[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.
庞贝贝, 苟娟琼, 穆文歆. 面向高校学生深度辅导领域的主题建模和主题上下位关系识别研究*[J]. 数据分析与知识发现, 2018, 2(6): 92-101.
Pang Beibei,Gou Juanqiong,Mu Wenxin. Extracting Topics and Their Relationship from College Student Mentoring. Data Analysis and Knowledge Discovery, 2018, 2(6): 92-101.
(Liu Yunfeng, Qi Huan, Hu Xiang’en, et al.Multi-hierarchy Documents Clustering Based on LSA Space Dimensionality Character[J]. Journal of Tsinghua University: Science & Technology, 2005, 45(S1): 1783-1786.)
doi: 10.3321/j.issn:1000-0054.2005.09.013
[2]
Lu Y, Mei Q, Zhai C.Investigating Task Performance of Probabilistic Topic Models: An Empirical Study of PLSA and LDA[J]. Information Retrieval, 2011, 14(2): 178-203.
doi: 10.1007/s10791-010-9141-9
[3]
Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022.
(Yang Haixia, Gao Baojun, Sun Hanlin.Extracting Topics of Computer Science Literature with LDA Model[J]. New Technology of Library and Information Service, 2016(11): 20-26.)
(Hu Jiming, Chen Guo.Mining and Evolution of Content Topics Based on Dynamic LDA[J]. Library and Information Service, 2014, 58(2): 138-142.)
doi: 10.13266/j.issn.0252-3116.2014.02.023
(Xu Yuemei, Li Yang, Liang Ye, et al.Analyzing Evolution of News Topics with Manifold Learning[J]. New Technology of Library and Information Service, 2016(10): 59-69.)
(Feng Jia, Zhang Yunqiu.Research on the Method of Detecting and Analyzing Scientific Fronts Based on LDA and Ontology[J]. Information Studies: Theory & Application, 2017, 40(8): 49-54.)
[8]
Rocca P D, Senatore S, Loia V.A Semantic-grained Perspective of Latent Knowledge Modeling[J]. Information Fusion, 2016, 36: 52-67.
doi: 10.1016/j.inffus.2016.11.003
(Wang Hong, Zhang Hao, Shi Jinchuan. Research on Domain Ontology Concept Acquisition Method Based on LDA and Application[J/OL]. Computer Engineering and Applications. [2017-07-21].
(Wang Hao, Zhu Hui, Deng Sanhong.Study on Construction of Hierarchy Relationship of Subject Terms Based on Formal Concept Analysis[J]. Journal of the China Society for Scientific and Technical Information, 2015, 34(6): 616-627.)
doi: 10.3772/j.issn.1000-0135.2015.006.007
[12]
Bloehdorn S, Cimiano P, Hotho A.Learning Ontologies to Improve Text Clustering and Classification[C]// Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e. V. University of Magdeburg. 2006: 334-341.
(Wang Jun, Wang Shitong, Deng Zhaohong.Survey on Challenges in Clustering Analysis Research[J]. Control and Decision, 2012, 27(3): 321-328.)
[14]
Hwang S H, Kim H G, Yang H S.A FCA-Based Ontology Construction for the Design of Class Hierarchy[C] //Proceedings of International Conference on Computational Science and Its Applications, Singapore. 2005: 827-835.
[15]
Ponzetto S P, Strube M.Deriving a Large Scale Taxonomy from Wikipedia[C]// Proceedings of the 22nd National Conference on Artificial Intelligence. AAAI Press, 2007: 1440-1445.
(Huang Chenghui, Yin Jian, Hou Fang.A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method[J]. Chinese Journal of Computers, 2011, 34(5): 856-864.)
doi: 10.3724/SP.J.1016.2011.00856
(Guan Peng, Wang Yuefen.Identifying Optimal Topic Numbers from Sci-Tech Information with LDA Model[J]. New Technology of Library and Information Service, 2016(9): 42-50.)
(Teng Guangqing, Bi Qiang.Comparative Study on ConExp and LatticeMiner[J]. New Technology of Library and Information Service, 2010(10): 17-22.)
[19]
Antoniou G, Harmelen F V.Web Ontology Language: OWL[A]// Handbook on Ontologies[M]. Springer Berlin Heidelberg, 2009: 67-92.
[20]
Rani M, Dhar A K, Vyas O P.Semi-Automatic Terminology Ontology Learning Based on Topic Modeling[J]. Engineering Applications of Artificial Intelligence, 2017, 63: 108-125.
doi: 10.1016/j.engappai.2017.05.006
