Mining learning-dependency between knowledge units from text

  • Authors:

  • Jun Liu;Lu Jiang;Zhaohui Wu;Qinghua Zheng;Yanan Qian

  • Affiliations:

  • Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049 and MOE KLINNS Lab and SKLMS Lab, Xi'an Jiaotong University, Xi'an, People's Repu ...;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049 and MOE KLINNS Lab and SKLMS Lab, Xi'an Jiaotong University, Xi'an, People's Repu ...;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049 and MOE KLINNS Lab and SKLMS Lab, Xi'an Jiaotong University, Xi'an, People's Repu ...;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049 and MOE KLINNS Lab and SKLMS Lab, Xi'an Jiaotong University, Xi'an, People's Repu ...;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049 and MOE KLINNS Lab and SKLMS Lab, Xi'an Jiaotong University, Xi'an, People's Repu ...

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2011

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Abstract

Identifying learning-dependency among the knowledge units (KU) is a preliminary requirement of navigation learning. Methods based on link mining lack the ability of discovering such dependencies among knowledge units that are arranged in a linear way in the text. In this paper, we propose a method of mining the learning- dependencies among the KU from text document. This method is based on two features that we found and studied from the KU and the learning-dependencies among them. They are the distributional asymmetry of the domain terms and the local nature of the learning-dependency, respectively. Our method consists of three stages, (1) Build document association relationship by calculating the distributional asymmetry of the domain terms. (2) Generate the candidate KU-pairs by measuring the locality of the dependencies. (3) Use classification algorithm to identify the learning-dependency between KU-pairs. Our experimental results show that our method extracts the learning-dependency efficiently and reduces the computational complexity.