基于语义分析的糖尿病健康教育系统研究与实现
摘要
语义网是当前万维网的延伸和扩展,它能够让人和计算机协作效果更好。基于语义的糖尿病知识检索分析系统就是采用了语义网技术,实现了糖尿病知识的优化组织和管理,并且为更好的帮助用户查询到理想的糖尿病知识提供了可能。基于语义网的检索机制与传统的基于关键词的检索相比较更加智能,并且能够有效提供资源检索的查准率和查全率。
糖尿病知识本体的建立是实现语义检索的关键。目前,糖尿病影响了全球很多人的生命。由于糖尿病病人的生活方式、预防保养和治疗等方面知识的宣传和普及能够极大地改善病人的生活质量。本文在糖尿病本体建立完成的基础上,设计了一个用户友好的糖尿病健康教育系统。对于本体的操作使用Jena来实现,使用Jena来计算本体概念类之间的语义相似度。但是语义相似度由多种因素决定,而且各种因素对语义相似度的影响各不相同。因为语义相似度影响因素的人为定义很大程度上影响到最终的结果,所以本文使用BP神经网络算法来更好地实现相似度算法,实现用户输入关键词在语义方面的扩展。这种方法能够更好地解决关键词查询带来的弊端,及其能够解决语义相似度算法受人为因素的影响,最终能够更好地实现语义相似度算法,从而使得搜索引擎能够达到很好的查全率和查准率。
在检索方面使用Lucene来实现全文搜索技术,对糖尿病资源描述库中的内容进行索引和检索。为了避免人工实现语义标注而带来的资源有限的不足,本文考虑在现有的基础上采用Heritrix爬虫来对网页知识进行抓取,从而扩充糖尿病知识库。
Semantic Web is an expansion of the current World Wide Web, it can make people and computers collaborate better. Diabetes knowledge retrieval system based on semantic analysis can realize optimal organization and management of diabetes knowledge, and make it possible to help people obtain ideal diabetes knowledge after the Semantic Web technology is applied. Semantic Web-based retrieval mechanism is more intelligent than the traditional keyword-based search, and it can improve precision and recall of resource search.
The establishment of Diabetes knowledge ontology is the key to realizing the semantic retrieval. Currently, diabetes affects the lives of many people worldwide. The publicity and outreach of knowledge of diabetes patient lifestyle, preventive maintenance and treatment can greatly improve the patient's quality of life. A user-friendly diabetes health education system is presented based on the establishment of diabetes ontology. Jena is used to implement ontology operation, and it is used to calculate the semantic similarity between the ontology classes. But the semantic similarity is influenced by a variety of factors, and the influence on the semantic similarity from various factors is different. Because the semantic similarity defined by man largely affects the final result, therefore, BP neural network algorithm is used in this article to achieve better similarity algorithm and realize the semantic extensions of keywords that the user inputted. The disadvantage of keyword query and human factors of the semantic similarity algorithm can be improved by this method. It makes the semantic similarity algorithm more efficient, and good search recall and precision can be achieved.
Lucene is applied to realize the full-text search technology in the retrieval, index and retrieve the contents of the diabetes resource description library. In order to avoid the limited resources caused by manual annotation, Heritrix crawler is considered here to crawl the knowledge on the basis of the existing web pages, and knowledge of diabetes can be expanded as a result.
