1. Centralized vs Distributed Knowledge Management: Is it the right question? Matteo Bonifacio Department of Informatics and Business Studies – University of Trento Automated Reasoning Division - ITC-IRST

2. 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

3. Centralized Knowledge Management Knowledge as content and the syntactic problem

4. 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)

5. 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 knowledge  KBs: in order to collect contents organized according to a corporate conceptual schema  EKP: in order to provide a single point of access for the members of different organizational units Enterprise knowledge portal KB Collaborative tools Contribution WfS

6. 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

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

8. 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 19951996199719981999200020012002 KM eBusiness ERP

9. 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.

10. Distributed Knowledge Management Knowledge as context and the semantic problem

11. 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

12. 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 SEALAKE UNRELATED By java I mean an island By lake she intends all By java they mean a language Intranet Site SOFTWARE LANGUAGES LISPJAVA IMAGES INDONEASIA SEA LAKE Personal internet site Suggeste KM experts Personal internet site Suggeste

13. 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

14. 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…

15. 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..

16. 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

17. 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

18. 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

19. 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 Autonomy Coordination Lotus Notes Team Room Intranet Site User DeskTop Lotus Notes DB Content Management tool User DeskTop

20. EDAMOK: DKM technological architecture iaia Context iaia iaia 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 Context Team CM tool Personal site Personal file system

21. A DKM Technology: the Knowledge Exchange System (KEx) A peer to peer system to support semantic interoperability across heterogeneous and cooperative knowledge sources

22. 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

23. 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

24. KEx Roles 1: Seeker, Provider, Broker Contextslexical index IC IC IC IC IC IC  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 PPPPPP Keyword1Doc1, doc5,… Keyword2Doc10, doc3,… Keyword3Doc1, docn,… Keyword4DocM, doc17,… Keyword5Doc2, doc6,…

25. 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 Group Office 3 Peer Group Query

26. 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) PP Outlook pst file File system Lotus Notes repository Other content repositories

27. Sharing documents Share these documents Keyword Indexing Service File system Association to concept Add to index Document path + Document name Semantic index Concept ID Security and Membership policies Decide sharing level

28. 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 PPP Advertise a user peer Discovery P1 Advertise a source peer P2 P Advertise a user peer P3

29. 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 PPP Discovery F1 Advertise a federation P F2

30. 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 PPP Keywords Query + Focus Select a target for the query Select a semantic focus Select keywords

31. 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) PP Lexical Indexing Service Semantic match Keyword match Keywords Result + Keywords +

32. 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…

33. Semantic Propagation (Brokering)  Semantic propagation: forward my query to semantically relevant peers PP Keywords Query + Focus Semantic match Bookmark + Result Forward to other relavant docs, peers, categories and federations Broker Seeker Provider P

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

35. And organizations as part of k networks Sector 2 Temporary consortium Peer Network Peer Group Sector 3 Sector 4 Sector 1

36. 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?

37. Evolutionary Knowledge Management Knowledge as process and the pragmatic problem

38. 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 domain e.g. Peer2Peer KM e.g. KM Portals

39. 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

40. 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

41. 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?

42. 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

43. 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 Reification Knowledge Creation Contextualization

44. 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 HolydaysWork Italy Europe Travel info HolydaysWork USA Europe A B Not compatible Travel info HolydaysWork Italy Europe B USA Travel info HolydaysWork Europe USA 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

45. 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? YesNo 85%15%

46. Interpretations are driven by interests Travel info HolydaysWork Italy Europe 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

47. 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 HolydaysWork Italy Europe Documents have been categorized Applications have been customized Interests have been developed Data have been structured DB

48. 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 HolydaysWork Italy Europe Travel info HolydaysWork USA Europe A B Doesn’t contain Italy docs Travel info HolydaysWork Italy Europe 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

49. 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

50. 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)

51. 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: ?

52. Thanks  matteo.bonifacio@itc.it matteo.bonifacio@itc.it  For my home page digit in google: matteo bonifacio  http://edamok.itc.it