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Structure of the Automatic Indexing Framework
The idea behind our framework is that learning object metadata can be derived from two different types of sources.
The first source is the learning object itself;
the second is the context in which the learning object is used.
Metadata derived from the object itself is obtained by content analysis, such as keyword extraction,
language classification and so on. The contexts typically are learning (content) management systems (like Blackboard)
or author institution information. A learning object context provides us with extra information about the learning object
that we can use to define the metadata.
The structure of our framework is inspired by this idea. It is summarized in the pictures below.
The framework consists of two major groups of classes that generate the metadata,
namely Object-based indexers and Context-based indexers.
The object-based indexers generate metadata based on the learning object itself, isolated from any other learning object
or learning management system. The second class of indexers uses a context to generate metadata.
By working this way, the framework is easily extensible for new (1) contexts and (2) learning object types.
To be complete, the framework also has some Extractors,
that for example extract the text from a Powerpoint-file, and a Metadatamerger that
can combine the results of the different indexers into one set of metadata.
 Figure: General structure of our Automatic Indexing Framework
 Figure: ObjectBasedIndexers: indexers that derive metadata from the learning object itself.
 Figure: ContextBasedIndexers: indexers that derive metadata from the context in which the learning object is used.
PS: The parts with a gray font are parts that are not implemented yet, but that seem interesting to do in the future.
Technically, the framework is implemented as a set of web services that generate metadata in
IEEE LOM format . We can also generate metadata according to the
Ariadne application profile.
The source code of the framework can be found on Sourceforge at
http://cvs.sourceforge.net/viewcvs.py/ariadnekps/AutoIndexing/.
Source code for a context indexer that mines data available in the Blackboard LMS is available at
http://cvs.sourceforge.net/viewcvs.py/ariadnekps/AutoIndexing/org/ariadne/autoindexing/indexers/contextBasedIndexers/ .
You can "play" with the indexing framework by uding the "try it" section that you'll find at the bottom of this website.
Information on how to install or use AMG, can be found on http://ariadne.cs.kuleuven.ac.be/amg/builds.php
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