面向主题的网页过滤机制研究
摘要
随着Internet的日益普及和迅猛发展，人们对网络的依赖程度越来越高，但Internet的开放性、平等性、无界性等特征又导致了网络的无限制滥用，大量的垃圾及敏感信息充斥于网络，如何滤除这些垃圾及敏感信息，消除网络带来的消极及负面影响已成为Internet信息服务须解决的关键问题之一。解决这一问题的最有效技术手段就是进行信息过滤。
文章在研究信息过滤一般原理及常用网页过滤技术的基础上，以需求为驱动，从功能的角度出发，提出并构建了一个基于主题的网页过滤体系，并对该体系进行了较为深入的研究，主要的研究工作和取得的创新成果有以下几个主要方面：
首先，分析了目前Internet中传播的各种信息流，根据过滤需求对网络中需要过滤的信息进行了分类，明确定义了研究的主题领域，在此基础上，设计了一个面向主题的信息过滤系统TSIFS，该系统采用分层的网页过滤策略，在信息过滤的分类方案中引入了神经网络技术，利用神经网络的学习能力及适应性弥补一般过滤机制的不足，从而可以提高了网页过滤的准确性。
其次，为了处理的方便性，通过归一化策略将Web页面包含的多类型数据变换为文本信息进行处理，在这一变换过程中结合了主题信息的过滤特征，利用主题专业词汇及人工编辑辞典完成了文本向量的表示，设计了一个新的特征词权重函数；另外还提出并设计了一种页面字符编码的判别算法。
再次，利用BP网络构建了基于神经网络的过滤信息分类模型，构造了TSIFS中的过滤引擎处理机制，并对涉及的输入向量正规化、参数选择等关键问题进行了重点讨论。
最后，文章通过仿真实验对构造的基于主题的过滤系统进行了可行性、有效性、准确性等方面的实验验证和分析。
In the wake of more popularization and swift development of Internet, the manners of people's querying information have been greatly changed and Internet has played more and more important part in our life. However, some features of Internet such as openness, equality, unboundness and etc. have also brought about the non-restricted abuse of the network: A lot of information noise and sensitive information, which can decrease the density of the useful information, flood it. Therefore how to filter these unwanted messages and eliminate negative influence has become one of the key questions in the field of Internet information service. Fortunately via information filtering, the most effective method, people can solve the problem in effect. In order to facilitate the filtering, recently techniques of machine learning have been applied to classify documents automatically in many researches.
Based on the research into general theory of information filtering and common technologies of web pages filtering, from the point of function, a topic-specific web pages filtering architecture is brought forward and constructed in this thesis, in which details have been deeply studied as well. The main work and creative results are as follows:
Firstly, analyses different information streams currently transmitted through the Internet and classifies them according to filtering requirements; then definitely defines the concerned topic-specific domains. Moreover, designs a topic-specific information filtering system(TSIFS), which adopts a layered filtering strategy and introduces Neural Network categorization into the classified scheme of information filtering. The learning capacity and adaptability of Neural Network categorization can cover the shortage of filtering, so the veracity of filtering will be increased.
Secondly, multiple types of data contained in web pages are transformed into text formatting to predigest the disposition. During this process the filtering features of topic information is considered, vectorization of text with focused vocabulary is accomplished, classification efficiency degradation is put forward and a new weighting function of key words is designed.
Thirdly, an information classifying model based on Back Propagation Network is constructed and the scheme of filtering engine including normalization of input-vector and selection of network parameters, etc. is also discussed.
Finally, emulation experiment of the proposed topic-specific filtering architecture is given and analyzed to prove out its feasibility, efficiency and veracity.
文章在研究信息过滤一般原理及常用网页过滤技术的基础上，以需求为驱动，从功能的角度出发，提出并构建了一个基于主题的网页过滤体系，并对该体系进行了较为深入的研究，主要的研究工作和取得的创新成果有以下几个主要方面：
首先，分析了目前Internet中传播的各种信息流，根据过滤需求对网络中需要过滤的信息进行了分类，明确定义了研究的主题领域，在此基础上，设计了一个面向主题的信息过滤系统TSIFS，该系统采用分层的网页过滤策略，在信息过滤的分类方案中引入了神经网络技术，利用神经网络的学习能力及适应性弥补一般过滤机制的不足，从而可以提高了网页过滤的准确性。
其次，为了处理的方便性，通过归一化策略将Web页面包含的多类型数据变换为文本信息进行处理，在这一变换过程中结合了主题信息的过滤特征，利用主题专业词汇及人工编辑辞典完成了文本向量的表示，设计了一个新的特征词权重函数；另外还提出并设计了一种页面字符编码的判别算法。
再次，利用BP网络构建了基于神经网络的过滤信息分类模型，构造了TSIFS中的过滤引擎处理机制，并对涉及的输入向量正规化、参数选择等关键问题进行了重点讨论。
最后，文章通过仿真实验对构造的基于主题的过滤系统进行了可行性、有效性、准确性等方面的实验验证和分析。
In the wake of more popularization and swift development of Internet, the manners of people's querying information have been greatly changed and Internet has played more and more important part in our life. However, some features of Internet such as openness, equality, unboundness and etc. have also brought about the non-restricted abuse of the network: A lot of information noise and sensitive information, which can decrease the density of the useful information, flood it. Therefore how to filter these unwanted messages and eliminate negative influence has become one of the key questions in the field of Internet information service. Fortunately via information filtering, the most effective method, people can solve the problem in effect. In order to facilitate the filtering, recently techniques of machine learning have been applied to classify documents automatically in many researches.
