knowledge management - universitetet i oslo...4 inf5100 - knowledge management - 2007 7 types of...

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Knowledge Management

INF5100 Autumn 2007

Norun C. Sanderson

INF5100 - Knowledge Management - 2007 2

Outline

Knowledge Management (KM) What is knowledgeKM ProcessesKnowledge Management Systems and Knowledge Bases

OntologiesWhat is an ontologyTypes of ontologiesUse of ontologies in KM

KM in Ad-Hoc InfoWare (Example application)Problem DescriptionApproach to KMOntology Based UpdateRescue Ontology Example

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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)

Knowledge KMS & KBsProcessesKM


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

Knowledge KMS & KBsProcessesKM Source: Alavi/Leidner 2001

Perspectives Implications for KM Implications for KMS

Knowledge vis-a-visdata and information

Data is facts, rawnumbers. Information is processed/interpreteddata. Knowledge is personalizedinformation.

KM focus: exposingindividuals to potentiallyuseful information and facilitating assimilationof information.

KMS not very differentfrom existing IS, butwith extensions towardhelping userassimilation ofinformation

Object Knowledge is an objectto be stored and manipulated.

Key KM issue: buildingand managingknowledge stocks

Role of IT involvesgathering, storing, and transferring knowledge

Access to information Knowledge is a condition of access to information.

KM focus: access to and retrieval of content

Role of IT: provideeffective search and retrieval mechanismsfor locating relevant information


Hierarchical view of Knowledge –Common in IT

Data:raw numbers and facts - symbols not yet interpreted

Information:interpreted data - data assigned meaninglinked to specific situation, only limited validity

Knowledge:authenticated, personalized informationenables people to act and deal intelligently with all available information sources (action component)what is considered correct and true, guide behavior and communication applicable in several situations, valid over a relatively long 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. knowing 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



Knowledge Management Processes

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

Storing/retrieving knowledgestorage, organization, retrieval of knowledge

Transferring and Sharing knowledgecommunicating and sharing knowledge

Applying knowledgeintegrate and make good use of knowledge in the organisation


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Knowledge Creation – Nonaka’s SECI Model

Socializationtacit => tacitsocial interaction, sharedexperience

Externalizationtacit => explicitknowledge aquisition, interviews, articulating, codifying

Combinationexplicit => explicitmerging, categorization, reclassifying, synthesising

Internalizationexplicit => tacite.g., learning from books



Knowledge Storage/RetrievalOrganisational memory

storage, organization, and retrieval of organisational knowledgee.g., documentation, structured information in DB, knowledge in expert systems, organisational procedures and processes Semantic memory: general, explicit and articulated knowledge

e.g., organisational archives of annual reportsEpisodic memory: context-specific and situated knowledge

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

Role of IT:Enhance and expand organisational memory, increase access speedEffective 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

Driven by communication processes and information flowsForms of knowledge transfer: informal/formal, personal/impersonalKnowledge 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 to locations where it is needed and can be used is importantKM Systems for KS: knowledge bases, people networks



Knowledge Application

Integrating and using the knowledgeThree primary mechanisms for integrating knowledge to create organisational capability (Grant 1996):

Directives: set of rules, standards, procedures, instructionsOrganisational routines: task performance and coordination patterns, interaction protocols, process specifications Creation of self-contained task teams: specialist teams for problem solving.


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Knowledge Management Processes- Role of ITKM Process Knowledge

CreationKnowledgeStorage/Retrieval

Knowledge Transfer KnowledgeApplication

SupportingInformationTechnology

Data miningLearning tools

Electronic bulletinboardsKnowledgerepositoriesDatabases

Electronic bulletinboardsDiscussion forumsKnowledgedirectories

Expert systemsWorkflow systems

IT Enables Combining newsources ofknowledgeJust in time learning

Support of individualand organizationalmemoryInter-groupknowledge access

More extensiveinternal networkMore communciationchannels availableFaster access to knowledge sources

Knowledge can be applied in manylocationsMore rapid application of newknowledgethrough workflowautomation

Groupware and communication technologiesPlatformTechnologies INTRANETS

Source: Alavi/Leidner 2001Knowledge KMS & KBsProcesses

KM


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

Two main components: knowledge bases and ontologies


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Knowledge Bases

A knowledge base is a databaseUsually domain dependent KBs can use information from both internal and external sources Ontologies in KBs:

knowledge-based specifications – typically describe taxonomy of the tasks that define the knowledge



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 it? Use in KMTypes

Ontologies


What is an ontology

Basically a model of some part of the world, or system (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)

What is it? Use in KMTypesOntologies

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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



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


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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).



