基于RDF的个性化服务模型
摘要
1990年，WWW（World Wide Web）出现，在随后的几年中它获得了空前的发展，Internet上的信息量以指数形式飞速增长。互联网上蕴藏着的海量信息，对于用户来说已经大大超过了可能的阅读量。通常用户在吸取有用信息的同时，也无奈的接收了许多无用的信息，浪费了大量的时间。现在，用户如何才能有效的获取有用信息，网站如何才能更有效地把信息推荐给用户，已经成为许多用户和网站经营者共同关心的问题，也成为一家网络公司是否能在市场竞争中掌握先机的关键。为此，本论文提出了以RDF元数据集为基础的个性化服务模型。论文的主要研究内容如下：
（1）论文对当前的个性化服务模型进行了分类研究，分析了其核心技术以及国际上主要的个性化服务的已有系统，该部分的研究对把握国际上有关此方面的最新技术具有重要的指导意义。
（2）论文针对个性化服务研究中的用户兴趣提取和表示技术进行了详细的研究，总结了用户兴趣提取和表示技术的核心问题；分析了当前的用户兴趣提取和表示的主要算法，并给出了一种新的用户兴趣提取算法和基于RDF的用户兴趣表示方法，仿真试验表明，这种算法和表示法能够准确的表达用户的实际兴趣。
（3）论文提出了一个根据用户兴趣进行网络信息过滤的新模型，其核心思想是：在过滤之前将信息用RDF元数据集表达出来，然后再与同样是以RDF表示的用户兴趣进行比较。这种算法的优点在于：信息过滤速度快、准确率高、不受信息的不同分类的限制、模型的应用范围广泛、有较强的实用性。
（4）从用户的角度出发，论文还提出了几个供用户与系统进行有效交互的渠道。从而使用户可以对系统的工作进行全面监控，体现了用户的自主性和能动性。
在论文中，利用了RDF元数据技术来表示用户兴趣和网络信息资源，采用Agent技术来挖掘用户上网的历史信息，从而发现用户的兴趣所在，然后再根据不同用户的不同兴趣来为用户提供不同的信息服务。该模型的研究，在网站的个性化服务领域进行了有益的探索，具有一定的研究价值。
Alonge with the World Wide Web's appearance in 1990, WWW developed amazingly. The anount of information on the Internet increased exponentially. And the users using Internet can't accept so much information contained by Internet. When the users receive available information, the users accept a great deal of unavailable information unwillingly . Now it is become the key probolem attended by the users using Internet and the operators of web-stations that how the users can obtain availabel information and how the web-station's operators can recommend the information what the users are interesting in. In the thesis, it is presented that a personalized service model based on RDF meta-data collection. The next is the main contend of the thesis.
(1) The paper processed a study about the personalized service models existing and analyzed the core technology of those models. The part of the study is important for comprehending the last technology of the personalized service.
(2) In the dissertation , we were engaged in research about the users' interests retrieval and expression in personalized service. Then we summarized the core problem of the users' interests retrieval and expression；analyzed the arithmetics about the retrieval and expression. At last we advanced a new arithmetic of users' interests retrieval and a new method based on RDF meta-data collection to express users' interests . The experiment about the arithmetics proves that it can retrieval and express users'interests .
(3)In our study,we created a web-information filtering arithmetic according to the user's interests.The core of the arithmetic is using RDF meta-data collection to denote the web-information before filtering,then comparing the web-information's RDF denotation with user's interests expressing in RDF. The arithmetic's virtues are the speed of filtering , high veracity , ect.
(4)In the dissertation, there are a few methods for users to communicate with the system. So the users can control the system across-the-aboard .
In our study , we use RDF meta-data collection to express users' interests and
web information resource, use Agent technology to mine the history information of the users' access log of Internet , and summarize users' interests. Then we provide
distinct service for different user according to the their respective interests. The study about the model is importance to provide personalized service for the users using Internet.
