面向知识发现的模糊本体融合与推理模型研究

doi:10.3772/j.issn.1000-0135.2021.04.001

情报学报

2021, Vol. 40

Issue (4): 333-344 DOI: 10.3772/j.issn.1000-0135.2021.04.001

情报理论与应用

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面向知识发现的模糊本体融合与推理模型研究

陆泉^1,3, 刘婷¹, 张良韬¹, 陈静²

1.武汉大学信息资源研究中心，武汉 430072
2.华中师范大学信息管理学院，武汉 430079
3.武汉大学大数据研究院，武汉 430072

Study on Knowledge Discovery Model Based on Fuzzy Ontology Fusion and Reasoning

Lu Quan^1,3, Liu Ting¹, Zhang Liangtao¹, Chen Jing²

1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Central China Normal University, Wuhan 430079
3.Big Data Institute, Wuhan University, Wuhan 430072

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摘要现有知识发现研究难以兼顾不同领域知识的精准性与模糊性，也缺乏描述和定义模糊本体的语言工具，本文从知识模糊性角度出发，提出一种基于OWL（ontology web language）语言的模糊本体表现模型，通过SWRL（semantic web rule language）语言表示精确规则和模糊规则，并结合概念对和隶属度将模糊知识转换成精确知识实现本体融合推理，构建面向知识发现的模糊本体融合和推理模型。选取药物相互作用这一典型领域的Drugs与Drugbank数据库中肿瘤及精神卫生疾病相关的药物数据对模型进行验证，研究结果表明，可在保持准确率水平的情况下，将对药物相互作用知识发现尤为重要的召回率显著提高至89.94%。本文提出的模糊本体模型可以同时描述精确知识和模糊知识，简化了对模糊知识的表示和处理。

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	陆泉
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	张良韬
	陈静

关键词 ：模糊本体, 知识融合, 知识推理, 知识发现, 药物相互作用

收稿日期: 2019-10-08

基金资助:国家社会科学基金重点项目“心理账户理论视角下在线健康社区精准信息服务研究”（20ATQ008）；中央高校基本科研业务费专项资金资助：武汉大学自主科研项目（人文社会科学）。

作者简介: 陆泉，男，1975年生，博士，教授，博士生导师，主要研究领域为数据挖掘、知识组织与知识服务研究，E-mail：mrluquan@whu.edu.cn；刘婷，女，1993年生，博士，主要研究领域为数据挖掘与知识组织研究；张良韬，男，1995年生，硕士，主要研究领域为数据分析与数据挖掘研究；陈静，女，1977年生，博士，教授，博士生导师，主要研究领域为信息组织与信息检索研；

引用本文:

陆泉, 刘婷, 张良韬, 陈静. 面向知识发现的模糊本体融合与推理模型研究[J]. 情报学报, 2021, 40(4): 333-344.
Lu Quan, Liu Ting, Zhang Liangtao, Chen Jing. Study on Knowledge Discovery Model Based on Fuzzy Ontology Fusion and Reasoning. 情报学报, 2021, 40(4): 333-344.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2021.04.001 或 https://qbxb.istic.ac.cn/CN/Y2021/V40/I4/333