Based on the research into general theory of information filtering and common technologies of web pages filtering, from the point of function, a topic-specific web pages filtering architecture is brought forward and constructed in this thesis, in which details have been deeply studied as well. The main work and creative results are as follows:
Firstly, analyses different information streams currently transmitted through the Internet and classifies them according to filtering requirements; then definitely defines the concerned topic-specific domains. Moreover, designs a topic-specific information filtering system(TSIFS), which adopts a layered filtering strategy and introduces Neural Network categorization into the classified scheme of information filtering. The learning capacity and adaptability of Neural Network categorization can cover the shortage of filtering, so the veracity of filtering will be increased.
Secondly, multiple types of data contained in web pages are transformed into text formatting to predigest the disposition. During this process the filtering features of topic information is considered, vectorization of text with focused vocabulary is accomplished, classification efficiency degradation is put forward and a new weighting function of key words is designed.
Thirdly, an information classifying model based on Back Propagation Network is constructed and the scheme of filtering engine including normalization of input-vector and selection of network parameters, etc. is also discussed.
Finally, emulation experiment of the proposed topic-specific filtering architecture is given and analyzed to prove out its feasibility, efficiency and veracity.
引文
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[2] Jacques Cohen. Special Issue on Information Filtering[J]. Communication of the ACM, 1992,35(12).
[3] Guduvada V.N. Information Retrieval on the World Wild Web[J]. IEEE Interact Computing,1997,1(5):58-68.
[4] Robertson S.E., Beaulieu M. Research and evaluation in information retrieval[J]. Emerald: Journal of Documentation, 1997,53(1):51-57.
[5] Hjorland B., Pedersen K.N.A substantive theory of classification for information retrieval[J]. Emerald: Journal of Documentation,2005,61 (5):582-597.
[6] 黄晓斌，邱明辉．网络信息过滤系统研究[J]．情报学报，2004，23(3)：326-332．
[7] 黄晓斌，夏明春．网络信息过滤的成本效益分析[J]．情报学报，2003，21(11)：1129-1132．
[8] 李石君，李洲，余军等．基于URL过滤与内容过滤的网络净化模型[J]．计算机技术与发展，2006，16(1)：5-7．
[9] 蒙应杰，张海波等．基于角色授权的Web Service访问控制模型[J]．兰州大学学报(自然科学版)，2007，43(2)：84-88．
[10] 蒙应杰，张海波等．基于Web Service的XML成组加密研究及实现[J]．计算机应用研究，2006增刊(下)：914-915．
[11] 张旭，张新慧．数字图书馆信息过滤系统研究[J]．现代情报，2005，(7)：92-94．
[12] 张晓冬，张书杰，王万亭．信息过滤的模糊聚类模型[J]．计算机工程与应用，2002，(9)：34-36．
[13] Birger H, Karsten N.P. A substantive theory of classification for information retrieval[J]. Emerald:Journal of Documentation, 2005,61 (5):582-597.
[14] 赵俊玲．网络信息过滤在美国公共图书馆中的应用[J]．图书馆理论与实践，2004，(1)：84-85．
[15] 何军，周明天．信息网络中的信息过滤技术[J]．系统工程与电子技术，2001，23(11)：76-79．
[16] Cooper W S. Getting beyond Boole[J]. Information Processing & Management, 1988,24(3):243-248.
[17] Maron, M.E. et al. On relevance, probabilistic indexing and information retrieval[J]. Journal of the ACM,1960,7(3):216-244.
[18] Saiton G, Wong A, Yang C S. A vector space model for automatic indexing[J]. Communications of the ACM,1975, 18(5):613-620.
[19] 梅海燕．信息过滤问题的研究[J]．现代图书情报技术，2002，(2)：44-47．
[20] 程妮，崔建海，王军．国外信息过滤系统的研究综述[J]．现代图书情报技术，2005，(6)：30-38．
[21] Ding C., Chi C.H., Deng J., et al. Centralized Content Based Web Filtering and Blocking: How Far Can It Go[J]. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 1999,2:115-119.
[22] Malone T.,Grant K.,Turbak F.,et al. Intelligent information sharing systems. Communications ofACM, 1987,30(5):390-402.
[23] Maria R.Lee. Context-Dependent Information Filtering. http://www.dl.slis.tsukuba.ac.jp/ISDL97/proceedings/maeia/maria.html, 2006-03-10.
[24] Daniela Godoy, Analf a Amandi. User Profiling for Web Page Filtering[J]. IEEE Intemet Computing,2005:56-64.
[25] Shardanand U, Maes P. Social Information Filtering:Algorithms for Automating Word of Mouth. In:Conf. proc. on Human factors in computing systems(ACM CHI'95),Denver, 1995:210-217.
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