流程服务属性的概念关联度研究
摘要
Internet的出现正在改变人们的生活,现在人们越来越喜欢去网络获取帮助,完成自己的任务。Web服务的出现提供了一种企业之间互相自动的进行通讯的能力,使得网络应用为人们提供帮助的能力大大增强。按照流程组织各种Web服务,可以更好的满足用户复杂的、个性化的需求,也可以更加有效的组织各个Web服务,发挥Web服务的最大作用。但在很多情况下,如果流程模式的使用者由成熟规范的企业用户,渐渐转为数目巨大、个性各异的普通用户,定制统一的流程已经无法满足众多的个性化要求。当流程模式面向普通用户时,必须设计一个友好、智能的交互工具,用来获取、规范、分析用户的需求,这个交互工具应该有更加友好、灵活、智能的交互界面,并且可以自动规范和分析用户提出的需求,理解用户需求中真正的含义。
智能流程模式是一种新型的流程模式,它适合面向服务的互联网环境,可以满足用户的个性化需求,借助流程技术,提供智能化的服务。智能流程模式是一种更加灵活、更加智能、更加敏捷的新型流程模式。为了实现个性化的交互界面,针对旅游领域,本文借助于智能流程模式的思想,把旅游领域中的服务进行了分类,提出了元服务的定义,并格式化。对旅游领域的服务概念进行抽象,建立了流程服务本体。元服务中的每一个属性都关联到流程服务本体的一个概念,每个概念都有自己的实例,通过对分散的服务概念进行关联度分析,结合用户的个性化信息,挑选符合用户的属性及实例形成交互界面同用户交互,以此来体现服务属性的个性化,满足用户的需求。
本文的主要贡献在于:
一.把旅游领域中的各类服务进行分类,把旅游领域中的服务分为六类:客票预定、客房预定、就餐预定、景点预定、公交查询、旅游提示。提出了元服务及个性化属性重组的定义。
二.针对旅游领域,抽象出旅游领域中的概念,建立了适合于智能流程模式的流程服务本体,为流程服务属性的概念关联度计算提供支持,为流程服务属性的个性化重组进而生成用户个性化界面奠定基础。
三.提出了用于流程服务属性个性化重组的合适的概念关联度算法。借助于此算法,结合用户的个性化要求,挑选不同服务的属性及实例放在同一界面中交互,生成个性化的交互界面,以此来体现服务属性的个性化,达到友好交互的目的。
基于国家自然基金项目“智能流程应用模式中的关键问题研究”的智能流程应用平台,我们课题组以智能旅游为背景,实现了一个智能虚拟旅行社平台IPVita(An Intelligent Platform of Virtual Travel Agency)。实验证明,此方法在IPVita平台上可以很好的计算流程服务的概念关联度,通过挑选合适的服务属性及实例,能够为用户生成友好的、个性化的交互界面,满足用户的个性化需求。
The emergence of Internet is changing people's lives, now more and more people like to go to the internet for help and to complete their tasks. The emergence of Web services provides the ability to automated communication between enterprises and makes the ability of network applications providing help for people greatly enhanced. Organizing various Web services according to process can better meet the complex and personality demands and organize various Web services more effectively to make web services play the biggest role. However, in many cases, if the user of process mode has gradually changed from the mature enterprise users to the huge number of ordinary users with personality, customizing the uniform process has not met the many individual requirements. When the process mode faces ordinary users, we must design a friendly, intelligent interactive tools to access, standardize, analyze the needs of users. This interactive tool should have more friendly, flexible, intelligent interface and can automatically regulate and analyze the needs of users understand the real meaning of user needs.
Smartflow Mode is a new type of process model, it is suitable for service-oriented internet environment. It can meet the user's specific needs and provide intelligent services with the help of process technologies. Smartflow Mode is a new more flexible, more intelligent, more agile process model. In order to realize personalized interactive interface, in view of the field of tourism, depending on Smartflow Mode,this paper gives the classification of the services in the field of tourism. This paper gives the the definition of Meta Sevice and formatting it. On the abstraction of the tourism services,we construct a process services ontology. Each attribute of Meta Service has been linked to a concept of service ontology. Each concept has its own instance. Depending on the relatedness degree analysis of the decentralized services concepts, We can choose the attribute instances fitting specific users to form the interactive interface with the user's customized information in order to reflect the personality of the service attributes and to meet the needs of users.