（1）论文对当前的个性化服务模型进行了分类研究，分析了其核心技术以及国际上主要的个性化服务的已有系统，该部分的研究对把握国际上有关此方面的最新技术具有重要的指导意义。
（2）论文针对个性化服务研究中的用户兴趣提取和表示技术进行了详细的研究，总结了用户兴趣提取和表示技术的核心问题；分析了当前的用户兴趣提取和表示的主要算法，并给出了一种新的用户兴趣提取算法和基于RDF的用户兴趣表示方法，仿真试验表明，这种算法和表示法能够准确的表达用户的实际兴趣。
（3）论文提出了一个根据用户兴趣进行网络信息过滤的新模型，其核心思想是：在过滤之前将信息用RDF元数据集表达出来，然后再与同样是以RDF表示的用户兴趣进行比较。这种算法的优点在于：信息过滤速度快、准确率高、不受信息的不同分类的限制、模型的应用范围广泛、有较强的实用性。
（4）从用户的角度出发，论文还提出了几个供用户与系统进行有效交互的渠道。从而使用户可以对系统的工作进行全面监控，体现了用户的自主性和能动性。
在论文中，利用了RDF元数据技术来表示用户兴趣和网络信息资源，采用Agent技术来挖掘用户上网的历史信息，从而发现用户的兴趣所在，然后再根据不同用户的不同兴趣来为用户提供不同的信息服务。该模型的研究，在网站的个性化服务领域进行了有益的探索，具有一定的研究价值。
Alonge with the World Wide Web's appearance in 1990, WWW developed amazingly. The anount of information on the Internet increased exponentially. And the users using Internet can't accept so much information contained by Internet. When the users receive available information, the users accept a great deal of unavailable information unwillingly . Now it is become the key probolem attended by the users using Internet and the operators of web-stations that how the users can obtain availabel information and how the web-station's operators can recommend the information what the users are interesting in. In the thesis, it is presented that a personalized service model based on RDF meta-data collection. The next is the main contend of the thesis.
(1) The paper processed a study about the personalized service models existing and analyzed the core technology of those models. The part of the study is important for comprehending the last technology of the personalized service.
(2) In the dissertation , we were engaged in research about the users' interests retrieval and expression in personalized service. Then we summarized the core problem of the users' interests retrieval and expression；analyzed the arithmetics about the retrieval and expression. At last we advanced a new arithmetic of users' interests retrieval and a new method based on RDF meta-data collection to express users' interests . The experiment about the arithmetics proves that it can retrieval and express users'interests .
(3)In our study,we created a web-information filtering arithmetic according to the user's interests.The core of the arithmetic is using RDF meta-data collection to denote the web-information before filtering,then comparing the web-information's RDF denotation with user's interests expressing in RDF. The arithmetic's virtues are the speed of filtering , high veracity , ect.
(4)In the dissertation, there are a few methods for users to communicate with the system. So the users can control the system across-the-aboard .
In our study , we use RDF meta-data collection to express users' interests and
web information resource, use Agent technology to mine the history information of the users' access log of Internet , and summarize users' interests. Then we provide
distinct service for different user according to the their respective interests. The study about the model is importance to provide personalized service for the users using Internet.
引文
1． Dreilinger D,Howe A E. Experiences with selecting search engine using metasearch.Acm Trans on Inf Sys,1997,15(3):195-222.
2． Thorstoen Joachims et at. Web Watcher:A Tour Guide for the WWW. http://www.cs.cmu.edu.
3． Mladenic D. Turning Yahoo into an automatic Web-page classifier. ECAI 98. In:13th European Conference on Artificial Intelligence. John Wiley & Sons Ltd. 1998 , 473-474.
4． Balabanovic M，Shoham Y，Yun Y. An Adaptive Agent for Automated Web Browsing. Available at: Http:// WWW.diglib.stanford.edu/cgi-bin/get/SIDL-WP.
5． Lieberman H.(1997)Autonomous Interface Agents. Proceedings of the ACM Conference on Computers and Human Interface,CHI-97,Atlanta,Georgia.
6． Filippo Menczer,Richard K. Belew, Wolfram Willuhn, "Artificial Life Applied to Adaptive Information Agents", AAAI Spring Symposium Series: Information gathering from heterogeneous, distributed environment, AAAI,2000.
