1. Constructing Natural Knowledge Ontologies to Implement Semantic Organisational Memory Dr. Laura Campoy-Gómez Information Systems Institute /IRIS University of Salford (UK) NLDB04 Conference, Salford, 25 June 2004

2. 2 The scope of the paper Knowledge management – the KM process cycle Knowledge management – the KM process cycle  Global process for capturing, acumulating, using and disseminating knowledge inside organisations Organisational Memory – the product Organisational Memory – the product Knowledge technologies – components for IS-based OM Knowledge technologies – components for IS-based OM  ontologies Collaborative and distributed knowledge construction through (ontological) integration – The OntoInt approach Collaborative and distributed knowledge construction through (ontological) integration – The OntoInt approach Towards the extension of the system Towards the extension of the system

3. 3 The OntoInt approach Formalisation of the environment Formalisation of the environment  Knowledge resides in ontologies – through time  Distributed ontology-based OM:  A global (integration-derived) ontology – the OM vision  Distributed user-owned ontologies incorporating newly constructed knowledge – to be added (shared)

4. 4 Initial requirements of the approach Distributed knowledge content =>INTEGRATION =>INTEGRATIONGLOBALCOOPERATIVEPERSONALISEDCONSISTENTAUTOMATIC => A CM model: a global integration-derived ontology

5. 5 Our model: technological- conceptual foundations Based on ontologies Based on ontologies  Space of concepts, atributes and relations  Taxonomic inheritanceinheritance  Mereological Creating the space by addition Creating the space by addition  Adding links - Parent/child//taxo./mereo.  Adding attributes

6. 6 Modus operandi of the system Distributed knowledge Distributed knowledge Client/server Client/server Integration + personalisation Integration + personalisation automatic automatic

7. 7 Modus operandi of the system

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10. 10 Modus operandi of the system

11. 11 Modus operandi of the system

12. 12 Our model : essential characteristics (4 key ideas) Distinction of a typology of ontologies Distinction of a typology of ontologies  functions and properties Sets of conceptsSets of concepts Sets of atributes (specific, inherited)Sets of atributes (specific, inherited) Distinction between integrable / non-integrable knowledge Distinction between integrable / non-integrable knowledge Management of synonym concepts (personalised terminology) Management of synonym concepts (personalised terminology) Principles (decision making) Principles (decision making)

13. 13 Our model : the integration mechanism Goal: contrastive ontological analysis Goal: contrastive ontological analysis Selection criteria: “more knowledge” Selection criteria: “more knowledge” Strategy: Strategy:  Concept by concept, level by level  Observance of consistency  conceptual definition (atributes)  organisational structure (taxonomic, mereological)  ‘Compatible’ ontologies

14. 14 Some current limitations - Towards an extension of the environment User corrections are not permitted nor contemplated during process – private knowledge  further consideration User corrections are not permitted nor contemplated during process – private knowledge  further consideration Need for consensus and automatism lead to too restrictive definition of consistency  further work: reformulation of inconsistency Need for consensus and automatism lead to too restrictive definition of consistency  further work: reformulation of inconsistency Language representation is limited  axioms needed – other than structural Language representation is limited  axioms needed – other than structural Substantial further work necessary to meet current needs  move to other languages – semantic web stardards Substantial further work necessary to meet current needs  move to other languages – semantic web stardards