Centralized vs Distributed Knowledge Management:Is it the right question?

Matteo Bonifacio

Department of Informatics and Business Studies – University of TrentoAutomated Reasoning Division - ITC-IRST

Outline

•Centralized Knowledge Management•Overview•Main assumptions•Main weaknesses

•Distributed Knowledge Management•Overview•Main assumptions•Demo: A peer to peer solution for distributed KM

•Evolutionary Knowledge Management•Limits of DKM•Knowledge as a process•Knowledge as a pragmatic matter

Centralized Knowledge Management

Knowledge as content and the syntactic problem

KM Scenario: 1996-2001

•At the beginning of the 90’s, Knowledge started been described as the emerging core asset of modern organizations and societies

•The knowledge society and workers (Peter Drucker)•The knowledge creating company (Ikujiro Nonaka)•The managerial discipline (Peter Senge)

•Organizations must be able to capture knowledge and reuse it generating scale and scope economies•Companies have invested huge amounts of money (HP 20 M Euros) in order to manage knowledge through technology adopting content management “carriers” called corporate knowledge portals (Vignette, BroadVision, Autonomy)

Traditional centralized KM architectures

Conceptually, KM architectures are usually composed by:

Collaborative environments: in order to facilitate the generation of “raw knowledge”Contribution workflows: in order to codify and standardize raw knowledgeKBs: in order to collect contents organized according to a corporate conceptual schemaEKP: in order to provide a single point of access for the members of different organizational units

Enterprise knowledge portal

KB

Collaborative tools

Contribution WfS

Centralized KM: assumptions

•Knowledge is a content that is encapsulated in some artefact (e.g. the document) through the use of a non ambiguous language. Meaning can be embedded in language

•Semantic heterogeneity is a noise to knowledge flows: the fact that people speak different languages is just a syntactic problem (conduit model)

•Thus, knowledge can be standardized, centralized, and controlled through a linear process that “cleans” diversity

•Technology is a neutral medium through which messages are standardized, stored and trasmitted from a sender to a receiver

Some problems

Centralized KM systems didn’t match expectations:– deserted by users that continue to develop, install and use local applications (7000 LN DBs at Andersen)– not flexible nor interoperable and thus unable to adapt to organizational change and differentiation

(Merging Banks, changing operating models)– very difficult to maintain (people and resources are needed to keep it updated and populated, 500

people at Accenture)– still benefits are not demonstrated (number of contributions and hits… measure of junk?)

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“KM Has Greatly Underperformed the Tech Sector”Stefano De Vescovi, Principal ETF Group, EDAMOK Board Member

But…

“A particularly intriguing characteristic of KM is that it has not faded as a serious management concern despite its shortcomings as a discipline in failing to provide organizations with all it has promised”Report on Knowledge Management to the European Commission, 2004

1995 1996 1997 1998 1999 2000 2001 2002

KM

eBusiness

ERP

Why?

“KM is a crucial competence in the new competitive arena… but the degree of predictability which has been inerent in KM thinking, reflecting the general belief in linearity, is now seroiusly questioned.”Report on Knowledge Management to the European Commission, 2004

The DKM general claim is that the weaknesses of KM is not due to a lack of KM needs nor to technicalities, but rather to the core linear design principles that underlie KM systems

The specific claim is that current KM systems view diversity and locality as a constraint that needs to be reduced, rather than an opportunity to be exploited.

Distributed Knowledge Management

Knowledge as context and the semantic problem

Lost local knowledge work

Looking for example at a corporate intranet, we find a lot of local knowledge capacities that are ignored:

– Local processing capacity – Local personal contents (estimated at nearly 70% of total)– Local indexing services (from Windows, to Lotus Notes all have indexing services)– Local category systems (file system structures, directories, taxonomies)– Local relational and social info (security, links, favourites)

This capacities represent investments in local technologies and knowledge work. Are they really waste?

