|Course Code: BCA6121||
Course Title: Knowledge Management (4 Credits)
Unit 1: Overview of Knowledge Management: Introducing Knowledge Management, Need for Knowledge Management, Valuation of Intellectual Capital, Intellectual Capital: Human vs. Structural Capital, Forces Driving Knowledge Management, Knowledge Management Systems, Issues in Knowledge Management
Unit 2: The Nature of Knowledge: What is Data, Information ?, What is Knowledge?, Data, Information, and Knowledge with Examples, Types of Knowledge, Subjective View of knowledge, Objective View of knowledge, Procedural vs. Declarative Knowledge, Tacit vs. Explicit Knowledge, General vs. Specific Knowledge, Technically vs. Contextually Specific Knowledge, Knowledge and Expertise, Types of Expertise, Types of Knowledge, Codifiability and Teachability of Knowledge, Specificity of Knowledge, Reservoirs of Knowledge, Characteristics of Knowledge,
Unit 3: Technologies to Manage Knowledge: Artificial Intelligence and Understanding Knowledge: Cognitive Psychology , Data, Information and Knowledge , Kinds of Knowledge, Expert Knowledge, Thinking and Learning in Humans , Knowledge vs Intelligence, dumb search, Heuristic search in Knowledge-Based Systems, Knowledge Based Systems for KM, What kinds of knowledge are in Knowledge-Based Systems?, Knowledge Based Systems vs Expert Systems, Advantage and disadvantage of Knowledge Based Systems vs Expert Systems.
Unit 4: Knowledge Management Systems Life Cycle: Challenges in KM Systems Development , Conventional Vs KM Systems Life Cycle(KMSLC), Key Differences , Key Similarities, KMSLC Approaches .
Unit 5: Knowledge Creation & Knowledge Architecture: Knowledge Creation, Nonaka’s Model of Knowledge Creation & Transformation, Knowledge Architecture , Acquiring the KM System.
Unit 6: Capturing the Tacit Knowledge: Expert Evaluation, Developing Relationship with Experts , Fuzzy Reasoning & Quality of Knowledge Capture , Interviewing as a Tacit Knowledge Capture Tool
Unit 7: Some Knowledge Capturing Techniques: On-Site Observation (Action Protocol) , Brainstorming, Electronic Brainstorming, Protocol Analysis (Think-Aloud Method) , Consensus Decision Making, Repertory Grid ,Nominal Group Technique (NGT) , Delphi Method ., Concept Mapping, Blackboarding .
Unit 8: Knowledge Codification: Modes of Knowledge Conversion, Codifying Knowledge , Codification Tools/Procedures Knowledge Maps, Decision Table , Decision Tree ,Frames, Production Rules , Case-Based Reasoning , Knowledge-Based Agents , Knowledge Developer’s Skill Set , Knowledge Requirements , Skills Requirements .
Unit 9: Knowledge Transfer in E-World: Introduction about the knowledge transfer, Transferring Knowledge, Fundamentals , Prerequisites for Transfer, E-World , Intranet ., Extranet , Groupware , E-Business , Value Chain , Supply Chain Management (SCM) , Customer Relationship Management (CRM) .
Unit 10: Learning from Data: The Concept of Learning , Data Visualization , Neural Network (Artificial) as Learning Model, Supervised/Unsupervised Learning ., Applications in Business , Relative Fit with KM , Association Rules , Classification Trees .
Unit 11: Knowledge Management Assessment of an Organization: Why Assess Knowledge Management?, When is KM needed?, Who Performs KM Assessment?, Measures Qualitative and Quantitative Assessments of KM, Illustrative Measures of Key Aspects of KM Solutions,
Unit 12: Preserving and Applying Human Expertise: Knowledge-Based Systems: Knowledge-Based System: User’s View, Developer’s View, Knowledge Representation: Rules, Inference chains, Knowledge Representation: Frames, Functional attributes, Frame-Based Reasoning, Rule-Based Reasoning, Forward chaining: Rule Interpretation Process, Backward chaining: Rule Interpretation Process, Backward chaining: Closed World Assumption, Knowledge engineering, Tools.
Unit 13: Using Past History Explicitly as Knowledge: Case-Based Reasoning Systems: Weaknesses of rule-based systems, Case-Based Reasoning (CBR), Case-Based Reasoning (CBR): Adaptation, Case-Based Reasoning (CBR): Successful vs failed cases, Indexing the case library: Flat library, Indexing the case library: Shared feature networks, Indexing the case library: Redundant shared feature networks, Advantages and Disadvantages of Case based systems.
Unit 14: Knowledge Elicitation –Converting Tacit Knowledge to Explicit: Basic One-On-One Interviews: Specific Problem-Solving, Knowledge-Gathering Sessions, Basic One-On-One Interviews: Knowledge Elicitation Sequence, Observational Elicitation, Observational Elicitation: Quiet on-site observation, Exercising the expert, Problem description and analysis,; Role Reversal Techniques, Team Interviewing, Team Interviewing: One-on-many, Team Interviewing: Many-on-many, Many-on-one;
Unit 15: Discovering New Knowledge – Data Mining: Objectives of Data Mining, Classical statistics & statistical pattern recognition, Induction of symbolic rules, Induction trees, Artificial Neural Networks, Supervised Learning: Back Propagation, Unsupervised Learning: Kohonen Network, The Future of Knowledge Management, Protecting Intellectual Property (IP), How to protect the organization from IP losses