SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Adaptive educational hypermedia on the web
Communications of the ACM - The Adaptive Web
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Re-using web information for building flexible domain knowledge
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Hi-index | 0.00 |
In this paper, we present an attempt to support education on the Web. We take advantage of information that can be taken directly during a learning session in order to update domain knowledge. A new technique to structure domain knowledge is presented. We represent domain knowledge as a hierarchy of concepts. Each concept consists of some dominant meanings, and each of those is linked with some chunks (segments of information) to define it. Based on this structure, we define a context-based information agent that can monitor conversations among a community of on-line learners, interpret the learners' inputs, and then assess the current context of the session. It is able to build a new query to get updated information from the Web. Then, it can filter the results, organizing, and presenting information useful to the learners in their current activities. We claim that specifying the context of a search better can significantly improve search results. An important task, therefore, is to assess the context based on dominant meaning space. That is a new set based measure to evaluate the closeness between queries and documents. Our experiments show that the proposed method greatly improves retrieval effectiveness, in terms of average overall accuracy.