7． 邹涛，王继成，朱华宇，金翔宇，张福炎，WWW上的信息挖掘技术及实现。计算机研究与发展，1999，36（8）：1019--1024.
8． Eui-Hong Han，Daniel Boley，Maria Gini，Robert Gross，Kyle Hastings，George Karypis，Vipin Kumar，Bamshad Mobasher，Jerome Moore."WebACE:A Web Agent for Document Categorization and Exploration" ，proceedings of the second international conference on Autonomous Agent，1999.
9． Liren Chen，Katia Sycara，"WebMate:A Personal Agent for Browsing and Searching " ，proceeding of the second international conference on Autonomous Agents，1999.
10． Nwana H S. Software Agents:an overview. The Knowledge Engineering Review, 1996,11(3):205～244.
11． Dwi H. Widyantoro， Thomas R. Ioerger，John Yen，"An Adaptive Algorithm for Learning Changes in User Interests" ，Eighth International Conference on Information and Knowledge Management (CIKM'99) ，1999.
A. Joshi and R. Krishnapuram. On Mining Web Access Logs. In Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 2000,
12． pp. 63-69, 2000.
13． Namespaces in XML, W3C Recommendation, 14 January 1999，http://www.w3.org/TR/REC-xml-names.
14． Dublin Core Metadata Element Set, Version 1.1: Reference Description http://dublincore.org/documents/1999/07/02/dces/.
15． W3C Recommendation of Resource Description Framework (RDF) Model and Syntax， RDF Abbreviated Syntax ，http://www.w3.org/TR/REC-rdf-syntax/#abbreviatedSyntax.
16． D. Megginson, SourceForge, SAX Simple API for XML, version 2，5 May 2000. This document is http://sax.sourceforge.net/.
17． XML Schema Part 0: Primer - W3C Recommendation, World Wide Web Consortium, 2 May 2001.
18． XML Schema Part 1: Structures - W3C Recommendation, World Wide Web Consortium, 2 May 2001.
19． XML Schema Part 1: Structures - W3C Recommendation, World Wide Web Consortium, 2 May 2001.
20． 黄伟红，张福炎，基于XML/RDF的MARC元数据描述技术，情报学报，2000年，19卷，4期，326--332页.
21． 马献明，严小卫，个性化网上信息代理技术的研究概述，广西师范大学学报（自然科学版），2000年，18卷，3期，56--60页.
22． 路海明，卢增祥，基于多Agent混合智能实现个性化网络信息推荐，计算机科学，2000年，27卷，7期，32--34页.
23． 赵加欣，陈跃新，网上信息Agent，计算机科学，2000年，27卷，6期，82--86页.
24． 范焱，郑诚，王清毅，蔡情生，刘洁，用Na?ve Bayes方法协调分类Web网页，软件学报，2001年，12卷，9期1387--1391页.
25． 张义忠，赵明生，朱精南，基于内容的网页特征提取，计算机工程与应用，2001年，10期，1--3页.
26． 朱明，王军，王俊普，Web网页识别中的特征选择问题研究，计算机工程，2000年，26卷，8期，35--37页.
27． 邹涛，王继成，中文文档自动分类系统的设计与实现，中文信息学报，1999年，13卷， 3期，26--32页.
28． 钟涛，陈新明，万均，张世永，中文文本Web搜索引擎的设计与实现，计算机工程与应用，2001年，17期，149--151页.
29． 邹海山，吴勇，吴月珠，陈阵，中文搜索引擎中的中文信息处理技术，计算机应用研究，2000年，12期，21--24页.
30． Schwab I., Pohl W. and Koychev, I. (2000). Learning to Recommend from Positive，Evidence, Proceedings of Intelligent User Interfaces 2000, ACM Press, pp.241-247.
31． Maloof M. and Michalski R. (2000). Selecting examples for partial memory learning. Machine Learning 41, 27-52.
32． M. Spiliopoulou. Web Usage Mining for Web Site Evaluation. Communications of the ACM, Vol. 43(8):pp. 127{134, August 2000.
