基于模式的XML文档相似度算法

提出一种基于XML模式的文档相似度算法,其中,XML模式间的相似度是XML文档聚类的重要依据,元素是XML模式的主体,模式的相似度由元素相似度组成,该算法综合考虑XML模式中元素的结构和语义信息,进一步提高计算相似度的精度。另外,该算法通过计算XML模式间的相似度,可以降低算法的复杂度,提高聚类的准确性,易于提取聚簇的通用XML模式。     

基于语义和结构的XML文档相似度的计算方法

个性化信息服务通过了解用户的兴趣爱好,为不同的用户提供不同的信息服务。XML是一种标示语言,是Web文档表示和交换的常用相关标准,因此XML文档之间相似度计算问题对于个性化推荐与信息检索非常重要,为此提出了一个计算XML文档之间的语义和结构相似度的方法XMLSim。首先,基于节点标记对之间的语义相似度和编辑距离计算节点标记对之间的相似度;在分析了路径上节点具有的偏序关系之后,将路径之间相似度问题抽象为最大相似子序列(MSS,Maximal Similar Subsequence)问题,并利用动态规划对MSS问题求解得到路径相似度NpathSim。最后,XML文档之间的相似度XMLSim通过路径集合之间的最大NPathSim的平均值得到。     

基于结点权重模型的XML片段检索策略

当用户向XML检索引擎提交查询后,返回的结果通常远远多于用户的期望,返回结果中难免有一些不相关的文档或结点.对于以文档为中心的XML文档集合,XML片段检索是根据用户的查询,从XML检索引擎返回的XML文档或结点中抽取出仅包含数百字节的片段,用户可以通过该片段判断片段所在的XML文档或结点与查询的真实相关性,以决定是否有必要进一步阅读,从而有效地提高从XML文档中获取信息的效率.该文提出了基于结点权重模型的XML片段检索策略.该策略先利用结点权重模型ATG(平均主题概括强度)对XML文档集中的标签或路径设置权重,再将该权重用于BM25模型,得到BM25NW检索模型.在利用BM25NW检索出XML结点后,对结点中定长窗口进行评分,考察其是否适合作为片段内容.最后在保证信息冗余较小的条件下,选择得分较高的窗口内容组成片段返回给用户.INEX 2011片段检索任务上的评测结果显示,基于结点权重模型ATG的XML片段检索策略具有很强的竞争力,性能明显优于其它参赛系统.     

基于XML的网页信息提取系统的研究与设计

该文提出了一种面向由XML描述的Web文档的基于用户主题信息的模式和数据抽取方法,它利用学习算法从样本文档中提取规则,然后使用匹配算法从目标文档中抽取出数据。该文使用一种改进的解析方法对XML文档进行解析,在模式抽取时使用了顺序覆盖算法从样本XML文档集中训练出模式。在数据抽取算法中,数据抽取算法从解析后的XML文档树中寻找用户所需的信息,它可以高效、准确地找到用户所需数据。     

微博中用户标签的研究

微博环境中用户可以为自己添加标签,用户所添加的标签往往被视为是对自身特点和兴趣的重要描述信息.标签中所包含的信息可能有助于建立精确的用户描述,因此在个性化推荐、专家检索、影响力分析等应用中有潜在的应用价值.首先,在大规模数据上分析和研究了微博中用户添加标签的行为及标签内容分布的特点;之后,通过主题模型对用户的微博内容进行分析,实验结果表明:用户的标签越相似,微博内容也越相似,反之亦然;随后,分析了用户关注关系与微博和标签内容之间的联系,实验结果显示,有关注关系的用户之间微博和标签的内容越相似;基于这个发现,分别使用标签内容和微博内容对真实微博数据中的用户关注关系进行预测,结果表明:基于标签的预测方法其效果明显优于基于微博内容的预测方法,显示出用户标签在描述用户兴趣方面的价值.     

基于结构分析和实体识别的信息集成

针对海量的web数据，提出了一种基于文档结构分析和实体识别的web信息提取和集成方法，利用XML强大的数据描述能力，灵活组织集成的web文档信息内容．方法首先将半结构化的HTML文档转化成具有模式结构的XML文档，然后使用实体识别的技术对不同主题区域进一步抽取出格式良好的数据，最后将得到的多数据类型的信息集成到数据库中，以支持进一步的分析和查询．实验结果证明了该方法的实用和有效性．     

基于XML文档相似性的构件聚类分析

构件聚类时,提出了一种计算基于XML描述的构件问相似度的递归算法,能有效度量构件XML描述文档包含的结构和语义信息.构造文档相似矩阵,利用遗传算法将高维样本映射到二维平面上,使用k-means算法聚类,获得全局最优的构件聚类.最后,在构件库测试模型上进行实验,实验结果表明,基于XML相似度的构件聚类算法在构件查询实践中具有可行性和有效性.     

