Relevant learning objects extraction based on semantic annotation of documents
Article No.: 47, Pages 1 - 11
Abstract
Users look frequently for learning information from the web or from databases to include them to their resources, or to use them in a learning process. In order to satisfy these user's needs, we proposed here a model which aims at automatically annotating texts with semantic metadata: learning category of textual segments. These metadata would allow us to search and extract learning information from texts indexed in that way. This model is build up from two parts: the first part consists on a semantic annotation of learning objects according to their semantic categories (definition, example, exercise, etc.). The second part uses automatic semantic annotation which is generated by the first part to create a semantic inverted index which is able to find relevant learning objects for queries associated with semantic categories. To sort the results according to their relevance, we apply the Rocchio's classification technique on the learning objects. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness.
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Index Terms
- Relevant learning objects extraction based on semantic annotation of documents
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Published In
June 2012
571 pages
ISBN:9781450309158
DOI:10.1145/2254129
- Conference Chair:
- Dumitru Dan Burdescu,
- Program Chairs:
- Rajendra Akerkar,
- Costin Bădică
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- UCV: University of Craiova
- WNRI: Western Norway Research Institute
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 June 2012
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WIMS '12
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- UCV
- WNRI
WIMS '12: 2nd International Conference on Web Intelligence, Mining and Semantics
June 13 - 15, 2012
Craiova, Romania
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