am semantic eim scenario table of content

Artificial Memory Semantic Enterprise Innovation

Management Scenario

DERI GALWAYGalway, September 2004 Lars Ludwig, Xuan Zhou

Sept. 2004

[email protected] - [email protected]

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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

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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

Idea / Innovation Management

Working ResultDocumentation

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Maintaining Enterprise Adapting Enterprise

Business Process Management

Performance Management, Business Intelligence

Process / Workflow Management

Traditional Knowledge Management

Idea / Innovation Management

Working ResultDocumentation

Status QuoStructured and Unstructured Information in EIM

structured information

unstructured information

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Ontologizing EIMReplacing Specific Information and Communication Systems by Semantic Web Technology


Semantic (Enterprise Knowledge) Web

Innovation Management Ontology

Communication / Collaboration Ontology

(Personal) Knowledge Management Ontology

Strategy & Performance Management Ontology

Process Management Ontology

(Personal) Knowledge Domain Ontologies

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Semantic (Enterprise Knowledge) Web






(Personal) Knowledge Domain Ontologies

Ontologizing EIMSlow-changing and Fast-changing Schemes

slow-changing schemes

fast-changing schemes

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Ontologizing EIMCharacteristics of Slow-changing and Fast-changing Schemes

• 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

slow-changing schemes …

• 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

fast-changing schemes …

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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

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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

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Role of Artificial MemorySemantic Consolidation

A. 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

B. A central organization / instance publishes entities to personal or sub-organizational artificial memories.

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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

2. Individuals make semantic schema extensions without changing the actual schema (semantic schema mimicking)

3. Individuals evaluate & consolidate semantic extensions by semantic communication and collaboration

4. Extensions are consolidated into, between, and through groups or sub-organizational AMs

5. After thorough evaluation and consolidation, the central schema is being updated and schema changes redistributed

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EKM Scenario EKM enabled by Artificial Memory

1. A (sub-)central organization provides basic knowledge of general importance

2. Individuals acquire or are being provided with new knowledge make extensions to their AM Knowledge Base

3. Individuals evaluate & consolidate knowledge by semantic communication and collaboration

4. Relevant knowledge is consolidated into, between, and through groups or sub-organizational AMs

5. After thorough evaluation and consolidation, the central knowledge base is being updated and generally relevant new knowledge redistributed

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EKM Scenario Integrated EKM

A. It is unlikely that current information systems will be soon and totally replaced by Artificial Memories and Ontology-based technology respectively.

B. Hence it is necessary to integrate present information systems into the scenario / show-case.

C. To distribute and share information a top-down and a bottom-up strategy should be applied likewise.

D. Information in other systems must be made available in hyperlink-able form or as relatable RDF-files in order to avoid unnecessary information duplication.

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DWH

Intranet /Internet

DMS

Web Crawler

CRMERP

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EKM Scenario Feedback Loops in Integrated EKM

A. 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.

B. 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.

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Web Crawler

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Next StepsHow to proceed? Possible Research Topics and Papers!


Semantic Enterprise Knowledge Web






(Personal) Knowledge Domain Ontologies PKM and AM

Reverse Ont. Eng.

Sem. Collaboration

Semantic EKM / Web

Integrated SEKM

Semantic iTeams

Semantic (E)IM

Sem. Innovation PM

Semantic Perf.Mgmt.

Semantic BPM

Research Show-Case

HP Use-Case / Ph.D.

Artificial Memory

am semantic eim scenario table of content

Documents

enterprise memory

amsreference entities

copies of entities

different technology

aminterlink information

groups of entities instances

semantic schema extensions

communication systems