The main contribution of this paper is as fallows:
First,it gives the classification to the various services in the field of tourism. Travel services are divided into six categories: ticket reservations,hotel reservations, dining reservations, attractions scheduled,bus enquiries, travel tips. It gives the definition of meta-service and reorganization of personality attributes.
Second, it abstracts concepts from the field of tourism and establishs the process service ontology fitting Smartflow Mode. The service ontology provides support for computation of concepts relatedness degree of process service a1tributes. It also lays the foundation for reorganization of process service attributes and generating customized interface.
Third,it proposes the suitable concepts relatedness degree algorithm for personality reorganization of the process services attributes. Depending on this algorithm, with the the users' personality requirements, it chooses the attributes instances of different services on the same interactive interface to generate personalized interactive interface in order to reflect the personality of the service attributes and achieve the purpose of friendly interaction.
Based on Smartflow application platform of National Nature fund project" the key issues in the Smartflow mode", our group realize IPVita (An Intelligent Platform of Virtual Travel Agency) on the background of intelligent tourism. Experiments show that this method can be very good at calculating the concept relatedness degree and choose the proper service attributes instances to generate friendly, personalized interactive interface to meet the user's specific needs.
智能流程模式是一种新型的流程模式,它适合面向服务的互联网环境,可以满足用户的个性化需求,借助流程技术,提供智能化的服务。智能流程模式是一种更加灵活、更加智能、更加敏捷的新型流程模式。为了实现个性化的交互界面,针对旅游领域,本文借助于智能流程模式的思想,把旅游领域中的服务进行了分类,提出了元服务的定义,并格式化。对旅游领域的服务概念进行抽象,建立了流程服务本体。元服务中的每一个属性都关联到流程服务本体的一个概念,每个概念都有自己的实例,通过对分散的服务概念进行关联度分析,结合用户的个性化信息,挑选符合用户的属性及实例形成交互界面同用户交互,以此来体现服务属性的个性化,满足用户的需求。
本文的主要贡献在于:
一.把旅游领域中的各类服务进行分类,把旅游领域中的服务分为六类:客票预定、客房预定、就餐预定、景点预定、公交查询、旅游提示。提出了元服务及个性化属性重组的定义。
二.针对旅游领域,抽象出旅游领域中的概念,建立了适合于智能流程模式的流程服务本体,为流程服务属性的概念关联度计算提供支持,为流程服务属性的个性化重组进而生成用户个性化界面奠定基础。
三.提出了用于流程服务属性个性化重组的合适的概念关联度算法。借助于此算法,结合用户的个性化要求,挑选不同服务的属性及实例放在同一界面中交互,生成个性化的交互界面,以此来体现服务属性的个性化,达到友好交互的目的。
基于国家自然基金项目“智能流程应用模式中的关键问题研究”的智能流程应用平台,我们课题组以智能旅游为背景,实现了一个智能虚拟旅行社平台IPVita(An Intelligent Platform of Virtual Travel Agency)。实验证明,此方法在IPVita平台上可以很好的计算流程服务的概念关联度,通过挑选合适的服务属性及实例,能够为用户生成友好的、个性化的交互界面,满足用户的个性化需求。
The emergence of Internet is changing people's lives, now more and more people like to go to the internet for help and to complete their tasks. The emergence of Web services provides the ability to automated communication between enterprises and makes the ability of network applications providing help for people greatly enhanced. Organizing various Web services according to process can better meet the complex and personality demands and organize various Web services more effectively to make web services play the biggest role. However, in many cases, if the user of process mode has gradually changed from the mature enterprise users to the huge number of ordinary users with personality, customizing the uniform process has not met the many individual requirements. When the process mode faces ordinary users, we must design a friendly, intelligent interactive tools to access, standardize, analyze the needs of users. This interactive tool should have more friendly, flexible, intelligent interface and can automatically regulate and analyze the needs of users understand the real meaning of user needs.
Smartflow Mode is a new type of process model, it is suitable for service-oriented internet environment. It can meet the user's specific needs and provide intelligent services with the help of process technologies. Smartflow Mode is a new more flexible, more intelligent, more agile process model. In order to realize personalized interactive interface, in view of the field of tourism, depending on Smartflow Mode,this paper gives the classification of the services in the field of tourism. This paper gives the the definition of Meta Sevice and formatting it. On the abstraction of the tourism services,we construct a process services ontology. Each attribute of Meta Service has been linked to a concept of service ontology. Each concept has its own instance. Depending on the relatedness degree analysis of the decentralized services concepts, We can choose the attribute instances fitting specific users to form the interactive interface with the user's customized information in order to reflect the personality of the service attributes and to meet the needs of users.