XML文档相似性的仿真研究

XML文档相似性的计算是XML文档分类中的一个难题。文中描述了一种基于结构的方法，通过序列化模式挖掘方法，挖掘出两个文档之间的最大相似路径，从而可以通过计算最大相似的路径的节点数目和所有路径的节点数目的比值，得到两个文档之间的相似度。文章提出了一种新的最小化XML文档的方法，并且综合考虑了文档节点的语义相似度和结构相似度，从而进一步地提高了计算文档相似度的精度。实验表明，该方法有着良好的应用前景。     

面向XML文档的细粒度强制访问控制模型

XML文档存放的信息需要受到访问控制策略的保护.现有的一些面向XML文档的访问控制模型都是基于自主访问控制策略或基于角色的访问控制.高安全等级系统需要强制访问控制来保证系统内信息的安全.首先扩展了XML文档模型使其包含标签信息,并给出了扩展后的文档模型需要满足的规则.然后通过讨论XML文档上的4种操作,描述了面向XML文档的细粒度强制访问控制模型的详细内容.该模型基于XML模式技术,它的控制粒度可以达到文档中的元素或者属性.最后讨论了该模型的体系结构和一些实现机制.     

GPE:一种基于图模型的NFS有意义结果确定模型

XML非完全结构查询(NFS)允许用户利用部分XML结构信息,甚至仅仅是关键字来描述查询要求,是在缺乏完整的XML文档结构信息情况下的重要查询手段.针对图模型下的NFS有意义结果判断问题,在PE模型基础上提出一种基于图的有意义结果判断模型GPE,包括结果粒度、模式实体定义、等价模式定义和判断规则;针对标签歧义性和复杂的结构语义,GPE提出一种结合基于领域字典的语境受限的标签语义相似性和模式结构相似性的等价模式计算方法.通过在实际数据集和XML实验数据上的实验表明,GPE模型在查准率和查全率上均有较大提高.     

基于模式的XML文档关系数据库存储研究

在XML文档中可以自定义元素标签,不同XML的应用名称同名的几率非常大.命名空间的使用可以区分来自不同XML文档应用的具有相同名称的元素和属性,避免元素和属性的同名冲突.命名空间在XML文档中是一类非常重要的信息,详细介绍XML文档在映射到关系数据库时对命名空间信息的提取映射.     

Measuring the structural similarity among XML documents and DTDs

Measuring the structural similarity between an XML document and a DTD has many relevant applications that range from document classification and approximate structural queries on XML documents to selective dissemination of XML documents and document protection. The problem is harder than measuring structural similarity among documents, because a DTD can be considered as a generator of documents. Thus, the problem is to evaluate the similarity between a document and a set of documents. An effective structural similarity measure should face different requirements that range from considering the presence and absence of required elements, as well as the structure and level of the missing and extra elements to vocabulary discrepancies due to the use of synonymous or syntactically similar tags. In the paper, starting from these requirements, we provide a definition of the measure and present an algorithm for matching a document against a DTD to obtain their structural similarity. Finally, experimental results to assess the effectiveness of the approach are presented.     

利用实体语义信息的关键字查询结果多样化

近年来,可扩展标记语言（extensible markup language,XML）数据的关键字查询受到广泛关注和研究,查询结果的多样化作为提高用户查找效率的有效途径,也成为一个研究热点。已有的方法采用不同的粒度对查询结果进行多样化,但效果并不理想。为解决这个问题,提出了一种新的方法——从查询结果所描述的中心实体出发对其进行多样化。首先分析实体包含的语义信息,根据实体的特征定义实体语义相似性计算公式,然后通过衡量这些实体之间的语义相似性,对其进行语义划分,并给出查询结果所属中心实体的定位规则。基于以上工作,就可以将查询结果依据其所属中心实体的分组情况进行分类。这样得到的查询结果分组可以让用户根据每组的语义标签进行查询导航,有利于提高查找效率。实验结果证明了该方法的有效性。     

XML 关键词检索的查询理解

与纯文本文档集相比,使用语义标签标注的半结构化的XML文档集,有助于信息检索系统更好地理解待检索文档.同样,结构化查询,比如SQL,XQuery和Xpath,相对于纯关键词查询更加清晰地表达了用户的查询意图.这二者都能够帮助信息检索系统获得更好的检索精度.但关键词查询因其简单和易用性,仍被广泛使用.提出了XNodeRelation算法,以自动推断关键词查询的结构化信息(条件/目标节点类型).与已有的推断算法相比,综合了XML文档集的模式和统计信息以及查询关键词出现的上下文及其关联关系等推断用户的查询意图.大量的实验验证了该算法的有效性.     

