Partial Generation of Contextualized Metadata in a Collaborative Modeling Environment
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Aug 2004 Eindhoven, [email protected]
Partial Generation of Contextualized Metadata in a Collaborative Modeling EnvironmentNiels Pinkwart, Marc Jansen, Maria Oelinger, Lena Korchounova, Ulrich Hoppe
COLLIDE / IIIS
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Aug 2004 SW-EL '04 - Eindhoven, NL 2 of 17
Function of Metadata in General
• General – Indexing– Retrieving– Re-using– Pre-information
• Learning material– Network learning scenarios
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Problems with handling Metadata
• Critical problems concerning user given metadata: – Reliability of user provided data– Incompleteness– Inconsistency
• Requirements– Minimize users' tedious work – Ensure reliable data (how?)
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Problems with handling Metadata• Approach
– Automation of metadata generation– Use task context– Use tool information– Consider document histories
• Aims– Provide content based awareness
in a user community– Enhance semantic retrieval– Build and connect learning communities
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Exploiting Task Context in a Modeling Environment• COllaborative Open Learning and
MODEling System = Cool Modes
• Supports discussions and synchronous cooperative modeling in various domains
• All plugins synchronously usable, mixing and combining different representations
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Cool Modes
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Interfaces
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Tool-embedded Retrieval Strategies
• Associative lookup strategy– Generate a query from the
user's document
• One simple generalization step– Relevant slot (specified)– Free slot (requested)– Not relevant slot
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Aug 2004 SW-EL '04 - Eindhoven, NL 9 of 17
Tool-embedded Retrieval Strategies
• Associative lookup strategy– Generate a query from the
user's document
• One simple generalization step– Relevant slot (specified)– Free slot (requested)– Not relevant slot
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Task context from underlying ontology
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Metadata slots considered• Enrich standard conform
set of metadata, e.g. LOM
• Enrichment– Used tools– Context information
• Metadata types– Tool specific
or independent– Context specific
or independent
Date Activity,e.g. project
Plugin,e.g. UML
Contributors,e.g. Maria
ind. spec.
spec.
ind.
context
tool
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Associative Lookup Strategy
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• XML based queries against Web Service
• General query patterns– Specified attribute: slot with value used as filter– Requested attribute: slot without value– Not relevant slots do not appear at all
• Results– Refine the query by a found document– Usually no content transmission– Document download optional from web
interface or Cool Modes
Implementation
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<query_pattern>
<entity>
<slot name="plug-in">
<value>MazeDesign</value>
</slot>
<slot name="author" />
</entity>
</query_pattern>
XML representation
Retrieval Query and Answer
<query_result> <entity> <slot name="plug-in"> <value>MazeDesign</value> </slot> <slot name="author"> <value>Maria</value> </slot> </entity> <entity> <slot name="plug-in"> <value>MazeDesign</value> </slot> <slot name="author"> <value>Lena</value> </slot> </entity></query_result>
query
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<query_pattern>
<entity>
<slot name="plug-in">
<value>MazeDesign</value>
</slot>
<slot name="author" />
</entity>
</query_pattern>
XML representation<query_result> <entity> <slot name="plug-in"> <value>MazeDesign</value> </slot> <slot name="author"> <value>Maria</value> </slot> </entity> <entity> <slot name="plug-in"> <value>MazeDesign</value> </slot> <slot name="author"> <value>Lena</value> </slot> </entity></query_result>
Retrieval Query and Answer
query
input for new query
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Outlook
• Use of document flows– Document history– Social Navigation
• Query patterns– Searching for documents which are
similar to the one the user is currently working on
– Build macros from typical searches