The main contribution of this paper is as fallows:
First,it gives the classification to the various services in the field of tourism. Travel services are divided into six categories: ticket reservations,hotel reservations, dining reservations, attractions scheduled,bus enquiries, travel tips. It gives the definition of meta-service and reorganization of personality attributes.
Second, it abstracts concepts from the field of tourism and establishs the process service ontology fitting Smartflow Mode. The service ontology provides support for computation of concepts relatedness degree of process service a1tributes. It also lays the foundation for reorganization of process service attributes and generating customized interface.
Third,it proposes the suitable concepts relatedness degree algorithm for personality reorganization of the process services attributes. Depending on this algorithm, with the the users' personality requirements, it chooses the attributes instances of different services on the same interactive interface to generate personalized interactive interface in order to reflect the personality of the service attributes and achieve the purpose of friendly interaction.
Based on Smartflow application platform of National Nature fund project" the key issues in the Smartflow mode", our group realize IPVita (An Intelligent Platform of Virtual Travel Agency) on the background of intelligent tourism. Experiments show that this method can be very good at calculating the concept relatedness degree and choose the proper service attributes instances to generate friendly, personalized interactive interface to meet the user's specific needs.
引文
[1]CTrip,http://www.ctrip.com.
[2]ELong,http://www.elong.com.
[3]baidu,http://www.baidu.com.
[4]google,http://www.google.com.
[5]yahoo,http://www.yahoo.com.
[6]朱礼军,陶兰,黄赤1语义万维网的概念、方法及应用(J)1计算机工程与应用,2004(3):79-801
[7]A.Budanitsky,Lexical Semantic Relatedness and Its Application in Natural Language Processing,Processing Computer Systems Research Group,University of Toronto,1999.
[8]Rada,Roy,Hafedh Mili,Ellen Bicknell,and Maria Blettner (1989).Development and Application of a Metric on Semantic Nets.IEEE Transactions on Systems,Man and Cybernetics 19:17-30.
[9]Richardson,R.,A.Smeaton,and J.Murphy Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words.Technical Report,Working paper CA-1294,School of Computer Applications,Dublin City University:Dublin,Ireland.1994
[10]Graeme Hirst and David St-Onge." Lexical chains as representations of context for the detection and correction of malapropisms." In Christiane Fellbaum,editor,WordNet:An Electronic Lexical Database,chapter 13,pages 305-332.The MIT Press,Cambridge,MA,1998.
[11]R.Rada,H.Mili,E.Bicknell,M.Blettner,Development and application of a metric on semantic nets,IEEE Transactions on Systems 19(1)(1989)17-30.
[12]P.Resnik,Using information content to evaluate semantic similarity in a taxonomy,in:Proceedings of the 14th International Joint Conference on Artificial Intelligence,1995.
[13]J.Jiang,D.Conrath,Semantic similarity based on corpus statistics and lexical taxonomy,in:Proceedings of the 10th International Conference on Research on Computational Linguistics,1997.
[14]D.Lin,An information-theoretic definition of similarity,in:Proceedings of the 15th International Conference on Machine Learning,1998.
[15]徐德智,郑春卉,Passi K.基于SUMO的概念语义相似度研究[J].计算机应用,2006,26(1):180-183.
[16]吴健,吴朝晖,李莹.基于本体论和词汇语义相似度的Web服务发现[J].计算机学报,2005,28(4):595-602.
[17]Wong A K Y,Ray P,Waran N P.Ontology Mapping for the Interoperability Problem in Network Management[J].IEEE Journal on Selected Areasin Communication,2005,23(10):2058-2068.
[18]Doan A H,Madhavan J,Domingos P.Learning to Map Between Ontologies on the Semantic Web[C]//Proceedings of the 11~(th)International Conference on World Wide Web,New York,USA.ACM Press,2002:662-673.
[19]Higgins D.Which Statistics Reflect Semantic? Rerhinking Synonymy and Word Similarity[C]//Proc.of International Conference on Linguistic Evidence.2004.