Legacy Databases

Text Analysis tools

Shared Directories

Content Management tools

Intranet/internet sites

KM Portal/Platform

Personal

directoris

Categories as local interpretative context

Category systems are interpretative contexts that are used by a knowledge worker to:– organize his knowledge (what do I mean for “Java”)– understand how the others organize knowledge (what do they mean for “Java”)– compare his understandings with the ones of others (do we mean the same when

referring to “lake”)

LESS GENERAL THAN

Personal File system

IMAGES

JAVA

SEA LAKE

UNRELATED

By java I mean an

island By lake she

intends all

By java they

mean a language

Intranet Site

SOFTWARE

LANGUAGES

LISP JAVA

IMAGES

INDONEASIA

SEA LAKE

Personal internet site

Suggeste

KM experts

Personal internet site

Suggeste

Unexploited knowledge work I: categories (interpretative contexts)

People find general category structures either irrelevant or oppressive (Bowker&Starr 00). They tend to develop and embed in technology local interpretative schemas (Contexts) that are used to make sense of their activities.

KM should not be under the

category technology

KM should be split according

to processes and not tools

Social accounts as local relational context

Through social accounts, people perform a double sided process (Wenger 98):– Qualify knowledge: give relevance, underline, validate, recommend– Motivate sharing: establish reciprocity, give visibility, provide membership

Without these social accounts, information tends to become meaningless and motivation to share decreases (Mantovani 96)

Suggested articles on KM

KM experts

Suggested links to KM groups

Protected areas:

–for Marie Curie Project members

–For philosophy dinner attendants

Suggested web directories

Security

We are part of…

References to knowledge

This is a relevant knowledge object

References to people

Suggeste

KM experts

She’s a relevant knowledge expert

References to concepts

Brint.com

Articles\KM\DKM

References to groups

www.knowledgeboard.org

They possess good knowledge of…

This concept stands for…

Unexploited knowledge work II: social accounts (social contexts)

Centralized systems hide implicitly an ideology of sharing: all share with all People don’t believe in such an ideology but rather continue to develop and embed in

technologies a web of social accounts (contexts) that give meaning to contents

Suggeste

KM experts

Give access to…

Link to…

Refer to..

Recomend to..

Subscribe to..

KM Portal/Platform

CM application

Personal site

Laptop

Group site

Desktop

Link to..

Knowledge as Context

Besides the global view of knowledge viewed as content, the DKM view suggests that knowledge should be seen as a web of local “knowledges” made up of contents that have meaning within Local Interpretative Contexts and Local Social Contexts

Meaningful knowledge exchange processes become possible only if contextual information is exchanged

Knowledge as content

Context

Content

Local “Knowledges”Global Knowledge

Interpret content

Interpret other

contexts

Address to trusted experts

DKM: the vision

•KM doesn’t stand for the linear process of creating, codifying and disseminating knowledge but rather…

•Managing Knowledge means supporting two qualitative different processes that characterize its social architecture:

•Perspective making and Taking (Boland, Tenkasi), Single and Double Loop Learning (Argyris and Schon), Exploitation and Exploration (March), A1 and A2 learning (Bateson), Normal Science and Paradigmatic Shifts (Kuhn), Innovation and Continuous Improvement (management in general)

•This means supporting the autonomy of each local knowledge (within a context-community, interpretative schema, identity, practice, language) and enabling some form of coordination (among different contexts-cross-fertilization, interoperation, meaning negotiation)

Social architecture of knowledge

Autonomy

Coordination

Context

Context

Context

Why centralized KM systems failed?

•Traditional KM systems failed because their centralized architecture was in contradiction to the distributed one that characterizes knowledge

Portal

KB

Social architecture of knowledge Technological architecture of knowledge

DKM Technologies in EDAMOK

A KM technological architecture must be consistent to the social organization of knowledge

It must support autonomy and coordination among heterogeneous knowledge nodes and communities

A KM system supports a network of specialized and heterogeneous sets of technologies, conceptual structures, and social accounts that need to cooperate in order to achieve their goal.

