Knowledge Management - Universitetet i oslo · 5 INF5100 - Knowledge Management - October 2006 9...

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Knowledge Management

INF5100 Autumn 2006

INF5100 - Knowledge Management -October 2006 2

Outline

BackgroundKnowledge Management (KM)

What is knowledgeKM ProcessesKnowledge Management Systems and Knowledge Bases

OntologiesWhat is an ontologyTypes of ontologiesUse of ontologies in KM

Ad-Hoc InfoWare (Example application)Ad-Hoc InfoWare and ApproachOntology Based UpdateRescue Ontology Example

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Application Domain:Rescue and Emergency Applications

Participants from different organizationsparamedics, police, fire,…Rescue Site Leader, Team Leaders

Dynamic environmentmovement and activity on sitepersonnel arriving and leaving


Sparse Mobile Adhoc Networks

minimal infrastructure, few nodes heterogeneity, limited resources (battery, bandwidth) a lot of movement; frequent disconnections; delay tolerance

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Knowledge Management (KM) in SparseMANETs

Definition for KM:”…the tools, techniques and processes for the most

effective management of an organization’s intellectual assets” (Davies et al 2003).

Adapted to information sharing in Sparse MANETs:

… effective management of the intellectual assets (information resources) available for sharing in a Sparse MANET


Knowledge Management (KM) in SparseMANETs

Information sharing and content integration not solved sufficiently in middleware for SMANETstoday. KM offer solutions, but these do not consider challenges posed by SMANETs

Beneficial for dynamic environments (e.g. rescue operations) to combine middleware infrastructure provided by SMANET with KM solutions KM solutions may be valuable contribution to SMANETs - and vice versa

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Problem StatementNetwork wide information sharing in rescue operations

Avoid information overflowCross organizational administrationInformation not static, frequent updates Only partial view of available information

Three main tasksEstablish who needs what informationEnable vocabulary sharing & mappingEfficient metadata management


Outline





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What is Knowledge Management?

Started in the 80s, Management Theorywith the notion that all knowledge can be formalizedthe goal was to automatize production processes

Multidisciplinary: political science, communication studies, IT, management sciences,…Knowledge central – seen as part of an organization's competenceCentral questions in KM:

what is knowledge in the production processhow can the knowledge flow be improved


What is Knowledge Management?

”…the tools, techniques and processes for the most effective management of an organization’s intellectual assets” (Davies et al 2003)

“… a dynamic, continuous organizational phenomenon of interdependent processes with varying scopes and changing characteristics.” (Alavi/Leidner 2001)

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Outline






Knowledge - Complementary Definition(Gardner95):

KNOWINGwhat information is neededhow information must be processedwhy which information is neededwhere information can be found

to achieve a specific resultwhen which information is needed

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Perspectives on Knowledge

Source: Alavi/Leidner 2001, p.111


Hierarchical View of Knowledge –Common in IT

Data:raw numbers and facts - symbols not yet interpreted

Information:interpreted data - data which has been assigned a meaningAlways linked to specific situation, has only limited validity

Knowledge:personalized informationenables people to act and to deal intelligently with all the available information sources. (action component)Whole set of insights, experiences and procedures considered correct and true, guide people’s thoughts, behavior and communication. Always applicable in several situations, valid over a relativelylong period of time.

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Types of Knowledge –The most common taxonomy

Explicit: facts, in documents, models, picturesarticulated, codified, and communicated in symbolic form and/or natural language

Tacit: implicit, a mental model, skillsrooted in action, experience and involvement in a specific context cognitive elements: mental models: mental maps, beliefs, paradigms, view-pointstechnical elements: concrete know-how, crafts, skills – apply to specific context, e.g. knowledge of the best way to approach a customer.