糖尿病知识本体的建立是实现语义检索的关键。目前,糖尿病影响了全球很多人的生命。由于糖尿病病人的生活方式、预防保养和治疗等方面知识的宣传和普及能够极大地改善病人的生活质量。本文在糖尿病本体建立完成的基础上,设计了一个用户友好的糖尿病健康教育系统。对于本体的操作使用Jena来实现,使用Jena来计算本体概念类之间的语义相似度。但是语义相似度由多种因素决定,而且各种因素对语义相似度的影响各不相同。因为语义相似度影响因素的人为定义很大程度上影响到最终的结果,所以本文使用BP神经网络算法来更好地实现相似度算法,实现用户输入关键词在语义方面的扩展。这种方法能够更好地解决关键词查询带来的弊端,及其能够解决语义相似度算法受人为因素的影响,最终能够更好地实现语义相似度算法,从而使得搜索引擎能够达到很好的查全率和查准率。
在检索方面使用Lucene来实现全文搜索技术,对糖尿病资源描述库中的内容进行索引和检索。为了避免人工实现语义标注而带来的资源有限的不足,本文考虑在现有的基础上采用Heritrix爬虫来对网页知识进行抓取,从而扩充糖尿病知识库。
Semantic Web is an expansion of the current World Wide Web, it can make people and computers collaborate better. Diabetes knowledge retrieval system based on semantic analysis can realize optimal organization and management of diabetes knowledge, and make it possible to help people obtain ideal diabetes knowledge after the Semantic Web technology is applied. Semantic Web-based retrieval mechanism is more intelligent than the traditional keyword-based search, and it can improve precision and recall of resource search.
The establishment of Diabetes knowledge ontology is the key to realizing the semantic retrieval. Currently, diabetes affects the lives of many people worldwide. The publicity and outreach of knowledge of diabetes patient lifestyle, preventive maintenance and treatment can greatly improve the patient's quality of life. A user-friendly diabetes health education system is presented based on the establishment of diabetes ontology. Jena is used to implement ontology operation, and it is used to calculate the semantic similarity between the ontology classes. But the semantic similarity is influenced by a variety of factors, and the influence on the semantic similarity from various factors is different. Because the semantic similarity defined by man largely affects the final result, therefore, BP neural network algorithm is used in this article to achieve better similarity algorithm and realize the semantic extensions of keywords that the user inputted. The disadvantage of keyword query and human factors of the semantic similarity algorithm can be improved by this method. It makes the semantic similarity algorithm more efficient, and good search recall and precision can be achieved.
Lucene is applied to realize the full-text search technology in the retrieval, index and retrieve the contents of the diabetes resource description library. In order to avoid the limited resources caused by manual annotation, Heritrix crawler is considered here to crawl the knowledge on the basis of the existing web pages, and knowledge of diabetes can be expanded as a result.
引文
[1]周文彬.一个Web本体的采集系统[D].东南大学硕士学位论文.2006,1.
[2]B. Thuraisingham.XML Databases and the Semantic Web.CRC Press Florida USA.2002.
[3]V.N. Stroetmann, K.A. Stroetmann. Semantic Interoperability for Better Health and Safer Healthcare.European Commission.2009.
[4]J.C. van Niekerk, K. Griffiths.Advancing Health Care Management with the Semantic Web.IEEE computer society.2008.
[5]T. Berners-Lee, J. Hendler,O. Lassila.The Semantic Web.Scientific American.May 2001.
[6]HCLSIG URL www.w3.org/2001/sw/hcls/.
[7]M.Arguello, J.Des, R.Perez.Electronic Health Records (EHRs) standards and the Semantic Edge:a case study of visualising clinical information from EHRs.UKSim 2009:11th International Conference on Computer Modelling and Simulation.
[8]M. Xi, L.Qi, Z. Qiang. Application of Spatial Information Search Engine Based on Ontology in Public health Emergence.IEEE.2009.
[9]L. Ding, T. Finin, A. Joshi.Search on the Semantic Web.TR CS-05-09.
[10]H. Dong, F. K. Hussain, E. Chang.A Survey in Semantic Search Technologies.2008 Second IEEE International Conference on Digital Ecosystems and Technologies.
[11]D. Turner, M. A. Shah, Y. Bitirim.An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines:Google, Yahoo, Msn and Hakia.Fourth International Conference on Internet Monitoring and Protection.2009.
[12]陈欣.基于Web服务的数据语义集成技术研究[D].东南大学硕士学位论文.2005,23.
[13]S. Wei-ping, H. Yong-ping, Z. Peng-fei.An Ontology Learning Method for Product Configuration Domain.IEEE.2009.
