© 2006 Tom Beckman Knowledge Representations for Semantic Interoperability Conceptual Primitives...
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Transcript of © 2006 Tom Beckman Knowledge Representations for Semantic Interoperability Conceptual Primitives...
© 2006 Tom Beckman
Knowledge Representations for
Semantic Interoperability
Conceptual PrimitivesKnowledge Structures
Reasoning Mechanisms
Tom BeckmanPrincipal, Beckman Associates
[email protected] 202-362-5774
© 2006 Tom Beckman
Outline
Forms of Knowledge Representation Knowledge Representation Dimensions Content Knowledge Forms Concept Dimensions Conceptual Primitives Knowledge Structures Reasoning Mechanisms Generic Tasks Semantic Web Services
© 2006 Tom Beckman
Introduction
Artificial Intelligence Disciplines Applied to Semantics Expert Systems Linguistics and Natural Language Understanding Machine Learning
Explicit Representation of: Knowledge Experience Expertise
Knowledge Representation Categories: Semantic: Symbols, numbers, language, meaning Sensory: Images and signals are interpreted into symbolic
form
© 2006 Tom Beckman
Knowledge Representation Basics
Knowledge Representation Characteristics: Models knowledge and reasoning about knowledge Describes characteristics and dimensions of knowledge Formally defines structures and processes for electronic and human
reasoning Exposes Knowledge Structures and hides Inference Engines
Symbolic Knowledge Representation is comprised of two parts: Knowledge Structures: Objects Declarative Static Reasoning Mechanisms: Process Procedural Dynamic
Knowledge Structures consist of symbols that explicitly define, describe, organize, and link knowledge Nodes: Symbols as concepts Links: Relations between symbols
Reasoning Mechanisms process Knowledge Structures in order to: Solve problems, create knowledge, explain reasoning & results Calculate measures of uncertainty and importance to improve
© 2006 Tom Beckman
Forms of Knowledge Representation
Numeric: Most precise reasoning Needs explanation of computing and results Often hidden – working in the background behind symbols and
text Symbolic:
Less precise but most concise Ease of reasoning and explanation
Linguistic: Best for explanation and understanding Not as precise, and not concise Hard to directly reason with – language parsers
Image: Described and classified using taxonomies and metadata
Signal: Time serieds are described, interpreted, and classified using taxonomies, metadata, and analysis engines
© 2006 Tom Beckman
Knowledge Representation Dimensions
Concept: Symbolic Format: <Concept Attribute Value> Concept Types: Object, Entity, and Abstraction Domain content knowledge
Structure: Declarative representation Composed of Nodes and Links Expert System types parallel human cognitive schema
Process: Procedural representation Reasoning and Inference Modeling and Simulation
© 2006 Tom Beckman
Concept Dimensions
Meaning: is nothing more than the sum of these concept dimensions
Definition Attributes:
Stereotypical description of characteristics Format: <Concept Attribute Value>
Relations: Between concepts Between attributes
Linguistics: Part of speech Grammar rules
Context: Based on user experience and purpose Common understanding
Mental Models and Cognitive Schema
© 2006 Tom Beckman
Concept Typology Objects:
Inanimate physical objects with characteristics: size, shape, color Man-made and naturally occurring objects Behaviors obey natural laws of physics and chemistry
Entities: Animate systems possess purpose and goals Types: Humans, animals, electronic agents, and plants Sense, reason, and take actions Humans and animals have emotions, values, beliefs, and drives Possess behaviors, procedures, and methods Have resources: memory, knowledge, skills Communicate with other intelligent entities
Abstraction: Created by entities to describe, order, and classify the world, perform
tasks, and model systems Represent and model real, mental, and virtual worlds Semantic and symbol based representations Obeys laws of inference in closed systems
© 2006 Tom Beckman
Conceptual PrimitivesKnowledge Templates are key conceptual primitives:
Represent assertions – the basic building blocks of structures Define, describe, and detail symbol features, values, & relations Can also represent Uncertainty & Importance Come in several standard templates:
Basic: <object attribute value> Faceted: <object attribute facet method> Measured: <object attribute facet value importance uncertainty>
Declarative Conceptual Primitives: Feature Descriptor: <Object Attribute Value> Ex: <Car Color Red> Relation: <Symbol Relation Symbol> Ex: <Ford is-kind-of Car>
Procedural Conceptual Primitives: Action: <Symbol Action Object> Ex: <User Places Order> Inference:<Predicate Method Assertion> Ex: <Formula Computes Price>