Individual: is created by and exists in the individualSocial/Collective: is created by and inherent in the collective actions of a group


OutlineBackgroundKnowledge Management (KM)




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Knowledge Management Processes

Creating knowledgedevelop new - or replace existing - content within an organization’s knowledgesocialization, combination, externalization, internalization

Storing/retrieving knowledgestorage, organization, and retrieval of knowledge

Transferring and Sharing knowledgecommunicating and sharing knowledge

Applying knowledgeintegrate and make good use of knowledge in the organization


Knowledge Management Processes- Role of IT

Source: Alavi/Leidner 2001, p.125

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Knowledge Storage/Retrieval –Organizational Memory

Knowledge in various formse.g., documentation, structured information in databases, knowledge stored in expert systems, organizational procedures and processes

Semantic memory: general, explicit and articulated knowledge

e.g., organizational archives of annual reports

Episodic memory: context-specific and situated knowledge

e.g. specific circumstances of organizational decisions and their outcomes, place and time


Knowledge Storage/Retrieval –Role of IT

Enhancement and expansion of semantic and episodic organizational memoryIncrease speed of access to organizational memoryEffective tools: Query languages, multimedia databases, DBMSsGroupware: enable creation and sharing of intra-organizational memory

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Knowledge Sharing (KS) and Transfer

Sharing vs. Transfer:Transfer: focus, a clear objective, unidirectionalSharing: can be unintentionally, multiple directionally, without a specific objective

may occurbetween and among individualswithin and among teamsamong organizational unitsamong organizations

KM Systems for KS:repositories – databases of knowledge (knowledge bases)networks – facilitate communications among team members or groups of individuals


Knowledge Sharing (KS) and Transfer

Knowledge about where the knowledge is – often as important as the original knowledge itself Sharing this kind of metadata important

E.g. corporate directories: who knows what in organization

Knowledge transfer is driven by communication processes and information flowsForms of knowledge transfer: informal/formal, personal/impersonalKnowledge transfer to locations where it is needed and can be used is important

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Outline






Knowledge Management Systems (KMS)

IT-based systems developed to support and enhance all KM processes Three common applications:

the coding and sharing of best practicesthe creation of corporate knowledge directories the creation of knowledge networks

Requirementsmust provide ontologiesmust provide search capabilitiesoften provide filter capabilities (filters can be computer-based or human-based)provide opportunities for collaboration and use of expertise

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KMS and Knowledge Bases

Two main components of KMSs: knowledge bases and ontologiesA knowledge base is a database

Usually domain dependent Information may need to be abstracted, synthesized, or integrated with other information (e.g. in best practices databases)

Ontologies provide shared vocabulary and facilitates reusability


Outline





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What is an Ontology

The term ontology can mean different thingsglossaries & data dictionaries thesauri & taxonomies schemas & data models formal ontologies & inference

Many definitions… the most commonly used:“An ontology is an explicit specification

of a conceptualization.” (Gruber)


What is an Ontology

Basically a model of some part of the world (Universe of Discourse) Defines a common vocabulary for sharinginformation in a domainSpecifies terms for classes/concepts and relations between these

informal text or using formal language (e.g. predicate logic)

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Ontology Modelling & Implementation

Can be modelled using different knowledge modelling techniques and implemented in various kinds of languagesHeavyweight ontologies:

AI based languages (framebased, first order logic): e.g., Ontolingua, LOOM Ontology mark-up languages: RDF(S), DAML + OIL, OWL

Only Lightweight ontologies :Techniques from software engineering & databases: UML, ER, SQL-scripts Not as expressive






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Types of Ontologies

We will look at two categorizationsThese are based on

the richness of the internal structureLightweight ontologiesHeavyweight ontologies (ontology proper)

the subject of their conceptualization


Lightweight Ontologies

Catalogs: controlled vocabulary – a finite list of terms.

Glossary: list of terms and meaning as natural language statements. Not machine processable.

Thesaurus: a networked collection of controlled vocabulary terms synonym relationship. No explicit hierarchy.

Informal is-a hierarchies: not strict subclassTop-level categories and specifications of these (e.g. Yahoo).