33． S. Perugini, P. Lakshminarayanan, and N. Ramakrishnan. Web Mechanics: Personalizing the GAMS Cross-Index. IEEE/AIP Computing in Science and Engineering, 2000. Invited Paper.
34． N. Ramakrishnan. PIPE: Personalization is Partial Evaluation. IEEE Internet Computing, Vol. 4(6), Nov-Dec 2000. Accepted for Publication.
35． D. Riecken. Personalized Views of Personalization. Communications of the ACM, Vol. 43(8):pp. 26{28, 2000}.
36． 吴建，吴长春，个性化网站中用户分类的研究，计算机应用研究2001年，5期，18--20页.
37． 欧洁，林守勋，个性化智能信息提取中的用户发现，计算机科学，2001年，11卷，3期，112--115页.
38． 张卫丰，徐宝文，许蕾，陈振强，赵凯华，利用Agent个性化搜索结果，小型微型计算机系统，2001年，22卷，6期，724--727页
39． S. Abiteboul, P Buneman, and D. Suciu. Data on the Web: From Relations to Semistructured Data and XML. Morgan Kaufmann Publishers, 2000.
40． G. Adomavicius and A. Tuzhilin. Expert-Driven Validation of Rule-Based User Models in Personalization Applications. Data Mining and Knowledge Discovery, Vol. 5, 2001.
41． N.J. Belkin. Helping People Find What They Don't Know. Communications of the ACM, Vol. 43(8):pp. 59{61, August 2000.}.
42． S. Boykin and A. Merlino. Machine Learning of Event Segmentation for News On Demand. Comminications of the ACM, Vol. 43(2):pp. 35{41, 2000.}.
2． Thorstoen Joachims et at. Web Watcher:A Tour Guide for the WWW. http://www.cs.cmu.edu.
3． Mladenic D. Turning Yahoo into an automatic Web-page classifier. ECAI 98. In:13th European Conference on Artificial Intelligence. John Wiley & Sons Ltd. 1998 , 473-474.
4． Balabanovic M，Shoham Y，Yun Y. An Adaptive Agent for Automated Web Browsing. Available at: Http:// WWW.diglib.stanford.edu/cgi-bin/get/SIDL-WP.
5． Lieberman H.(1997)Autonomous Interface Agents. Proceedings of the ACM Conference on Computers and Human Interface,CHI-97,Atlanta,Georgia.
6． Filippo Menczer,Richard K. Belew, Wolfram Willuhn, "Artificial Life Applied to Adaptive Information Agents", AAAI Spring Symposium Series: Information gathering from heterogeneous, distributed environment, AAAI,2000.
7． 邹涛，王继成，朱华宇，金翔宇，张福炎，WWW上的信息挖掘技术及实现。计算机研究与发展，1999，36（8）：1019--1024.
8． Eui-Hong Han，Daniel Boley，Maria Gini，Robert Gross，Kyle Hastings，George Karypis，Vipin Kumar，Bamshad Mobasher，Jerome Moore."WebACE:A Web Agent for Document Categorization and Exploration" ，proceedings of the second international conference on Autonomous Agent，1999.
9． Liren Chen，Katia Sycara，"WebMate:A Personal Agent for Browsing and Searching " ，proceeding of the second international conference on Autonomous Agents，1999.
10． Nwana H S. Software Agents:an overview. The Knowledge Engineering Review, 1996,11(3):205～244.
11． Dwi H. Widyantoro， Thomas R. Ioerger，John Yen，"An Adaptive Algorithm for Learning Changes in User Interests" ，Eighth International Conference on Information and Knowledge Management (CIKM'99) ，1999.
A. Joshi and R. Krishnapuram. On Mining Web Access Logs. In Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 2000,
12． pp. 63-69, 2000.
13． Namespaces in XML, W3C Recommendation, 14 January 1999，http://www.w3.org/TR/REC-xml-names.
14． Dublin Core Metadata Element Set, Version 1.1: Reference Description http://dublincore.org/documents/1999/07/02/dces/.
15． W3C Recommendation of Resource Description Framework (RDF) Model and Syntax， RDF Abbreviated Syntax ，http://www.w3.org/TR/REC-rdf-syntax/#abbreviatedSyntax.