These knowledge elements have certain properties: Naming (Object) Describing (Attribute and Value) Organizing (Hierarchy) Relating (Functional, Causal, & Empirical Links) Constraining and Negating (Networks and Rules)
© 2006 Tom Beckman
Attribute Value Typology
Numeric: Ordinal: Likert Scale Interval: Range, Continuous Variable Continuous: Ratio, normalized continuous variable
Semantic: Text Value Types:
Unstructured: Instant Messaging Semi-Structured: Email, Memo Structured: Document, Hypertext
Symbolic Value Types: Binary: Boolean Categorical: Unrelated Nominal Ordinal: Related Nominal
Sensory: Image: Digital spatial array, picture, video Signal: Time series, audio, sensor
© 2006 Tom Beckman
Types of Concept Relations
Synonym and Antonym Typing and Metadata Hierarchy: Taxonomy and Ontology Composition: Object parts Network: BBN, NN, Fuzzy Sets, Semantic, & Constraint Causal: Inference chains Association: Statistical and Bayesian Temporal: Model/Process of ordered activities Physical: Location, relative physical arrangement
© 2006 Tom Beckman
Expert System Typology
Case or Similarity Rule Object Network Process/Model Hybrid Expert Systems are explicit representations of
human cognitive schema
© 2006 Tom Beckman
Reasoning Mechanism Typology Document Search: Keyword Bayesian search Database Query: Relational and Dynamic Queries Web Query: Keyword Search, Semantic Search Search Engines: Brute Force, Beam, Best-First Similarity-Based Reasoning: Cases Forward & Backward Chaining: Rules Graph Reasoning: Networks and Decision Tree Logic: Propositional, FOPC, Fuzzy Statistical and Bayesian Reasoning Object Methods: Inheritance and Classification Simulation and Modeling: Process Concept Classification: Metadata and Metatagging Natural Language Understanding Analysis Methods: Data Mining, Text Mining, & Knowledge Discovery Machine Learning
© 2006 Tom Beckman
Semantic Web Components Domain Content: Knowledge, experience, and expertise Domain Taxonomy and Ontology:
RDF/OWL Object Methods: Inheritance and Classification
Organization and Structure: Web sites and document collections Classification Methods:
Similarity-based Rule-based Network-based Object-based
Indexing: Item typing and meta-tagging Linguistics: Natural Language Processing & Text Generation Search Query: Keyword Bayesian Search & Semantic Search Entity Extraction: People, places, and events Analysis Methods: Data Mining, Text Mining, Link Analysis,
Machine Learning, & Knowledge Discovery Intelligent Agents: Simulation and Modeling
© 2006 Tom Beckman
Methods to Increase the Value of Knowledge
Apply KnowledgeCreate KnowledgeCapture KnowledgeOrganize KnowledgeShare KnowledgeAbsorb KnowledgeImprove Quality of Knowledge
© 2006 Tom Beckman
Apply Knowledge
Perform a TaskManage a TaskMake a DecisionSolve a ProblemImprove Task Performance
© 2006 Tom Beckman
Create Knowledge
Create a new KM framework: Structure/Taxonomy Content/Knowledge Function/Process/Method System Outputs/Performance Resources
Organize and combine existing knowledgeSynthesize new knowledge through research and analysisInnovate towards a stretch goalBrainstorm and other directed creativity
© 2006 Tom Beckman
Capture Knowledge
Identify knowledge sources:Organizational DocumentsBooks, Journals, and Internet Internal Subject Matter ExpertsExternal Consultants and Experts
Define key knowledge subjectsElicit key knowledge from internal SMEsDevelop core competencies
© 2006 Tom Beckman
Share Knowledge
Communication Media: Web Sites and Email Publications Meetings Communities
Define audiences and contextOrganize communities of practice and interestHold workshops and seminars to solve problems, make decisions, and develop new knowledge
© 2006 Tom Beckman
Absorb Knowledge
Learn new concepts and practices: Read books and journals Take classroom training courses Gain experience Peer and community seminars
Learn as groups and individualsIdentify best practicesOrganize and combine existing knowledge
© 2006 Tom Beckman
Improve the Quality of Knowledge
Transform Knowledge Up the Value Taxonomy
Capability
Expertise
Knowledge
Information
Data
Sensory
© 2006 Tom Beckman
Valuation of Knowledge AssetsKnowledge is intangible and difficult to measure/value Most knowledge loses value or depreciates over timeThe value of knowledge depends on several factors: * user need: correct context and level of knowledge * user experience and awareness of value * formalization and organization of knowledge * knowledge currency: latest/best research and results * knowledge availability: ease of access and timeliness * format options and presentation customization * ease of sharing knowledgeKnowledge provides organizational value through its creation, sharing & use: * improved performance * more effective management, better decisions * increased learning and innovation * increased knowledge sharing, teamwork, collaboration * increased value of knowledge asset through formalization & organization of knowledge * anticipated benefits: synergy & cooperationKnowledge also conveys personal/political power: * knowledge hoarding gives power to the holder who gain personally by filtering/