Social and technological architecture of knowledge

AutonomyCoordination

Lotus Notes Team Room

Intranet Site

User DeskTop

Lotus Notes DB

Content Management toolUser

DeskTop

User DeskTop

EDAMOK: DKM technological architecture

ia

Context

ia ia

•Autonomy: tools and methodologies to sustain extraction, representation and manipulation of local contextual knowledge

•Personal Knowledge Manager•Source Peer Manager

Context

MEANING COORDINATION

•Coordination: communication layer to support meaningful knowledge exchange processes

•SUN’s JXTA open source p2p protocol•Higher level communication services•Semantic matching protocol

Suggeste

KM experts

Extract

Extract Extract

Context

Team CM tool

Personal site

Personal file system

A DKM Technology: the Knowledge Exchange System (KEx)

A peer to peer system to support semantic interoperability across heterogeneous and cooperative knowledge sources

DKM: assumptions

•Knowledge is an intrinsically contextual matter. Contents (e.g. documents) gain meaning only within contexts (e.g. classifications)

• Semantic heterogeneity is not a noise since:•It is irriducible: people have limited knowledge capacities and thus they have to focus on different portions of the world (Bounded Rationality) •It should not be reduced: people generate different world views that express their values, identities, and preferences (Relativism)

•Nonetheless, these different interpretations need to meet in order to:•Sustain coordination (diversity as cognitive division of labour)•Sustain innovation (boundaries as places where discontinuity occurs)

•To do so, knowledge must be “translated” among these different interpretations

•Technology should be used to support processes of both semantic contextualization and semantic translation

Knowledge Nodes

Knowledge Nodes are social entities (individuals, teams, communities, BUs) that “own” a local knowledge in terms of a content that has meaning within a context

That is, a KN is a social actor that displays some degree of semantic autonomy

A lotus notes team room

A community internal web site

An individual’s file system directory or outlook folders

ContextContent

Local Knowledge

KEx Roles 1: Seeker, Provider, Broker

Contextslexical index

I C I C

I C

I C

I C

I C

A KEX peer is the technological transposition of a KN

A KEX Peer plays the double role of knowledge seeker and provider (and broker)

Since Knowledge is content within a context, a KEX peer has access to the user local contextual information (metadata), content and relational knowledge.

Currently, contextual information is given by contexts and lexical indexes

P

PP

P P

P

Keyword1 Doc1, doc5,…

Keyword2 Doc10, doc3,…

Keyword3 Doc1, docn,…

Keyword4 DocM, doc17,…

Keyword5 Doc2, doc6,…

KEx Roles 2: Federations

Federations are spontaneous groupings of K-Peers that want to appear as a sole entity in respect to the ptp system

When a query is sent to a federation, it will be forwarded to each federation member

Office 1

Peer Group

Office 2

Peer GroupOffice 3

Peer Group

Query

The Context Editor

A KEX Peer wraps contexts from the owner’s local technologies and provides a tool to manage contextual information: the Context Editor

– Extraction of local structures– Definition of new and multiple views on knowledge– Automatically generated from docs (in progress)

P

P

Outlook pst file

File systemLotus Notes repository

Other content repositories

Sharing documents

Share these documents

KeywordIndexingService

File system

Association to concept

Add to index

Document path + Document name

Document path + Document name

Document path + Document name

Document path + Document name

Semantic index

Concept ID

Concept ID

Concept ID

Concept ID

Security andMembership

policies

Decide sharing level

Peers Discovery

Each KEx peer advertises his/her presence in the network and dynamically can discover who is available in the moment for knowledge sharing

Peers can be either user or source peers. No centralized directory service is needed

P

P

P

Advertise a user peer

Discovery

P1

Advertise a source peer

P2

P

Advertise a user peer

P3

Federation Discovery

KEx peers can create dynamic groups of peers that advertise themselves as a single source

Through discovery, peers can find which federation is available and who is member

P

P

P

Discovery

F1

Advertise a federation

P

Advertise a federation

F2

Querying…

Each peer can use his contexts to browse local knowledge but also to query other KNs: Queries can be of three types:

– Keyword querying: look for information that contain some keyword– Semantic querying: look for information that are categorized in a way which is relevant to my selected

concepts– Conceptual querying: look for information that are categorized under a certain concept

P

P

P

Keywords Query+

Focus

Select a target for the query

Select a target for the query

Select a semantic focus

Select a semantic focus

Select keywords

Select keywords

The Semantic Matching protocol

Each peer provides a semantic matching service that is able to establish semantic relations across different contexts. The SM Protocol uses both structural and linguistic information.

