AM Semantic EIM Scenario Table of Content

1. Artificial Memory Semantic Enterprise Innovation Management ScenarioDERI GALWAYGalway, September 2004 Lars Ludwig, Xuan Zhou

2. AM Semantic EIM Scenario Table of Content • Status quo of Enterprise Information Management • Ontologizing Enterprise Information Management • The role of slow- and fast-changing ontology schemes • Personal Knowledge Management • Semantic communication & collaboration • Semantic consolidation and reverse ontology engineering • Enterprise Knowledge Management • Integrated Enterprise Knowledge Management • Steps towards an AM Semantic EIM Scenario

3. Status QuoEnterprise Information Management and Innovation Management Today Enterprise Information Management Maintaining Enterprise Adapting Enterprise Business Process Management Performance Management, Business Intelligence Process / Workflow Management Traditional Knowledge Management Working Result Documentation Idea / Innovation Management

4. Enterprise Information Management Maintaining Enterprise Adapting Enterprise Business Process Management Performance Management, Business Intelligence Process / Workflow Management Traditional Knowledge Management Working Result Documentation Idea / Innovation Management Status QuoStructured and Unstructured Information in EIM structured information unstructured information

5. (Personal) Knowledge Domain Ontologies Ontologizing EIMReplacing Specific Information and Communication Systems by Semantic Web Technology Enterprise Information Management Semantic (Enterprise Knowledge) Web Process Management Ontology Strategy & Performance Management Ontology Innovation Management Ontology Communication / Collaboration Ontology (Personal) Knowledge Management Ontology

6. Enterprise Information Management Semantic (Enterprise Knowledge) Web Process Management Ontology Strategy & Performance Management Ontology Innovation Management Ontology Communication / Collaboration Ontology (Personal) Knowledge Management Ontology (Personal) Knowledge Domain Ontologies Ontologizing EIMSlow-changing and Fast-changing Schemes slow-changing schemes fast-changing schemes

7. slow-changingschemes … • describe how we (want to) acquire, organize, communicate, exchange, and evaluate information • make information pieces to objects of our acting • can be centrally modelled by classical ontological engineering in a top-down approach due to their stability • can be centrally mapped to each other due to their stability fast-changingschemes … • describe the information itself • cannot be centrally modeled due to often quick changes in individual knowledge • cannot be mapped centrally alone - due to individual schema extensions Ontologizing EIMCharacteristics of Slow-changing and Fast-changing Schemes

8. AM Role of Artificial MemoryPersonal Knowledge Management • AM replaces documents in Knowledge Management thereby providing a single system to manage structured and unstructured information (structured unstructured information) • The authoring process of information is simplified – no additional annotation process is necessary • Documents become views on instance entities • The de-normalization of data (duplication in database) can be prevented

9. AM Role of Artificial MemorySemantic Communication / Collaboration Different persons use their Artificial Memory to … • Synchronously and asynchronously send & receive entities or copies of entities and groups of entities (instances, concepts, relations, entitiy references) between each other; (manually or automatically) integrate received entities into own AM • Interlink information bases to allow for seamless inter-AM-browsing • Search in several AMs • Reference entities in other AMs • Collaborative editing, co-browsing • Publish & subscribe entities; notification of new entities, • Export (in different formats) and then send / publish (using different technology) entities; subscribe & import from different channels AM AM AM

10. AM AM Role of Artificial MemorySemantic Consolidation • Different persons use their Artificial Memory to consolidate their knowledge in a group, departmental or enterprise memory using the same mechanisms as used for semantic communication and semantic collaboration • A central organization / instance publishes entities to personal or sub-organizational artificial memories. AM AM AM AM AM AM

11. AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM EKM ScenarioReverse / Bottom-up Ontology Engineering for Fast-changing Schemes 1. A central organization enables AM with basic slow-changing and basic fast-changing schemes • Individuals make semantic schema extensions without changing the actual schema (semantic schema mimicking) • Individuals evaluate & consolidate semantic extensions by semantic communication and collaboration • Extensions are consolidated into, between, and through groups or sub-organizational AMs • After thorough evaluation and consolidation, the central schema is being updated and schema changes redistributed

12. AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM AM EKM Scenario EKM enabled by Artificial Memory 1. A (sub-)central organization provides basic knowledge of general importance • Individuals acquire or are being provided with new knowledge make extensions to their AM Knowledge Base • Individuals evaluate & consolidate knowledge by semantic communication and collaboration • Relevant knowledge is consolidated into, between, and through groups or sub-organizational AMs • After thorough evaluation and consolidation, the central knowledge base is being updated and generally relevant new knowledge redistributed

13. AM AM AM AM EKM Scenario Integrated EKM • It is unlikely that current information systems will be soon and totally replaced by Artificial Memories and Ontology-based technology respectively. • Hence it is necessary to integrate present information systems into the scenario / show-case. • To distribute and share information a top-down and a bottom-up strategy should be applied likewise. • Information in other systems must be made available in hyperlink-able form or as relatable RDF-files in order to avoid unnecessary information duplication. Web Crawler Intranet / Internet DMS DWH ERP CRM

14. EKM Scenario Feedback Loops in Integrated EKM • When new information flows into the EKM system, it is important to filter this information. Information has to be evaluated. The information should be aligned to the given knowledge structure. Hence automated information extraction should be ontology-driven. A notification system then can introduce new information available. • The use of a feedback / rating ontology should provide semantics to steer the filtering of information in semantic communication and consolidation. In turn, the feedback might improve the extraction that thereby would be driven by both domain and feedback ontology. Web Crawler AM AM AM AM

15. Enterprise Information Management Sem. Innovation PM Semantic Enterprise Knowledge Web Process Management Ontology Semantic iTeams Strategy & Performance Management Ontology Innovation Management Ontology Communication / Collaboration Ontology (Personal) Knowledge Management Ontology Reverse Ont. Eng. (Personal) Knowledge Domain Ontologies Artificial Memory Next StepsHow to proceed? Possible Research Topics and Papers! Semantic BPM Semantic Perf.Mgmt. ResearchShow-Case HP Use-Case / Ph.D. Semantic (E)IM Integrated SEKM Semantic EKM / Web Sem. Collaboration PKM and AM