1 李国杰, 程学旗. 大数据研究: 未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J]. 中国科学院院刊, 2012, 27(6): 647-657.
2 MacKinnon M J, Glick N. Applications: data mining and knowledge discovery in databases-an overview[J]. Australian & New Zealand Journal of Statistics, 1999, 41(3): 255-275.
3 Straccia U. Reasoning within fuzzy description logics[J]. Journal of Artificial Intelligence Research, 2001, 14: 137-166.
4 唐新香, 缪淮扣. 基于MDA的模糊本体生成方法[J]. 应用科学学报, 2007, 25(5): 541-543.
5 Matsuki E, Tsukada Y, Nakaya A, et al. Successful treatment of adult onset Langerhans cell histiocytosis with multi-drug combination therapy[J]. Internal Medicine, 2011, 50(8): 909-914.
6 Nestorov I. Whole body pharmacokinetic models[J]. Clinical Pharmacokinetics, 2003, 42(10): 883-908.
7 Percha B, Altman R B. Informatics confronts drug-drug interactions[J]. Trends in Pharmacological Sciences, 2013, 34(3): 178-184.
8 王国胤, 张清华, 马希骜, 等. 知识不确定性问题的粒计算模型[J]. 软件学报, 2011, 22(4): 676-694.
9 Zadeh L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
10 Kahraman C, ?ztay?i B, ?evik Onar S. A comprehensive literature review of 50 years of fuzzy set theory[J]. International Journal of Computational Intelligence Systems, 2016, 9(sup1): 3-24.
11 Wu Y N, Xu C B, Zhang T. Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: a case in China[J]. Energy, 2018, 147: 1227-1239.
12 Gul M, Celik E, Gumus A T, et al. A fuzzy logic based PROMETHEE method for material selection problems[J]. Beni-Suef University Journal of Basic and Applied Sciences, 2018, 7(1): 68-79.
13 Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, et al. Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment[J]. Artificial Intelligence in Medicine, 2018, 87: 20-33.
14 徐赐军, 李爱平, 刘雪梅. 基于本体的知识融合框架[J]. 计算机辅助设计与图形学学报, 2010, 22(7): 1230-1236.
15 Zhao X C, Luo Q S, Han B L. Survey on robot multi-sensor information fusion technology[C]// Proceedings of the 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008: 5019-5023.
16 潘泉, 于昕, 程咏梅, 等. 信息融合理论的基本方法与进展[J]. 自动化学报, 2003, 29(4): 599-615.
17 顾慧翔, 俞勇. 基于领域本体和知识推理的语义互联网应用[J]. 上海交通大学学报, 2004, 38(4): 583-585.
18 Jiang S P, Lowd D, Dou D J. Learning to refine an automatically extracted knowledge base using Markov logic[C]// Proceedings of the IEEE 12th International Conference on Data Mining. IEEE, 2012: 912-917.
19 Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion[C]// Proceedings of Advances in Neural Information Processing Systems. New York: Curran Associates Inc., 2013: 926-934.
20 陆凌云, 李伟, 杨明, 等. 基于混合推理的仿真实验设计方法智能选择[J]. 自动化学报, 2019, 45(6): 1055-1064.
21 Cummins M R. Nonhypothesis-driven research: data mining and knowledge discovery[M]// Clinical Research Informatics. Cham: Springer, 2019: 341-356.
22 Bergen K J, Johnson P A, de Hoop M V, et al. Machine learning for data-driven discovery in solid earth geoscience[J]. Science, 2019, 363(6433): eaau0323.
23 翟社平, 马传宾, 李威, 等. 基于SWRL规则推理的知识发现研究[J]. 信息技术, 2016, 40(2): 76-79.
24 Liu X D, Jia W J, Wang Y G, et al. Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8(5): e1268.
25 Chelly Dagdia Z, Zarges C, Schannes B, et al. Rough set theory as a data mining technique: a case study in epidemiology and cancer incidence prediction[C]// Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer, 2018: 440-455.
26 Abdelhamid N, Ayesh A, Thabtah F, et al. MAC: a multiclass associative classification algorithm[J]. Journal of Information and Knowledge Management, 2012, 11(2): 1250011.
27 Czibula G, Marian Z, Czibula I G. Software defect prediction using relational association rule mining[J]. Information Sciences, 2014, 264: 260-278.
28 Fisch D, Kalkowski E, Sick B. Knowledge fusion for probabilistic generative classifiers with data mining applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3): 652-666.
29 李海燕. 语义网粗糙本体支持的知识推理研究[D]. 大连: 大连海事大学, 2012.
30 Yang B R, Zhang T H, Song W, et al. Coordinators based on cognitive psychology features and the corresponding KDD process model[J]. Journal of University of Science and Technology of China, 2007, 37(2): 212-216.
31 Cerrito P B. Introduction to data mining using SAS Enterprise Miner[M]. SAS Institute, 2006.
32 Bharadwaj K K, Saroj. A parallel genetic programming based intelligent miner for discovery of censored production rules with fuzzy hierarchy[J]. Expert Systems with Applications, 2010, 37(6): 4601-4610.
33 Pears R, Kutty S. FGC: an efficient constraint based frequent set miner[C]// Proceedings of the 2007 IEEE/ACS International Conference on Computer Systems and Applications. IEEE, 2007: 424-431.
34 Al Ghoson A M. Decision tree induction & clustering techniques in SAS enterprise miner, SPSS clementine, and IBM intelligent miner a comparative analysis[J]. International Journal of Management & Information Systems, 2011, 14(3): 57-70.
35 Perez-Rey D, Anguita A, Crespo J. OntoDataClean: ontology-based integration and preprocessing of distributed data[C]// International Symposium on Biological and Medical Data Analysis. Heidelberg: Springer, 2006: 262-272.
36 Gruber T R. Toward principles for the design of ontologies used for knowledge sharing?[J]. International Journal of Human-Computer Studies, 1995, 43(5/6): 907-928.
37 Bobillo F, Straccia U. Fuzzy ontology representation using OWL 2[J]. International Journal of Approximate Reasoning, 2011, 52(7): 1073-1094.
38 张艳涛, 陈俊杰, 相洁. 基于SWRL本体推理研究[J]. 微计算机信息, 2010, 26(9): 182-183,132.
39 Pan J Z, Stoilos G, Stamou G, et al. f-SWRL: a fuzzy extension of SWRL[C]// Journal on Data Semantics VI, 2006, 4090: 28-46.
40 Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394-424.
41 国家肿瘤质控中心. 2019年全国癌症报告[EB/OL]. [2020-04-16]. http://www.china-rt.cn/special/846.html.
42 Mehnert A, Br?hler E, Faller H, et al. Four-week prevalence of mental disorders in patients with cancer across major tumor entities[J]. Journal of Clinical Oncology, 2014, 32(31): 3540-3546.
43 Drugs.com[EB/OL]. [2020-03-09]. https://www.drugs.com/.
44 Wishart D S, Feunang Y D, Guo A C, et al. DrugBank 5.0: a major update to the DrugBank database for 2018[J]. Nucleic Acids Research, 2018, 46(D1): D1074-D1082.
45 Boyce R D, Collins C, Horn J, et al. Modeling drug mechanism knowledge using evidence and truth maintenance[J]. IEEE Transactions on Information Technology in Biomedicine, 2007, 11(4): 386-397.
46 Herrero-Zazo M, Segura-Bedmar I, Hastings J, et al. DINTO: using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms[J]. Journal of Chemical Information and Modeling, 2015, 55(8): 1698-1707.
47 Moitra A, Palla R, Tari L, et al. Semantic inference for pharmacokinetic drug-drug interactions[C]// Proceedings of the 2014 IEEE International Conference on Semantic Computing. IEEE, 2014: 92-95.
48 Preissner S, Kroll K, Dunkel M, et al. SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions[J]. Nucleic Acids Research, 2010, 38(database issue): D237-D243.
49 Hunta S, Aunsri N, Yooyativong T. Drug-drug interactions prediction from enzyme action crossing through machine learning approaches[C]// Proceedings of the 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. IEEE, 2015: 1-4.
50 Tari L, Anwar S, Liang S, et al. Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism[J]. Bioinformatics (Oxford, England), 2010, 26(18): i547-i553.