Offline strategies for online question answering: answering questions before they are asked
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Internet Learning and the Building of Knowledge
Internet Learning and the Building of Knowledge
Modeling commonality among related classes in relation extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Knowledge element extraction for knowledge-based learning resources organization
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
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Preorder relation between Knowledge Units (KU) is the precondition for navigation learning. Although possible solutions, existing link mining methods lack the ability of mining preorder relation between knowledge units which are linearly arranged in text. Through the analysis of sample data, we discovered and studied two characteristics of knowledge units: the locality of preorder relation and the distribution asymmetry of domain terms. Based on these two characteristics, a method is presented for mining preorder relation between knowledge units from text documents, which proceeds in three stages. Firstly, the associations between text documents are established according to the distribution asymmetry of domain terms. Secondly, candidate KU-pairs are generated according to the locality of preorder relation. Finally, the preorder relations between KU-pairs are identified by using classification methods. The experimental results show the method can efficiently extract the preorder relation, and reduce the computational complexity caused by the quadratic problem of link mining.