-Concept suggestion Ontology visualization

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