suppressing bad news and broadcasting good news * openness and sharing may force functions to act in ways opposed to local interests
© 2006 Tom Beckman
Methods to Increase Intellectual Asset ValueCollect/Uncover Existing Knowledge * read & research * attend conferences & seminars * buy commercial databases and knowledge sourcesMake Knowledge Explicit * put knowledge in electronic form * collect existing available explicit knowledge * query humans for implicit knowledge * observe behaviors, relationships and events * elicit tacit knowledge from domain expertsOrganize and Structure Explicit Knowledge * create a knowledge repository and Web site * classify, edit and maintain the repositoryShare Existing Knowledge * access knowledge repository and Web site * develop communities of practice * sponsor conference & workshops * build help desksApply Knowledge to Perform WorkCreate New Knowledge * research, reflect, discuss, hypothesize, experimentTransform Knowledge Up the Knowledge Hierarchy
© 2006 Tom Beckman
Technology Resource Class
P h ys ica l
In te llec tu a l
W o rk fo rce Technology
F in a n c ia l Smart System Methodology incorporates many innovative IT concepts, disciplines and technologies:o Enterprise Architecture
o Artificial intelligence, Advisory Systems, Object methods
o Machine learning: deduction, induction, genetic algorithms, NN
o Natural language and text generation
o Knowledge representation & knowledge elicitation
o Knowledge exploration & discovery
* data mining * text mining
* data visualization * link and event analysis
o Knowledge repository & performance support systems
o E-Learning and intelligent tutoring systems
o Web services: portal, search, content management
o Semantic technology: taxonomies, metatagging
o Business and competitive intelligence
Proper application of ITcan enable all
Business System Model components and greatly increase
Stakeholder value
© 2006 Tom Beckman
P h ys ica l
In te llec tu a l
W o rk fo rce Technology
F in a n c ia l
Intellectual Asset Resource ClassResource conservation does not apply to knowledge * knowledge is not consumed or depleted through use * knowledge is multiplied through sharing * knowledge often increases through sharing and use * knowledge can be used simultaneously by multiple processes
Intellectual assets in human form are volatile and can disappear or become unavailable overnight * employees are promoted or get new job assignments * employees leave the firm for other career opportunities * employees retire or are downsized/dismissed * functions are outsourced * employees/organizations may refuse to share expertise
Costs of acquiring/developing/creating knowledge * knowledge acquisition costs can vary widely (from buying books, to knowledge elicitation and new hires) * development costs are often high (lessons learned, education, learning, research, experimenting) * costs of storing, sharing, using knowledge are low, especially if already have IT infrastructure
Knowledge Management, a type of resource management * delivered at the right time * available at the right place * present in the right format * satisfies quality requirements * obtained at the lowest possible cost
© 2006 Tom Beckman
Building Competency and Knowledge
IRS Criminal Investigation Special Agents identify & develop fraud cases for prosecution by Justice Dept The unit of work is the case; 18 months is average time to work
1. Novices are taught concepts and case exemplars in classroom by experts and senior practitioners
2. Novices apply concepts and examples to real cases under guidance of senior practitioners
3. Practitioners work cases and document their experiences & results from cases into a Case-Base (a type of expert system & a part of a knowledge repository)
4. Case-based reasoning expert systems are used to research and find similar cases
5. Experts abstract and generalize from cases and create models (Model-Based Reasoning), principles, rules (Rule-Based Systems) and guidelines
6. Senior practitioners and experts engage in Communities of Practice and hold peer workshops to further develop their concepts, models and practices
7. Senior practitioners and experts also develop & refine Knowledge Repositories, consisting of documents and Expert Systems
© 2006 Tom Beckman
Knowledge Representations for
Semantic Interoperability
Questions?
Tom BeckmanPrincipal, Beckman Associates
[email protected] 202-362-5774