Fast and effective clustering of XML data using structural information

This paper presents the incremental clustering algorithm, XML documents Clustering with Level Similarity (XCLS), that groups the XML documents according to structural similarity. A level structure format is introduced to represent the structure of XML documents for efficient processing. A global criterion function that measures the similarity between the new document and existing clusters is developed. It avoids the need to compute the pair-wise similarity between two individual documents and hence saves a huge amount of computing effort. XCLS is further modified to incorporate the semantic meanings of XML tags for investigating the trade-offs between accuracy and efficiency. The empirical analysis shows that the structural similarity overplays the semantic similarity in the clustering process of the structured data such as XML. The experimental analysis shows that the XCLS method is fast and accurate in clustering the heterogeneous documents by structures.     

On the use of hierarchical information in sequential mining-based XML document similarity computation

Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML trees paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.     

A query-oriented XML text summarization for mobile devices

Extensible Markup Language (XML) is a simple, flexible text format derived from SGML, which is originally designed to support large-scale electronic publishing. Nowadays XML plays a fundamental role in the exchange of a wide variety of data on the Web. As XML allows designers to create their own customized tags, enables the definition, transmission, validation, and interpretation of data between applications, devices and organizations, lots of works in soft computing employ XML to take control and responsibility for the information, such as fuzzy markup language, and accordingly there are lots of XML-based data or documents. However, most of mobile and interactive ubiquitous multimedia devices have restricted hardware such as CPU, memory, and display screen. So, it is essential to compress an XML document/element collection to a brief summary before it is delivered to the user according to his/her information need. Query-oriented XML text summarization aims to provide users a brief and readable substitution of the original retrieved documents/elements according to the user’s query, which can relieve users’ reading burden effectively. We propose a query-oriented XML summarization system QXMLSum, which extracts sentences and combines them as a summary based on three kinds of features: user’s queries, the content of XML documents/elements, and the structure of XML documents/elements. Experiments on the IEEE-CS datasets used in Initiative for the Evaluation of XML Retrieval show that the query-oriented XML summary generated by QXMLSum is competitive.     

A multi-user perspective for personalized email communities

Email classification and prioritization expert systems have the potential to automatically group emails and users as communities based on their communication patterns, which is one of the most tedious tasks. The exchange of emails among users along with the time and content information determine the pattern of communication. The intelligent systems extract these patterns from an email corpus of single or all users and are limited to statistical analysis. However, the email information revealed in those methods is either constricted or widespread, i.e. single or all users respectively, which limits the usability of the resultant communities. In contrast to extreme views of the email information, we relax the aforementioned restrictions by considering a subset of all users as multi-user information in an incremental way to extend the personalization concept. Accordingly, we propose a multi-user personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We construct a social graph using multi-user personalized emails. Subsequently, the social graph is uniquely leveraged with expedient attributes, such as semantics, to identify user communities through collaborative similarity measure. The multi-user personalized communities, which are evaluated through different quality measures, enable the email systems to filter spam or malicious emails and suggest contacts while composing emails. The experimental results over two randomly selected users from email network, as constrained information, unveil partial interaction among 80% email users with 14% search space reduction where we notice 25% improvement in the clustering coefficient.     

抽取XML文档到关系数据库

XML在关系数据库中的存储问题是XML研究领域中的一个重要问题。在总结多种映射方法的基础上,提出了一种方法将多个相似的XML文档进行解析,根据映射关系,生成各自的关系模式,并分析归纳出一个集成的关系模式,然后创建一个关系数据库,并在映射关系的基础上提取并存储XML文档数据到关系数据库。此方法以较为简洁的结构保存了XML文档的数据信息,其最大的特点就是不用考虑文档的模式信息（DTD,XML Schema）。并通过一个具体的实验结果来说明这种方法的有效性。