e-collaboration and knowledge sharing based on text notes

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e-Collaboration and Knowledge Sharing based on Text Notes EMBET System Institute of Informatics, SAS Michal Laclavik

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e-Collaboration and Knowledge Sharing based on Text Notes. EMBET System Institute of Informatics, SAS Michal Laclavik. Overview. Applications Motivation, history Experience Management Approach EMBET Architecture Ontology GUI Examples. Application of the system. - PowerPoint PPT Presentation

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Page 1: e-Collaboration and Knowledge Sharing based on Text Notes

e-Collaboration and Knowledge Sharing based on Text Notes

EMBET SystemInstitute of Informatics, SAS

Michal Laclavik

Page 2: e-Collaboration and Knowledge Sharing based on Text Notes

Overview

Applications Motivation, history Experience Management Approach EMBET Architecture Ontology GUI Examples

Page 3: e-Collaboration and Knowledge Sharing based on Text Notes

Application of the system

Collaboration among users Knowledge Sharing Recommendation

Representation of Experience or Knowledge Text Notes

Page 4: e-Collaboration and Knowledge Sharing based on Text Notes

Motivation, History

In Pellucid IST Project Active Hint approach AH = action on resource(s) in context + explanation Many AHs in Pellucid:

See Text Note in this context because is useful

Text Notes Natural way for people If we are able to detect context of note - note can help

others better then other formalized knowledge People like to enter notes or memos to remind something

to themselves or others

Page 5: e-Collaboration and Knowledge Sharing based on Text Notes

Experience Management Approach

Problem p Problem Space (P) In EMS Case-Lesson pairs (c, l)

Case Space (C) lesson space (L).

maps problem space to case space c = f(p)

Page 6: e-Collaboration and Knowledge Sharing based on Text Notes

General vs. EMBET Approach

General EM Approach Characterize a problem Transform the problem from the space P to the space C. Choose from the cases the most "useful" lesson from the case-lesson

pairs stored in the database Apply that lesson.

EMBET EM Approach User context detection from environment which describes problem P Our Model is described by ontology and Notes are stored with

associated context, which describes space C Notes represent learned lesson L which is associated with space C

(note context). The note context is matched with a user problem described by the detected user context. The user context is wider than the note context and as a result all applicable notes are matched and returned.

Applying the lesson is left to the user be reading appropriate notes.

Page 7: e-Collaboration and Knowledge Sharing based on Text Notes

User enter a Note

Pattern DetectionAnnotation of

Note with context

Compare current user context

and Note context

Displaying Context List

User approve and submit

Context of note

Notes are displayed to user in

detected user context

Vote on Note,updating Relevance

Knowledge Cycle

Page 8: e-Collaboration and Knowledge Sharing based on Text Notes

Architecture

XML messages

JSPPages

HTML Output

JSTL

XSLT Style Sheets CSS

UAA GUI

RDFOWL

XML messages

KnowledgeNotes

FeedbackOn

knowledge

UpdateKnowledge

ContextUpdate

UAA Core

KnowledgeNotes

Detection

ContextDetection

StoreKnowledge

GOM

Page 9: e-Collaboration and Knowledge Sharing based on Text Notes

Ontology

Page 10: e-Collaboration and Knowledge Sharing based on Text Notes

System GUI

Page 11: e-Collaboration and Knowledge Sharing based on Text Notes

Example Note:

the ALADIN model is not appropriate for weather forecast in late fall because it gives results which differ approx. 50% from reality

Dealing with a problem of Floods □

Running model for Meteorology □

Running Service type ALADIN ■

Running Service Instance for ALADIN located in Slovakia

In late fall (October – November) ■

Other (specify) □

Page 12: e-Collaboration and Knowledge Sharing based on Text Notes

Examples In High Tatra mountain 40% of snow can sublimate Precipitation measuring tools are not used during the winter,

consider this in models computing floods especially in spring because you will have not proper precipitation data

In area of East Slovakia most of the floods are caused by flash rains in spring

Intensity of flash rains can be discovered by vertical balloon measurement – quite expensive, find correct intensity of measurement when needed

context: location, time, model You are not able to predict weather for 1km2 because grid

size is 3x3km context: model, input data, output data

Page 13: e-Collaboration and Knowledge Sharing based on Text Notes

Examples

Water levels for Slovakia can be found at http://www.povodia.sk/ context: input data, location

Notes comparing modeled output data with historical real data or historical model output data context: model, output data, historical data, real data

Notes for performance prediction, monitoring or other – notification from K- Wf Grid system (WP2, WP3, KAA)

Page 14: e-Collaboration and Knowledge Sharing based on Text Notes

Thank you !

Institute of Informatics, SASMichal Laclavik