[14]R. Harrison, D. Obst, C.W. Chan.Design of an Ontology Management Framework. IEEE.2009.
[15]Y. Zhao, L. Jianqiang.Domain Ontology Learning from Websites.IEEE.2009.
[16]Q. Wei.Development and Application of Knowledge Engineering Based on Ontology. IEEE.2010.
[17]Z. Ying-Hui, F. Feng-Rui, Y. Chao.The Method of Ontology Compositon on Web.3rd International Conference on Advanced Computer Theory and Engineering (ICACTE).2010.
[18]R. Studer, VR. Benjamins, D. Fensd.Knowledge Enineering, Principles and Methods. Data and Knowledge Engineering.1998,25(1-2):161-197.
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[21]T.R. Gruber. A Translation Approach to Portable Ontology Specifications.Knowledge Acquisition.1993,5(2):199-220.
[22]Y. Yalan, Z. Jinlong, Y. Mi.Ontology Modeling for Contract:Using OWL to Express Semantic Relations.Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference.2006.
[23]Y. Hongyan.Research on Building Ocean Domain Ontology.Second International Workshop on Computer Science and Engineering.2009.
[24]M. Jaszuk, G. Szostek, A. Walczak.An Ontology Building System for Structuring Medical Diagnostic Knowledge.IEEE.2010.
[25]W. Nianbin, X. Xiaofei.A Method to Build Ontology.IEEE.2000.
[26]陈洁.NPM项目管理系统及知识管理研究[D].南京理工大学硕士学位论文.2006,11.
[27]邱明.语义相似性度量及其在设计管理系统中的应用[D].浙江大学博士学位论文.2006,35.
[28]L. Sujian.Research of relevancy between sentences based on semantic computation. Computer Engeneering and Applications.2002,38(7):75-77.
[29]L. Xueqiang, R. Feiliang, H. Zhidan.Sentence similarity model and the most similar Sentence search algorithm. Journal of Northeastern University (Natural Science).2003, 24(6):531-534.
[30]C. Wanxiang, L. Ting, Q. Bing.Chinese similarity sentence retrieval based on improved edit-distance.High Technology Letters.2004,14(7):15-19.
[31]L. Bing, L. Ting, Q. Bing.Chinese sentence similarity computing based on semantic dependency relationship analysis.Computer Application Research.2003,20(12):15-17.
[32]S. Zhifang, Y. Shiwen. Similarity computing model based on framework dependency tree.International Conference on Chinese Information Processing.2005.
[33]宋玲,郭家义.概念与文档的语义相似度计算.计算机工程与应用.2008,44(35)：2
[34]Z. Wu, M. Palmer.Verb semantics and lexical selection.1994.
[35]李鹏,陶兰.一种改进的本体语义相似度计算及其应用.计算机工程与设计.2007,28(1)： 2
[36]王冠亚.BP神经网络算法在教务管理系统中对学业方向识别的研究与应用[D].中国海洋大学硕士学位论文.2009,44-52
[37]梁颖蕾.网站用户交互行为模式的可视化系统的设计和实现[D].中山大学硕士学位论文.2010,32.
[38]周登朋,谢康林.Lucene搜索引擎.计算机工程.2007,33(18)：3
[39]白坤,耿国华.基于Lucene/Heritrix的垂直搜索引擎的研究与应用.计算机应用与软件.2009,26(1)：2
[40]郎小伟,王申康.基于Lucene的全文检索系统研究与开发.计算机工程.2006,32(4)：3
[41]管建和,甘剑峰.基于Lucene全文检索引擎的应用研究与实现.计算机工程与设计.2007,28(2)：2-3
[42]Z. Guobing, Z. Bofeng, G.Yanglan.An Ontology-based Metodology for Semantic Expansion Search.IEEE.2008.
[43]邱哲,符滔滔,王学松.开发自己的搜索引擎—Lucene+Heriterx.人民邮电出版社,2010,332-429
[44]J.W. lomiu, T.C. Ionescu, R. Ruppelt.Using NDIS intermediate drivers for extending the protocol stack a case study[J].computer cornmunications.2001,24:703-715.