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.


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Lightweight vs. Heavyweight Ontologies

An ontology proper has to meet at least these properties:

Finite controlled (extensible) vocabularyUnambiguous interpretation of classes and term relationshipsStrict hierarchical subclass relationships between classes

To the right of red lineCan be used as basis for inference

Source: Ontology Spectrum, (McGuinnes, 2002)



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

Offer a way to cope with heterogeneousrepresentations of resourcesKnowledge representationGive 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 sourceslanguage for translation

3 General approaches: (Wache et. al 2001)Single: global ontology with shared semantics.Multiple: 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 policiesCoordination of knowledge management and resourcemanagement

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

Scenario domain: Rescue and emergency applications

Problem OntoBsdUpdApproachAd-Hoc InfoWare

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Ad Hoc InfoWare – Architecture Overview


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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


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


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Sparse Mobile Adhoc Network

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 SparseMANETs:

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


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KM in Sparse MANETs (SMANETs)

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

Beneficial for dynamic environments (e.g. rescueoperations) to combine middleware infrastructureprovided 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 rescueoperations

Avoid information overflowCross organisational administrationInformation not static, frequent updatesOnly partial view of available information

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


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Knowledge Management (KM) in AdHoc InfoWare

Manage knowledge sharing and integration in a Sparse MANET.Adds 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 knowledgeFocused 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 rescueoperation)


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The Knowledge Manager

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


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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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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


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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 organisationProblem OntoBsdUpdApproachAd-Hoc InfoWare

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Example of Organisation and Structure in Rescue Operations


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Simple Model of Rescue Operation Roles


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Upper Ontology for all Profiles


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Information Profileand exampleinformation priorities


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ExampleshowingUserProfile


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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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Example ofDB contentfor User Profile

4rscex:Team3

nilrscex:TL1

hlth:TmM

rscex:TM6

3rscex:Team3

nilhlth:OiC

hlth:TmL

rscex:TL1

2pr:OSCteam

nilpr:Oschlth:OffIC

hlth:OiC

1pr:OSCteam

nilnilpr:OSco

pr:Osc

pr:hasUpdatePriority

pr:isMemberOf

pr:responsibility

pr:reportsTo

pr:RORoleType

pr:RORId

pr:RescueOperationRole

4rscex:Team3

nilrscex:TL1

hlth:TmM

rscex:TM6

3rscex:Team3

nilhlth:OiC

hlth:TmL

rscex:TL1

2pr:OSCteam

nilpr:Oschlth:OffIC

hlth:OiC

1pr:OSCteam

nilnilpr:OSco

pr:Osc

pr:hasUpdatePriority

pr:isMemberOf

pr:responsibility

pr:reportsTo

pr:RORoleType

pr:RORId

pr:RescueOperationRole

rscex:Team3

pr:OSCteam

...pr:TId

pr:Team

rscex:Team3

pr:OSCteam

...pr:TId

pr:Team

rscex:TM6rc:MMrscex:MMprofile

rscex:TL1rc:LLrscex:LLprofile

hlth:OiCrc:KKrscex:KKprofile

pr:Oscrc:JJrscex:JJprofile

pr:rolepr:personpr:UPId

pr:UserProfile

rscex:TM6rc:MMrscex:MMprofile

rscex:TL1rc:LLrscex:LLprofile

hlth:OiCrc:KKrscex:KKprofile

pr:Oscrc:JJrscex:JJprofile

pr:rolepr:personpr:UPId

pr:UserProfile

Mia Moerc:MM

Lars Lierc:LL

Kari Kirtrc:KK

Jan Jorrc:JJ

pr:namepr:TId

pr:Person

Mia Moerc:MM

Lars Lierc:LL

Kari Kirtrc:KK

Jan Jorrc:JJ

pr:namepr:TId

pr:Person


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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 ManagementSemantic Metadata and Ontology Framework

Sharing and dynamic updateData Dictionary Manager

Viewed as resources to be shared


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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

knowledge management - universitetet i oslo...4 inf5100 - knowledge management - 2007 7 types of...

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