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Heavyweight Ontologies

Formal is-astrict subclass hierarchies, necessary for exploiting inheritance

Formal instance relationships (formal is-a) includes domain instances

Framesontology includes classes with property information. All subclasses inherit properties.

Value restrictions More expressive ontologies, can place restrictions on values that can fill a property.

Expressing general logical constraintsthe most expressive, first order logic.


Lightweight vs. Heavyweight Ontologies

Ontology Spectrum, (McGuinnes, 2002)

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Types of Ontologies Based on the Subjectof the Conceptualization

Top-level ontologies aka Upper-level ontologies, general concepts, existing ontologies link root terms to these (e.g. Cyc, SUMO)

Domain ontologiesReusable in a specific domain (KM, medical, law, engineering, chemistry etc. )E.g., UMLS (medical)

Application ontologiesapplication dependent, often extend & specialize vocabulary of a domain






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Use of Ontologies in KM

Knowledge representationOffer a way to cope with heterogeneousrepresentations of resourcesGive shared and common understanding of a domainCan be communicated between people and application systems


Information Sharing and Integration

Interoperability problem have to make the different systems and domainsunderstand each other

Structural heterogeneitydata structures, schemasolutions from domain of distributed databases

Semantic heterogeneitymeaning of contentontologies possible solution

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Ontologies in Information Integration

As solution to semantic heterogeneity problem: explicitly describe semantics of information sources language for translation

3 General approaches: (Wache et. al 2001)Single: global ontology with shared semanticsMultiple: need mapping between (each pair of) ontologies (inter-ontology mapping)Hybrid: multiple ontologies are built on top of or linked to a shared vocabulary of basic terms (may function like a bridge or a translation)


Single, Multiple, and Hybrid Ontology Approaches

single ontologyapproach

hybrid ontologyapproach

global ontology

multiple ontologyapproach

localontology

localontology

localontology

localontology

localontology

localontology

shared vocabulary

Or Top-level ontology

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Outline






Ad-Hoc InfoWare

Simplify application development for Sparse MANETSConfigurable MW services

scalable protocols and servicesTradeoff

between abstraction and awareness of location, resources, context,...between non-functional requirements, e.g. performance vs. security and availability

Separation of mechanisms and policies Coordination ofknowledge management and resource management

Integration of informationInformation, data, meta-data, resourcesContext awarenessResource and QoS aware data placement

Scenario domain: Rescue and emergency applications

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Ad-Hoc InfoWare– Architecture Overview

Watchdogs

WatchdogsManager

WatchdogsExecution

Envir.

Resource Manager

Replic.Mgnt

ProposalUnit

Resource Monitor

Adjac. Monitor

Local Monitor

Resource Avail.

Distributed Event Notification Service

Delivery

StateMgnt

Availability & Scaling

StorageMgnt

Security and Privacy Manager

Authentication Access Control Key Management Encryption

Knowledge Manager

Semantic Meta-data & Ontology

Framework

QueryMgnt

XML/RDFparser

Profile &Context Mgnt

LDD

SDDD

Data Dict.Manager


Knowledge Management (KM) in Ad-Hoc InfoWare

Manage knowledge sharing and integration in a Sparse MANETAdds layer of knowledge Services that allow relating metadata descriptions to semantic context.Only give tools (not decide usage & content) Share information about where to find knowledge about what

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Related to KM Elements

Hierarchical view of knowledgeExplicit knowledge Focused KM processes: Storage/Retrieval and Transfer ( or Knowledge Sharing)

Not addressing learning aspect (knowledge creation)Use of ontologies

Domain ontologies, e.g. medical, police, fireUpper level ontology/ shared vocabulary (similar to Hybrid approach)Ontology based updateMetadata enriched with terms/concepts from ontologiesOnly ontology use (development etc not during rescue operation)


The Knowledge Manager

DistributedEventNotificationSystem

Watchdogs

ResourceManagement

Security and Privacy Management

Data Dictionary Mgnt.

