CPE/CSC 580: 
Knowledge Management
Dr. Franz J. Kurfess
Computer Science Department
Cal Poly
Course Overview
•	Introduction
•	Knowledge Processing
•	Knowledge Acquisition, Representation and Manipulation
•	Knowledge Organization
•	Classification, Categorization
•	Ontologies, Taxonomies, Thesauri
•	Knowledge Retrieval 
•	Information Retrieval
•	Knowledge Navigation
•	Knowledge Presentation
•	Knowledge Visualization
•	Knowledge Exchange
•	Knowledge Capture, Transfer, and Distribution
•	Usage of Knowledge
•	Access Patterns, User Feedback
•	Knowledge Management Techniques
•	Topic Maps, Agents
•	Knowledge Management Tools
•	Knowledge Management in Organizations
Overview Knowledge Processing
•	Motivation
•	Objectives
•	Chapter Introduction
•	Knowledge Processing as Core AI Paradigm
•	Relationship to KM
•	Terminology
•	Knowledge Acquisition
•	Knowledge Elicitation
•	Machine Learning
•	Knowledge Representation
•	Logic
•	Rules
•	Semantic Networks
•	Frames, Scripts
•	Knowledge Manipulation
•	Reasoning
•	KQML
•	Important Concepts and Terms
•	Chapter Summary
Logistics
•	Introductions
•	Course Materials
•	textbook
•	handouts
•	Web page
•	CourseInfo/Blackboard System and Alternatives
•	Term Project
•	Lab and Homework Assignments
•	Exams
•	Grading
Bridge-In
Pre-Test
Motivation
•	the representation and manipulation of knowledge has been essential for the development of humanity as we know it
•	the use of formal methods and support from machines can improve our knowledge representation and reasoning abilities
•	intelligent reasoning is a very complex phenomenon, and may have to be described in a variety of ways
•	a basic understanding of knowledge representation and reasoning is important for the organization and management of knowledge
Objectives
•	be familiar with the important aspects of commonly used knowledge representation and reasoning methods
•	understand different roles and perspectives of knowledge representation and reasoning methods
•	examine the suitability of knowledge representations for specific tasks
•	evaluate the representation methods and reasoning mechanisms employed in computer-based systems
Evaluation Criteria
Chapter Introduction
•	Knowledge Processing as Core AI Paradigm
•	Relationship to KM
•	Terminology
Knowledge Processing as Core AI Paradigm
Relationship to KM
Terminology
•	Data
•	Information 
•	Knowledge
Data, Information, Knowledge
Data
Information 
Knowledge
•	knowledge characteristics
•	meaningful only with respect to humans
•	it is context-sensitive
•	it may be elaborate
•	it may be explicit or tacit
•	explicit knowledge consists of documented facts
•	frequently objective
•	tacit knowledge is in people’s heads
•	frequently subjective
•	surfaces through interaction

Acquisition, Representation and Manipulation of Knowledge
•	Knowledge Acquisition	
•	Published Sources	
•	Physical Media	
•	Digital Media	
•	People as Sources
•	Interviews
•	Questionnaires
•	Formal Techniques
•	Observation Techniques
•	Knowledge Acquisition Tools	
Knowledge Representation
•	Types of Knowledge
•	Factual Knowledge
•	Subjective Knowledge	
•	Heuristic Knowledge	
•	Deep and Shallow Knowledge	
•	Knowledge Representation Methods
•	Rules, Frames, Semantic Networks
•	Blackboard Representations
•	Object-based Representations
•	Case-Based Reasoning
•	Knowledge Representation Tools
Roles of Knowledge Representation
•	Surrogate
•	Ontological Commitments
•	Fragmentary Theory of Intelligent Reasoning
•	Medium for Computation
•	Medium for Human Expression
KR as Surrogate
•	a substitute for the thing itself
•	enables an entity to determine consequences by thinking rather than acting
•	reasoning about the world through operations on the representation
•	reasoning or thinking are inherently internal processes
•	the objects of reasoning are mostly external entities (“things”)
•	some objects of reasoning are internal, e.g. concepts, feelings, ...
Surrogate Aspects
•	Identity
•	correspondence between the surrogate and the intended referent in the real world
•	Fidelity
•	Incompleteness
•	Incorrectness
•	Adequacy
•	Task
•	User
Surrogate Consequences
•	perfect representation is impossible
•	the only completely accurate representation of an object is the object itself
•	incorrect reasoning is inevitable
•	if there are some flaws in the world model, even a perfectly sound reasoning mechanism will come to incorrect conclusions
Ontological Commitments
•	terms used to represent the world
•	by selecting a representation a decision is made about how and what to see in the world
•	like a set of glasses that offer a sharp focus on part of the world, at the expense of blurring other parts
•	necessary because of the inevitable imperfections of representations
•	useful to concentrate on relevant aspects
•	pragmatic because of feasibility constraints
Ontological Commitments Examples
•	logic
•	views the world in terms of individual entities and relationships between the entities
•	rules
•	entities and their relationships expressed through rules
•	frames
•	prototypical objects
•	semantic nets
•	entities and relationships
KR and Reasoning
•	a knowledge representation indicates an initial conception of intelligent inference
•	often reasoning methods are associated with representation technique
•	first order predicate logic and deduction
•	rules and modus ponens
•	the association is often implicit
•	the underlying inference theory is fragmentary
•	the representation covers only parts of the association
•	intelligent reasoning is a complex and multi-faceted phenomenon
KR for Reasoning
•	a representation suggests answers to fundamental questions concerning reasoning:
•	What does it mean to reason intelligently?
•	implied reasoning method
•	What can possibly be inferred from what we know?
•	possible conclusions
•	What should be inferred from what we know?
•	recommended conclusions
KR and Computation
•	for our purposes, reasoning is a computational process
•	machines are used as reasoning tools
•	without efficient ways of implementing such computational process, it is practically useless
•	e.g. Turing machine
•	most representation and reasoning mechanisms are modified for efficient computation
•	e.g. Prolog vs. predicate logic
Computational Medium
•	computational environment for the reasoning process
•	reasonably efficient
•	organization of knowledge so that reasoning is facilitated
KR for Human Expression
•	a language that can be used by humans to make statements about the world
•	expression of knowledge
•	expressiveness, generality, preciseness
•	communication of knowledge
•	among humans
•	between humans and machines	
•	among machines
Knowledge Acquisition
•	Knowledge Elicitation
•	Machine Learning
Knowledge Elicitation
•	knowledge is already present in humans, but needs to be converted into a form suitable for computer use
Machine Learning
•	extraction of higher-level information from raw data
Knowledge Representation Mechanisms
•	Logic
•	Rules
•	Semantic Networks
•	Frames, Scripts
Logic
Rules
Semantic Networks
Frames, Scripts
Knowledge Manipulation
•	Reasoning
•	KQML
Reasoning
KQML
Post-Test
Evaluation
•	Criteria

Important Concepts and Terms
Summary Knowledge Processing