Mappings are deduced via logical reasoning rather than derived from heuristic procedures (from structural or linguistic similarity to the problem of deducing relations between formulas that represent the meaning of each concept in a model)

P

P

Lexical IndexingService

Semantic match

Semantic match

Keyword match

Keyword match

Keywords

Result

+

Keywords+

Learning and Semantic Bookmarking

The user can decide to store semantic mappings that relate his concepts to:– Documents stored somewhere else (content knowledge)– Other relevant categories– Peer and federations that are relevant (relational knowlede)

Bookmark to…

Bookmark to…

Semantic Propagation (Brokering)

Semantic propagation: forward my query to semantically relevant peers

P

P

Keywords Query+

Focus

Semantic match

Semantic match Bookmark+

Result

Forward to other relavant docs, peers, categories and federations

Forward to other relavant docs, peers, categories and federations

Broker

Seeker

Provider

P

A Knowledge system as a semantic web of knowledge technologies,contents, concepts, people, and groups

And organizations as part of k networks

Sector 2

Temporary consortium

Peer Network

Peer Group

Peer Group

Peer Group

Peer Group

Peer Group

Sector 3Sector 4

Sector 1

Distributed KM: problems

•The practitioner: how can we sustain reliable knowledge exchange processes if nothing is stably shared? How can we take into account the fact that there exists some stable organizational or group level shared knowledge?

•The researcher: how can meanings be translated if we don’t agree at least on some transformation rule? How can we understand each other if we don’t have some stable external referent to point to?

Evolutionary Knowledge Management

Knowledge as process and the pragmatic problem

The debate around distributedness and centralization

The KM debate (but not only) gravitates around an ideological dichotomy

The Distributed KM view:

•Knowledge is intrinsically contextual and distributed

•Semantic interoperability is achieved by means of runtime mappings

The Centralized KM view:

•Knowledge heterogeneity is a noise

•Semantic interoperability is achieved by means meaning standardization

Dichotomy

Contexts: Private, and subjective views of a knowledge domain

Ontologies: Public, and “objective” representations of a

knowledge domaine.g. Peer2Peer KM e.g. KM Portals

Assumption 1: status/level

Both approaches share an assumption: that the problem of semantic heterogeneity is a problem of level or status

That is, they try to answer the question: What is the status of knowledge? what level of heterogeneity/standardization is acceptable?

The Distributed KM view:

•Status: Knowledge is subjective

•Level: Maximum heterogeneity should be preserved

The Centralized KM view:

•Status: Knowledge is objective

•Level: Maximum standardization should be achieved

Arguments:

•Every agreement may be wrong

•Every agreement may change

•Diversity is a source of value

Arguments

•People look for shared meanings

•Stability is needed for reliable coordination

•Standardization is value

Dichotomy

The centralization/distributedness trade-off

It’s increasingly evident to KM researchers and practitioners that this dichotomy hides a trade-off:

The Distributed KM view:

•Pros: flexibility, adaptability

•Cons: incapable to deal with critical transactions

The Centralized KM view:

•Pros: stability, reliability

•Cons: incapable to deal with change

Trade-off

Organizations need both flexibility to deal with changing environments and consolidation in order to

manage critical transactions

Current solution and drawbacks

Trade-off

Corporate KM

Individual and community KM

Project or BU KM

P2PKM Distributed Content

Management

KMPortal

needs to converge and share a

taxonomy in order to manage a project

needs to converge to some shared corporate

language

need to re discuss the corporate language

Such evolving process happens, and is usually managed “outside” technology (e.g standardization committees, delegations, meetings)

Given a particular knowledge domain, what level of heterogeneity/standardization is acceptable? What k

should be considered as subjective and what objective?

The knowledge evolution perspective: first guideline

A Knowledge evolution perspective wants to inquire the dynamics between local and centralized knowledge

providing methods, and tools to support such process within (and not outside), technology

As an example, we envision a tool that suggests to people how their contexts should change in order to

converge towards shared schemas and vice versa

Semantic convergence

Semantic divergence

The Evolution of Knowledge

Ontology/Schema A Ontology/Schema B

Ontology/Schema A Ontology/Schema B

Coordination

Modified Ontology/Schema A

Modified Ontology/Schema B

Meaning Negotiation

SharedOntology/Schema ReificationKnowledge Creation Contextualization

Assumption 2: semantics as abstract

Which force drives knowledge evolution? The problem of finding relationships and potential overlappings between two knowledge

representations can be solved at the representation level The traditional question is: which is, in abstract, the best way to converge?