Semantic Metadata &Ontology Framework XML

Parser

Profile & Context Mgnt

Query Mgnt

Knowledge Manager

LDD SDDD

UNDERSTANDING

INFORMATIONOVERLOAD

EXCHANGE

RETRIEVAL

AVAILABILITY

SDDD = Semantic Linked Distributed Data Dictionary. LDD = Local Data Dictionary.

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Three Types of Metadata

Information structure and content descriptionmetadata

Data Dictionary ManagementContent, formats, data types etc

Semantic metadataSemantic Metadata and Ontology FrameworkRelations between concepts,

e.g. is-a, hasPart, hasResource, hasDeviceTypeProfile and context metadata

Profile and Context ManagementUser profile, device profileContext: location, time, situation


Profiles and Context

ProfilesWhat, who

Device type, resources, groups etc (for device profile) User preferences, roles, personalia etc (for user profile).

Fairly static informationContext

Where, when, whylocation, time, situation (e.g. rescue operation)

Dynamic information (network nodes moving)Used in different meanings (the term context)

time, location and situation for a device or usersemantic or topical context

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SDDD –linking level

(Instance)

(Link)

LDD –metadata

Semantic/ topicalContext

Informationlayer

Conceptual

Implementation

Ontology layer

SDDD = Semantic Linked Distributed Data Dictionary. LDD = Local Data Dictionary.

Three-layered Approach


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Approach to Ontology Based Update

Ontologies to representrescue operation context modelprofiles for user, device and information

Update prioritiesinformation types rescue operation roles

Operational structure and organization


Issues of Dynamic Update

Dynamicity and limited resourcesunstable availability

Frequent updatesincreased communication needsconsistency issues

Need efficient metadata management to achieveontology based update in this environment

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Kinds of Dynamic Update- Overview

BetweenEntities

Metadata or Data

Change or Append

(Vertical)

Local updateDifferent leveldata dictionaries

Metadata Both

(Horizontal)MetadataExchange

SDDDs Metadata Append

(Horizontal)OntologyBased

SDDDs & KBs Both Both

SDDD = Semantic Linked Distributed Data Dictionary. KB = Knowledge Base.


Outline





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Example of Organization and Structure in Rescue Operations


Simple Model of Rescue Operation Roles

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Upper Ontology for All Profiles


Information Profileand ExampleInformation Priorities

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UserProfile


Example of DB SchemaInformation Profile:

pr:InformationProfile(pr:IPId, pr:item)pr:InformationItem(pr:IId, pr:subject, pr:priority)pr:InformationPriority(pr:IPrId,...)

UserProfile:pr:UserProfile(pr:UPId, pr:person, pr:role)pr:RescueOperationRole(pr:RORId, pr:RORoleType, pr:reportsTo,

pr:responsibility, pr:isMemberOf, pr:hasUpdatePriority)pr:Responsibility(pr:PId,...)pr:Team(pr:TId,...)pr:Person(pr:PId, pr:name,...)

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Exampleof DB Contentfor UserProfile


Rescue Scenario Timeline –Populating the Knowledge Base

Phase 1: initial population of knowledge basePhase 2: ontology individuals for current operationPhase 4: adjustments: changes and new arrivals

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Handling Profile Ontologies in our Architecture

Storage - who keeps what? Based on user role in rescue operationEach node keeps its own device profile and user profile

ComponentsRescue ontology profiles

Profile and Context Management Semantic Metadata and Ontology Framework

Sharing and dynamic updateData Dictionary Manager

Viewed as resources to be shared


Litterature

M. Alavi and D. Leidner. Knowledge Management and Knowledge Management Systems: conceptual foundations and research issues; MISQuarterly Vol. 25 No.1, pp.107-136, March 2001. http://www.coba.usf.edu/departments/isds/faculty/abhatt/rm/Alavi01-KnowledgeManagement.pdf

Deborah L. McGuinness. "Ontologies Come of Age". In Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2002. http://www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-mit-press-(with-citation).htm

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Knowledge Management - Universitetet i oslo · 5 INF5100 - Knowledge Management - October 2006 9...

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