[20]Boualem Benatallah,Quan Z.Sheng,Anne H.H.Ngu,Marion Dumas:Declarative Composition and Peer-to-Peer Provisioning of Dynamic Web Services.ICDE 2002:297-308
[21].M R Genesereth,N J Nilsson.Logical Foundations of Artificial Intelligencel San Mateo:Morgan Kaufmann Publishers,1987
[22] T R Gruberl A translation approach to portable ontology specifica2 tionsl Stanford University , Tech Rep : Logic29221 , 1993
[23] T R Gruberl Towards principles for the design of ontologies used for knowledge sharingl Stanford University , Tech Rep : KSL293204 ,1993
[24] http://www.w3.org/TR/REC- rdf-syntax/, 1999
[25] W3C. RDF Schema. http://Web.w3.org/TR/rdf-schema/, 2002
[26] OWL[EB/OL]. http://www. w3. org/2004/OWL/, 2004.
[27] http://www. sts. tu-harburg. de/～r. f. moeller/racer/
[28] http://herzberg.ca. sandia. gov/
[29] http://pellet.owldl.com/
[30] Klein M. Combining and Relating Ontologies: An Analysis of Problems and Solutions[C]. Proc. of IJCAI' 01 Workshop on Ontologies and Information Sharing, Seattle, WA, 2001: 53-62.
[31] Wache H, Vgele T. Ontology-based Integration of Information ---- a Survey of Existing Approaches[C]. Proceedings of the Workshop Ontologies and Information Sharing, 2001.
[32] Prasenjit M. A Graph-oriented Model for Articulation of Ontology Interdependencies[C]. Proc. of EDBT' 00. Springer-Verlag, 2000:86-100.
[33] http://www. ontoknowledge. org/oil/
[34] KIF: http://logic.stanford.edu/kif/dpans.html
[35] http://protege. Stanford. edu/
[36] http://www. hpl. hp. com/personal/bwm/rdf/jena/
[37] DAML[EB/OL]. http://www. daml. org/asd, 2004.
[38]A. Budanitsky, Lexical Semantic Relatedness and Its Application in Natural Language Processing, Processing Computer Systems Research Group, University of Toronto,1999.
[39]N.Seco,T.Veale,and J.Hayes,An Intrinsic Information Content Metric for Semantic Similarity in WordNet,ECAI 2004:1089-1090,2004.
[40]V.Cross and T.Sudkamp,Similarity and Compatibility in Fuzzy Set Theory:Assessment and Applications,New York:Physica-Verlag,ISBN 3-7908-1458,2002
[41]M.Andrea Rodriguez and Max J.Egenhofer,"Determining Semantic Similarity Among Entity Classes from Different Ontologies",IEEE Transactions on Knowledge and Data Engineering,15(2):442-456,2003.
[42]A.Rodriguez,M.Egenhofer,and R.Rugg,"Assessing Semantic Similarity Among Geospatial Entity Class Definitions,"Interoperating Geographic Information Systems INTEROP99,A.Vckovski,K.Brassel,and H.-J.Schek,eds.,pp.189-202,1999.
[43]A.Smeaton and I.Quigley,"Experiment on Using Semantic Distance Between Words in Image Caption Retrieval," Proc.19~(th)Int'1 Conf.Research and Development in Information Retrieval SIGIR' 96,1996.
[2]ELong,http://www.elong.com.
[3]baidu,http://www.baidu.com.
[4]google,http://www.google.com.
[5]yahoo,http://www.yahoo.com.
[6]朱礼军,陶兰,黄赤1语义万维网的概念、方法及应用(J)1计算机工程与应用,2004(3):79-801
[7]A.Budanitsky,Lexical Semantic Relatedness and Its Application in Natural Language Processing,Processing Computer Systems Research Group,University of Toronto,1999.
[8]Rada,Roy,Hafedh Mili,Ellen Bicknell,and Maria Blettner (1989).Development and Application of a Metric on Semantic Nets.IEEE Transactions on Systems,Man and Cybernetics 19:17-30.
[9]Richardson,R.,A.Smeaton,and J.Murphy Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words.Technical Report,Working paper CA-1294,School of Computer Applications,Dublin City University:Dublin,Ireland.1994
[10]Graeme Hirst and David St-Onge." Lexical chains as representations of context for the detection and correction of malapropisms." In Christiane Fellbaum,editor,WordNet:An Electronic Lexical Database,chapter 13,pages 305-332.The MIT Press,Cambridge,MA,1998.
[11]R.Rada,H.Mili,E.Bicknell,M.Blettner,Development and application of a metric on semantic nets,IEEE Transactions on Systems 19(1)(1989)17-30.
[12]P.Resnik,Using information content to evaluate semantic similarity in a taxonomy,in:Proceedings of the 14th International Joint Conference on Artificial Intelligence,1995.
