Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
Managing Semantic Content for the Web
IEEE Internet Computing
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems
Ontological User Profiles for Representing Context in Web Search
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Metadata, semantics, and ontology: providing meaning to information resources
International Journal of Metadata, Semantics and Ontologies
Incorporating concept hierarchies into usage mining based recommendations
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Metadata domain-knowledge driven search engine in "HyperManyMedia" E-learning resources
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
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We present an approach for personalized search in an e-learning platform, that takes advantage of semantic Web standards (RDF and OWL) to represent the content and the user profiles. Personalizing the finding of needed information in an e-learning environment based on context requires intelligent methods for representing and matching the learning needs and the variety of learning contexts. Our framework consists of the following phases: (1) building the semantic e-learning domain using the known college and course information as concepts and sub-concepts in a lecture ontology, (2) generating the semantic learner's profile as an ontology from navigation logs that record which lectures have been accessed, (3) clustering the documents to discover more refined sub-concepts (top terms in each cluster) than provided by the available college and course taxonomy, (4) re-ranking the learner's search results based on the matching concepts in the learning content and the user profile, and (5) providing the learner with semantic recommendations during the search process, in the form of terms from the closest matching clusters of their profile. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learner's context can be effectively used for improving the precision and recall in e-learning search, particularly by re-ranking the search results based on the learner's past activities.