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Peer–Mediated Distributed Knowledge Management M. Bonifacio, P. Bouquet, G. Mameli, M. Nori AMKM-2003 Stanford University March 24-26.

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Presentation on theme: "Peer–Mediated Distributed Knowledge Management M. Bonifacio, P. Bouquet, G. Mameli, M. Nori AMKM-2003 Stanford University March 24-26."— Presentation transcript:

1 Peer–Mediated Distributed Knowledge Management M. Bonifacio, P. Bouquet, G. Mameli, M. Nori AMKM-2003 Stanford University March 24-26

2 Traditional KM architectures: knowledge as content In the last 10 years, companies have invested huge amounts of money in order to manage knowledge adopting technological “carriers” (such as corporate knowledge portals or content management platforms). 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 Assumption: Knowledge as content that can be centralized, standardized and controlled

3 Some problems  KM systems don’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…?) KM Has Greatly Underperformed the Tech Sector

4 Our idea: Knowledge as Context  Besides knowledge viewed as content, there are other forms of knowledge which are to be considered: –Interpretative Context: people know how to interpret what happens and generate a language to talk about things and events. Context is a mean to interpret content –Relational Context: people know who knows what and reduce complexity through trust and identity. Context as a mean to refer to other people. ( People don’t believe in the paradigm of ideological sharing (all with all). They develop and use technologies if enable the sharing of knowledge within groups that evolve dynamically. )  Value emerges when content is positioned within its context: conceptual schemas, web of relations, business processes… Knowledge as content Context Content Local “Knowledges” Global Knowledge Interpret content Interpret other contexts Address to trusted experts

5 Complex organizations as made up of Knowledge Nodes Knowledge Nodes are social entities that “own” a local knowledge in terms of a content that has meaning within a context  Individuals  Communities  Teams A community internal web site A lotus notes team room Content An individual’s file system directory or outlook folders Context Local Knowledge Marketing Communiy R&D Communiy Sales Force Communiy Sales Project Team 1 Knowledge Network Communiy

6 The inconsintency of the technological architectures in current KM systems  From a technological point of view, current KM technologies are inconsistent with the very nature of knowledge and its social architecture  failure Portal KB Social architecture of knowledge Technological architecture of knowledge Social and technological architecture of knowledge AUTONOMY COORDINATION

7 From organization to the technological architecture: Actors and Roles in an Information Retrieval applications  Organizational Actors –Individual  K-Peer of a P2P network –Group  K-Federation  Roles –Knowledge Seeker  seeker module –Knowledge Provider  provider/federation module –Broker (suggesting potential providers to seekers)  brokering module Sales Force R&D Marketing Sales Project Team 1 Knowledge Network Community K-Federation K-Peer New Tool? I know! we know! she knows!

8  Create and manage a knowledge representation –Context Markup Language –Context Editor –Context extraction tools  Declare its existence in the network –Advertisement  Discover other available / active peers on the network –Discovery module –Ping module  Ask, receive and provide information in and from them –Knowledge Exchange module  Discover, create and join to federation of peers –Advertisement –Discovery module –Membership module Autonomy From organization to the technological architecture: Knowledge Processes CONTEXT Vacation SeaLakeSeaMountains PugliaSpainUSA Coordination Sales Force R&D Marketing Sales Project Team 1 Peer Network Community Communiy Coordination

9 KEx (Knowledge Exchange): User interface for Seeker

10 Semantic search Matching Service Provider Seeker Document descriptors User GUI Perform a semantic match Query (Focus Source) Document descriptors Focus Target Document descriptors Search documents related to a focus Mail id Document nameDocument path Document nameDocument path Document nameDocument path Document nameDocument path Mail subjectMail id Mail subject Mail id Document descriptors Search (Focus Source) P. Bouquet, A. Donà, L. Serafini, ConTeXtualized Local Ontology Specification via CTXML Proceedings of the AAAI Workshop on Meaning Negotiation, Edmonton (Alberta, Canada), July 28, 2002 Context management tool

11 Semantic Matching

12 Keyword based search Indexing Service Provider Seeker Search (Keywords) Document descriptors pst txt ppt html doc pdf xls ps Document repository User GUI Search documents that contain keywords Query (keywords) Document descriptors Keywords Document descriptors Search documents that contain keywords Mail id Document nameDocument path Document nameDocument path Document nameDocument path Document nameDocument path Mail subjectMail id Mail subject Mail id Document descriptors

13 KEx: Knowledge Space

14 Provide documents Share these documents Context management tool Outlook pst file File system Lotus Notes repository Other content repositories Document descriptor Green Table Concept ID Provider Incoming query Indexing Service Indexing Service Indexing Service Indexing Service Keyword matching Semantic matching Association to concept Add to indexes + Keywords

15 Conclusions and future works  We have developed a P2P application (based on JXTA protocols) for DKM that provides functionalities such as: –Seeker –Provider –Broker –Federations management  To be developed: –Community Management: peers can suggest to users to which federations they should join since semantically similar to them (bottom up community formation) –Push information services: peers can advertise services to other peers on the base of semantically relevant interests (targeted advertisement) –Recommendation: peers can listen to other users or other peers requests and suggest where or what to look for (help new comers) –Social Network Monitoring: Knowledge Managers can monitor the formation and evolution of trust networks and corporate languages

16 Thank you Gianluca Mameli: Project:


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