16． D. Megginson, SourceForge, SAX Simple API for XML, version 2，5 May 2000. This document is http://sax.sourceforge.net/.
17． XML Schema Part 0: Primer - W3C Recommendation, World Wide Web Consortium, 2 May 2001.
18． XML Schema Part 1: Structures - W3C Recommendation, World Wide Web Consortium, 2 May 2001.
19． XML Schema Part 1: Structures - W3C Recommendation, World Wide Web Consortium, 2 May 2001.
20． 黄伟红，张福炎，基于XML/RDF的MARC元数据描述技术，情报学报，2000年，19卷，4期，326--332页.
21． 马献明，严小卫，个性化网上信息代理技术的研究概述，广西师范大学学报（自然科学版），2000年，18卷，3期，56--60页.
22． 路海明，卢增祥，基于多Agent混合智能实现个性化网络信息推荐，计算机科学，2000年，27卷，7期，32--34页.
23． 赵加欣，陈跃新，网上信息Agent，计算机科学，2000年，27卷，6期，82--86页.
24． 范焱，郑诚，王清毅，蔡情生，刘洁，用Na?ve Bayes方法协调分类Web网页，软件学报，2001年，12卷，9期1387--1391页.
25． 张义忠，赵明生，朱精南，基于内容的网页特征提取，计算机工程与应用，2001年，10期，1--3页.
26． 朱明，王军，王俊普，Web网页识别中的特征选择问题研究，计算机工程，2000年，26卷，8期，35--37页.
27． 邹涛，王继成，中文文档自动分类系统的设计与实现，中文信息学报，1999年，13卷， 3期，26--32页.
28． 钟涛，陈新明，万均，张世永，中文文本Web搜索引擎的设计与实现，计算机工程与应用，2001年，17期，149--151页.
29． 邹海山，吴勇，吴月珠，陈阵，中文搜索引擎中的中文信息处理技术，计算机应用研究，2000年，12期，21--24页.
30． Schwab I., Pohl W. and Koychev, I. (2000). Learning to Recommend from Positive，Evidence, Proceedings of Intelligent User Interfaces 2000, ACM Press, pp.241-247.
31． Maloof M. and Michalski R. (2000). Selecting examples for partial memory learning. Machine Learning 41, 27-52.
32． M. Spiliopoulou. Web Usage Mining for Web Site Evaluation. Communications of the ACM, Vol. 43(8):pp. 127{134, August 2000.
33． S. Perugini, P. Lakshminarayanan, and N. Ramakrishnan. Web Mechanics: Personalizing the GAMS Cross-Index. IEEE/AIP Computing in Science and Engineering, 2000. Invited Paper.
34． N. Ramakrishnan. PIPE: Personalization is Partial Evaluation. IEEE Internet Computing, Vol. 4(6), Nov-Dec 2000. Accepted for Publication.
35． D. Riecken. Personalized Views of Personalization. Communications of the ACM, Vol. 43(8):pp. 26{28, 2000}.
36． 吴建，吴长春，个性化网站中用户分类的研究，计算机应用研究2001年，5期，18--20页.
37． 欧洁，林守勋，个性化智能信息提取中的用户发现，计算机科学，2001年，11卷，3期，112--115页.
38． 张卫丰，徐宝文，许蕾，陈振强，赵凯华，利用Agent个性化搜索结果，小型微型计算机系统，2001年，22卷，6期，724--727页
39． S. Abiteboul, P Buneman, and D. Suciu. Data on the Web: From Relations to Semistructured Data and XML. Morgan Kaufmann Publishers, 2000.
40． G. Adomavicius and A. Tuzhilin. Expert-Driven Validation of Rule-Based User Models in Personalization Applications. Data Mining and Knowledge Discovery, Vol. 5, 2001.
41． N.J. Belkin. Helping People Find What They Don't Know. Communications of the ACM, Vol. 43(8):pp. 59{61, August 2000.}.
42． S. Boykin and A. Merlino. Machine Learning of Event Segmentation for News On Demand. Comminications of the ACM, Vol. 43(2):pp. 35{41, 2000.}.