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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 92-101    DOI: 10.11925/infotech.2096-3467.2018.0066
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Extracting Topics and Their Relationship from College Student Mentoring
Beibei Pang,Juanqiong Gou(),Wenxin Mu
School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
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Abstract  

[Objective] This paper proposes a framework for small-scale knowledge acquisition and modeling, aiming to more effectively manage the College Students’ deep mentoring work. [Methods] Firstly, we used the LDA to identify topics of collected documents, as well as the phrases describing the topics. Secondly, we used the concept hierarchy analysis to get the relations among these topics. Finally, we encoded ontology of the modeling results for knowledge retrieval. [Results] This study further refined the granularity of topic knowledge on the basis of LDA modeling, which reduced the difficulty of topic modeling and describe their relationship. [Limitations] We did not examine the expanded knowledge base generated by the new depth mentoring documents. [Conclusions] The proposed framework supports the modeling and retrieval of multi granularity knowledge from deep counseling, such as identifying problems, communication methods, and guiding skills.

Key wordsLDA      FCA      Knowledge Modeling      Ontology     
Received: 18 January 2018      Published: 11 July 2018

Cite this article:

Beibei Pang,Juanqiong Gou,Wenxin Mu. Extracting Topics and Their Relationship from College Student Mentoring. Data Analysis and Knowledge Discovery, 2018, 2(6): 92-101.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0066     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I6/92

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