[45]何伟,薛素静,孔梦荣.基于Lucene的全文搜索引擎的设计与实现.情报杂志.2006,NO.9：2
[46]张铭晖.基于语义的教学资源检索系统的研究与实现[D].华中科技大学硕士学位论文.2006,54
[47]李世勇.基于混合式客户端蜜罐的恶意网址收集系统的设计与实现.武汉科技大学硕士学位论文.2008,41.
[48]吕玉鹏.基于领域本体的网页信息采集与检索研究.大连理工大学硕士学位论文.2008,43.
[49]孙垒.基于BP神经网络的模拟电路故障诊断系统的FPGA设计与实现.南京理工大学硕士学位论文.2009,11.
[50]陈叶旺.国家农业本体协同建构与语义检索若干技术研究.复旦大学博士学位论文.2009,24-25.
[2]B. Thuraisingham.XML Databases and the Semantic Web.CRC Press Florida USA.2002.
[3]V.N. Stroetmann, K.A. Stroetmann. Semantic Interoperability for Better Health and Safer Healthcare.European Commission.2009.
[4]J.C. van Niekerk, K. Griffiths.Advancing Health Care Management with the Semantic Web.IEEE computer society.2008.
[5]T. Berners-Lee, J. Hendler,O. Lassila.The Semantic Web.Scientific American.May 2001.
[6]HCLSIG URL www.w3.org/2001/sw/hcls/.
[7]M.Arguello, J.Des, R.Perez.Electronic Health Records (EHRs) standards and the Semantic Edge:a case study of visualising clinical information from EHRs.UKSim 2009:11th International Conference on Computer Modelling and Simulation.
[8]M. Xi, L.Qi, Z. Qiang. Application of Spatial Information Search Engine Based on Ontology in Public health Emergence.IEEE.2009.
[9]L. Ding, T. Finin, A. Joshi.Search on the Semantic Web.TR CS-05-09.
[10]H. Dong, F. K. Hussain, E. Chang.A Survey in Semantic Search Technologies.2008 Second IEEE International Conference on Digital Ecosystems and Technologies.
[11]D. Turner, M. A. Shah, Y. Bitirim.An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines:Google, Yahoo, Msn and Hakia.Fourth International Conference on Internet Monitoring and Protection.2009.
[12]陈欣.基于Web服务的数据语义集成技术研究[D].东南大学硕士学位论文.2005,23.
[13]S. Wei-ping, H. Yong-ping, Z. Peng-fei.An Ontology Learning Method for Product Configuration Domain.IEEE.2009.
[14]R. Harrison, D. Obst, C.W. Chan.Design of an Ontology Management Framework. IEEE.2009.
[15]Y. Zhao, L. Jianqiang.Domain Ontology Learning from Websites.IEEE.2009.
[16]Q. Wei.Development and Application of Knowledge Engineering Based on Ontology. IEEE.2010.
[17]Z. Ying-Hui, F. Feng-Rui, Y. Chao.The Method of Ontology Compositon on Web.3rd International Conference on Advanced Computer Theory and Engineering (ICACTE).2010.
[18]R. Studer, VR. Benjamins, D. Fensd.Knowledge Enineering, Principles and Methods. Data and Knowledge Engineering.1998,25(1-2):161-197.
[19]Y. Sure, R. Studer.On-To-Knowledge Methodology:Final Version, Institute of Applied Informatics and Formal Description Methods.University of Karlsruhe.2002.
[20]A. Gomez-Perez. Ontological Engineering:a State of the Art.Expert Update.1999, 2(3):33-43.
[21]T.R. Gruber. A Translation Approach to Portable Ontology Specifications.Knowledge Acquisition.1993,5(2):199-220.
[22]Y. Yalan, Z. Jinlong, Y. Mi.Ontology Modeling for Contract:Using OWL to Express Semantic Relations.Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference.2006.
