广西师范大学学报（自然科学版） ›› 2014, Vol. 32 ›› Issue (4): 45-51.

• • 上一篇    下一篇

基于数据脑本体的脑信息相关文档选取

钟寒1, 钟宁1,2,3, 陈建辉4, 韩健1   

  1. 1. 北京工业大学国际WIC研究院,北京100124;
    2. 磁共振成像脑信息学北京市重点实验室,北京100124;
    3. 日本前桥工科大学生命信息系,日本前桥371-0816;
    4. 清华大学计算机科学与技术系,北京100084
  • 收稿日期:2014-05-23 发布日期:2018-09-26
  • 通讯作者: 钟宁(1956-),男,北京人,北京工业大学教授、博导,日本前桥工科大学博导。E-mail:zhong@maebashi-it.ac.jp
  • 基金资助:
    国家自然科学基金资助项目(61272345);国家重点基础研究发展计划(973计划)资助项目(2014CB744605);国际科技合作资助项目(2013DFA32180)

Document Selection for the Data-Brain Ontology and Related Information

ZHONG Han1, ZHONG Ning1,2,3, CHEN Jian-hui4, HAN Jian1   

  1. 1.International WIC Institute, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China;
    3. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City 371-0816, Japan;
    4. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • Received:2014-05-23 Published:2018-09-26

PDF (PC)

157

摘要： 数据脑本体有重要意义,是脑信息加工处理的基础。传统的源文档选取研究只考虑概念的因素,不能满足系统化脑信息学研究的需要。因此,本文在数据脑本体的理论基础上,首先分析了数据脑本体所需的脑信息源知识具有的概念性、属性性、关系性的特点;然后,针对这些特点采用改进的VSM(vector space model)方法和特征相结合的方法计算文档权值;最后,通过使用与脑科学知识相关的真实文档进行实验,实验结果显示相关文档的概念、属性和关系权值以及每个文档的权值与专家判定结果基本一致,拟合相关系数达0.975 346。

关键词: 数据脑本体, 脑信息源, 文档选取

Abstract: The document selection related to brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, traditional research of document selection only focuses on the concept, and cannot meet the requirement of the systematic Brain Informatics study. This paper analyzes the characteristics of source knowledge firstly with concepts, attributes and relations. Then, the weight of documents by using the improved method of Vector Space Model is calculated. Finally, experimental results between the weight of each document and experts’ judgement are basically the same by using real documents associated with brain science. The correlation coefficient is 0.975 346.

Key words: data-brain ontology, brain informatics provenances, documents selection

中图分类号: 

  • TP308
[1] ZHONG Ning, BRADSHAW J M, LIU Ji-ming, et al. Brain Informatics[J]. IEEE Intelligent Systems, 2011, 26(5): 16-21.
[2] HOWE D, COSTANZO M, FEY P, et al. Big Data: the future of biocuration[J]. Nature, 2008, 455(7209): 47-50.
[3] ZHONG Ning, CHEN Jian-hui. Constructing a new-style conceptual model of brain data for systematic brain informatics[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(12): 2127-2142.
[4] LEE D L, CHUANG Huei, SEAMONS K. Document ranking and the vector-space model[J]. IEEE Software, 1997, 14(2): 67-75.
[5] EGGHE L, MICHEL C. Construction of weak and strong similarity measures for ordered sets of documents using fuzzy set techniques[J]. Inf Proc and Manage, 2003, 39(5): 771-807.
[6] DANILOWICZ C, BALINSKI J. Document ranking based upon Markov chains[J]. Inf Proc and Manage, 2001, 37(4): 623-637.
[7] 邢军, 韩敏, 周开朋.领域本体信息源选取方法研究与实现[J].大连理工大学学报, 2007, 47(4): 598-604.
[8] CHU Wei, KEERTHI S S.New approaches to support vector ordinal regression[C]// Proceedings of the 22nd International Conference on Machine Learning.New York: ACM Press, 2005: 145-152.
[9] USUNIER N, BUFFONI D, GALLINARI P. Ranking with ordered weighted pairwise classification[C]// Proceedings of the 26th International Conference on Machine Learning. New York: ACM Press, 2009:1057-1064.
[10] McFEE B, LANCKRIET G. Metric learning to rank[C]//Proceedings of the 27th International Conference on Machine Learning. New York: ACM Press, 2010: 775-782.
[11] CARUANA R, BALUJA S, MITCHELL T. Using the future to "sort out" the present: Rankprop and Multitask learning for medical risk evaluation[C]// Advances on Neural Information Processing System 8. Cambridge, MA:MIT Press, 1996:959-965.
[12] SHASHUA A, LEVIN A. Ranking with large margin principle: two approaches[C]// Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press, 2003:961-968.
[13] MATSUMURA A, TAKASU A, ADACHI J. Effect of relationships between words on Japanese information retrieval[J]. ACM Transactions on Asian Language Information Processing, 2006, 5(3): 264-289.
[14] ANH V N, de KRETSER O, MOFFAT A. Vector-space ranking with effective early termination[C]// Proceedings of the 24th Annual International ACM SIGIR Conference on Research and development in information retrieval. New York: ACM Press, 2001: 35-42.
[15] 朱礼军. 万维网环境下基于领域知识的信息资源管理模式研究[D]. 北京:中国农业大学,2004.
[16] ZHONG Han, CHEN Jian-hui, KOTAKE T, et al. Developing a Brain Informatics provenance model[C]// Brain and Health Informatics: LNCS Vol 8211. Berlin Springer, 2013:439-449.
[17] LIANG Pei-peng, ZHONG Ning, LU Sheng-fu, et al. ERP characteristics of sentential inductive reasoning in time and frequency domains[J]. Cognitive Systems Research, 2010, 11(1): 67-73.
[18] ZHONG Ning, LIANG Pei-peng, QIN Yu-lin, et al. Neural substrates of data-driven scientific discovery: an fMRI study during performance of number series completion task[J]. Science China Life Sciences, 2011, 54(5): 466-473.
[19] GÄRDIN A, BRISMAR T B, MOVIN T, et al. Dynamic contrast enhanced magnetic resonance imaging in chronic Achilles tendinosis[J]. BMC Medical Imaging, 2013,13:39.
[1] 钟寒,张鸿洲,尹德春,沈辉. 基于PageRank算法的团伙犯罪中嫌疑人排名[J]. 广西师范大学学报（自然科学版）, 2019, 37(3): 79-86.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 广西师范大学学报（自然科学版）编辑部
地址：广西桂林市三里店育才路15号　邮编：541004
电话：0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发