Travel info

Holydays Work

ItalyEurope

Travel info

Holydays Work

USAEurope

A B

Not compatible

Travel info

Holydays Work

ItalyEurope

B

USA

Travel info

Holydays Work

EuropeUSA

A

Italy

Option 1: Italy becomes part of

Europe

Option 2: Italy is added at the same

level of Europe

At an abstract level, Option 1 is more

correct than Option 2

Do you invest?

As the president of an airline company, you have invested 10 million dollars of the company's money into a research project. The purpose was to develop a plane that would not be detected by conventional radar, in other words, a radar-blank plane. When the project is 90% completed, another firm begins marketing a plane that cannot be detected by radar. Also, it is apparent that there is much faster and more economical than the plane your company is building. The question is: should you invest the last 10% of the research funds to finish your radar-blank plane?

(Arkes and Blumer 85)

Do you continue your investment?

Yes No

85% 15%

Interpretations are driven by interests

Travel info

Holydays Work

ItalyEurope

Do you agree with the proposed changes?

Will the others agree?

If you categorized 2 docs

Yes No Yes No

Yes No Yes No

If you categorized 10.000 docs

If they categorized 2 docs

If they categorized 10.000 docs

You’re the content manager of a travel company and you built the company taxonomy in order to collect travel info. You and your colleagues manually tagged a set of docs. Now somebody at the upper level wants to change it and asks for your opinion.

Interpretations are driven by interests and interests drive the evolution of knowledge representations

The pragmatic view on semantic heterogeneity

Semantic structures are not abstract representations of knowledge, but rather an expression of how people concretely work and invest onto a particular interpretation

Changes at the representation level, imply a series of costs generated by the consequent need to reconfigure a concrete world

convergence is a meaning negotiation process in which participants attempt to reach a representational configuration that must be as much as possible compatible with their social and economic interests

Travel info

Holydays Work

ItalyEurope

Documents have been categorized

Applications have been customized

Interests have been developed

Data have been structured

DB

In pragmatic terms, semantic change costs

If we look the problem from a pragmatic perspective, changes my generate different costs and, thus, impact on the interests of people and groups

For example, if we consider that re-categorizing documents has a cost, than speakers will choose the alternative that minimizes these costs

Travel info

Holydays Work

ItalyEurope

Travel info

Holydays Work

USAEurope

A B

Doesn’t contain Italy docs

Travel info

Holydays Work

ItalyEurope

B

USA

At concrete level, Option 2 is more convenient than

Option 1

Option 2: Italy is added at the same

level of Europe

Not compatible

Compatible

The knowledge evolution perspective: a second guideline

Knowledge evolution dynamics do not occur only at an abstract representational level, but are also heavily influenced by social and economic considerations

As an example, we envision a technology that is able to support semantic negotiation and convergence processes on the base of the impact on “interests”, and not only of

its consistency and validity

The knowledge evolution perspective: in sum

Which processes define knowledge evolution? Which social and economic dimensions affect these processes? How can concrete knowledge evolution dynamics be represented,

and supported within a technology enabled environment?

This perspective requires an interdisciplinary collaboration among Computer Science and…– Organization studies (game theory, theory of coalitions, market and

hierarchies…)– Sociology of Knowledge (knowledge and identity, conflict mediation,

ethnography of infrastructure)– Epistemology (science as evolution, interests and paradigms)

The knowledge evolution perspective: in sum

Centralized KM

Problem: syntactic heterogeneity

Solution: standardization

Distributed KM

Problem: semantic heterogeneity

Solution: interoperability

Evolutionary KM

Problem: pragmatic heterogeneity

Solution: negotiation

Object: content

Object: interests

Object: context

Technology: portals and ontologies

Technology: p2p and semantic matching

Technology: ?

Thanks

matteo.bonifacio@itc.it

For my home page digit in google: matteo bonifacio

http://edamok.itc.it