[13]J.Jiang,D.Conrath,Semantic similarity based on corpus statistics and lexical taxonomy,in:Proceedings of the 10th International Conference on Research on Computational Linguistics,1997.
[14]D.Lin,An information-theoretic definition of similarity,in:Proceedings of the 15th International Conference on Machine Learning,1998.
[15]徐德智,郑春卉,Passi K.基于SUMO的概念语义相似度研究[J].计算机应用,2006,26(1):180-183.
[16]吴健,吴朝晖,李莹.基于本体论和词汇语义相似度的Web服务发现[J].计算机学报,2005,28(4):595-602.
[17]Wong A K Y,Ray P,Waran N P.Ontology Mapping for the Interoperability Problem in Network Management[J].IEEE Journal on Selected Areasin Communication,2005,23(10):2058-2068.
[18]Doan A H,Madhavan J,Domingos P.Learning to Map Between Ontologies on the Semantic Web[C]//Proceedings of the 11~(th)International Conference on World Wide Web,New York,USA.ACM Press,2002:662-673.
[19]Higgins D.Which Statistics Reflect Semantic? Rerhinking Synonymy and Word Similarity[C]//Proc.of International Conference on Linguistic Evidence.2004.
[20]Boualem Benatallah,Quan Z.Sheng,Anne H.H.Ngu,Marion Dumas:Declarative Composition and Peer-to-Peer Provisioning of Dynamic Web Services.ICDE 2002:297-308
[21].M R Genesereth,N J Nilsson.Logical Foundations of Artificial Intelligencel San Mateo:Morgan Kaufmann Publishers,1987
[22] T R Gruberl A translation approach to portable ontology specifica2 tionsl Stanford University , Tech Rep : Logic29221 , 1993
[23] T R Gruberl Towards principles for the design of ontologies used for knowledge sharingl Stanford University , Tech Rep : KSL293204 ,1993
[24] http://www.w3.org/TR/REC- rdf-syntax/, 1999
[25] W3C. RDF Schema. http://Web.w3.org/TR/rdf-schema/, 2002
[26] OWL[EB/OL]. http://www. w3. org/2004/OWL/, 2004.
[27] http://www. sts. tu-harburg. de/～r. f. moeller/racer/
[28] http://herzberg.ca. sandia. gov/
[29] http://pellet.owldl.com/
[30] Klein M. Combining and Relating Ontologies: An Analysis of Problems and Solutions[C]. Proc. of IJCAI' 01 Workshop on Ontologies and Information Sharing, Seattle, WA, 2001: 53-62.
[31] Wache H, Vgele T. Ontology-based Integration of Information ---- a Survey of Existing Approaches[C]. Proceedings of the Workshop Ontologies and Information Sharing, 2001.
[32] Prasenjit M. A Graph-oriented Model for Articulation of Ontology Interdependencies[C]. Proc. of EDBT' 00. Springer-Verlag, 2000:86-100.
[33] http://www. ontoknowledge. org/oil/
[34] KIF: http://logic.stanford.edu/kif/dpans.html
[35] http://protege. Stanford. edu/
[36] http://www. hpl. hp. com/personal/bwm/rdf/jena/
[37] DAML[EB/OL]. http://www. daml. org/asd, 2004.
[38]A. Budanitsky, Lexical Semantic Relatedness and Its Application in Natural Language Processing, Processing Computer Systems Research Group, University of Toronto,1999.
[39]N.Seco,T.Veale,and J.Hayes,An Intrinsic Information Content Metric for Semantic Similarity in WordNet,ECAI 2004:1089-1090,2004.
[40]V.Cross and T.Sudkamp,Similarity and Compatibility in Fuzzy Set Theory:Assessment and Applications,New York:Physica-Verlag,ISBN 3-7908-1458,2002
[41]M.Andrea Rodriguez and Max J.Egenhofer,"Determining Semantic Similarity Among Entity Classes from Different Ontologies",IEEE Transactions on Knowledge and Data Engineering,15(2):442-456,2003.
[42]A.Rodriguez,M.Egenhofer,and R.Rugg,"Assessing Semantic Similarity Among Geospatial Entity Class Definitions,"Interoperating Geographic Information Systems INTEROP99,A.Vckovski,K.Brassel,and H.-J.Schek,eds.,pp.189-202,1999.
[43]A.Smeaton and I.Quigley,"Experiment on Using Semantic Distance Between Words in Image Caption Retrieval," Proc.19~(th)Int'1 Conf.Research and Development in Information Retrieval SIGIR' 96,1996.