Mining preorder relation between knowledge units from text.

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.