[23]Y. Hongyan.Research on Building Ocean Domain Ontology.Second International Workshop on Computer Science and Engineering.2009.
[24]M. Jaszuk, G. Szostek, A. Walczak.An Ontology Building System for Structuring Medical Diagnostic Knowledge.IEEE.2010.
[25]W. Nianbin, X. Xiaofei.A Method to Build Ontology.IEEE.2000.
[26]陈洁.NPM项目管理系统及知识管理研究[D].南京理工大学硕士学位论文.2006,11.
[27]邱明.语义相似性度量及其在设计管理系统中的应用[D].浙江大学博士学位论文.2006,35.
[28]L. Sujian.Research of relevancy between sentences based on semantic computation. Computer Engeneering and Applications.2002,38(7):75-77.
[29]L. Xueqiang, R. Feiliang, H. Zhidan.Sentence similarity model and the most similar Sentence search algorithm. Journal of Northeastern University (Natural Science).2003, 24(6):531-534.
[30]C. Wanxiang, L. Ting, Q. Bing.Chinese similarity sentence retrieval based on improved edit-distance.High Technology Letters.2004,14(7):15-19.
[31]L. Bing, L. Ting, Q. Bing.Chinese sentence similarity computing based on semantic dependency relationship analysis.Computer Application Research.2003,20(12):15-17.
[32]S. Zhifang, Y. Shiwen. Similarity computing model based on framework dependency tree.International Conference on Chinese Information Processing.2005.
[33]宋玲,郭家义.概念与文档的语义相似度计算.计算机工程与应用.2008,44(35)：2
[34]Z. Wu, M. Palmer.Verb semantics and lexical selection.1994.
[35]李鹏,陶兰.一种改进的本体语义相似度计算及其应用.计算机工程与设计.2007,28(1)： 2
[36]王冠亚.BP神经网络算法在教务管理系统中对学业方向识别的研究与应用[D].中国海洋大学硕士学位论文.2009,44-52
[37]梁颖蕾.网站用户交互行为模式的可视化系统的设计和实现[D].中山大学硕士学位论文.2010,32.
[38]周登朋,谢康林.Lucene搜索引擎.计算机工程.2007,33(18)：3
[39]白坤,耿国华.基于Lucene/Heritrix的垂直搜索引擎的研究与应用.计算机应用与软件.2009,26(1)：2
[40]郎小伟,王申康.基于Lucene的全文检索系统研究与开发.计算机工程.2006,32(4)：3
[41]管建和,甘剑峰.基于Lucene全文检索引擎的应用研究与实现.计算机工程与设计.2007,28(2)：2-3
[42]Z. Guobing, Z. Bofeng, G.Yanglan.An Ontology-based Metodology for Semantic Expansion Search.IEEE.2008.
[43]邱哲,符滔滔,王学松.开发自己的搜索引擎—Lucene+Heriterx.人民邮电出版社,2010,332-429
[44]J.W. lomiu, T.C. Ionescu, R. Ruppelt.Using NDIS intermediate drivers for extending the protocol stack a case study[J].computer cornmunications.2001,24:703-715.
[45]何伟,薛素静,孔梦荣.基于Lucene的全文搜索引擎的设计与实现.情报杂志.2006,NO.9：2
[46]张铭晖.基于语义的教学资源检索系统的研究与实现[D].华中科技大学硕士学位论文.2006,54
[47]李世勇.基于混合式客户端蜜罐的恶意网址收集系统的设计与实现.武汉科技大学硕士学位论文.2008,41.
[48]吕玉鹏.基于领域本体的网页信息采集与检索研究.大连理工大学硕士学位论文.2008,43.
[49]孙垒.基于BP神经网络的模拟电路故障诊断系统的FPGA设计与实现.南京理工大学硕士学位论文.2009,11.
[50]陈叶旺.国家农业本体协同建构与语义检索若干技术研究.复旦大学博士学位论文.2009,24-25.