基于服务分组的语义Web服务发现研究

提出一种多本体环境下的服务组织和发现方法。服务注册时,根据服务本体合理组织服务,即计算服务描述与本体概念组间的相似度,对注册服务进行分组。服务发现时,不需要将服务请求与所有注册的服务顺序地、一个一个地进行匹配判断。它首先根据服务请求的服务领域,缩小服务查找空间,然后根据服务请求概念集与本体概念组之间的相似度,进一步减小服务查找空间,进而提高了服务的发现效率。同时设定服务请求概念集与本体概念组之间的相似度阀值ξ的大小,来保证服务的查全率;而服务查找空间确定后,按照服务功能参数匹配筛选满足服务请求功能需求的服务,最后参考服务质量进行选择服务,提高服务查准率。     

语义Web的“语义”与“语义Web”的语义

语义是人脑中的概念与外指意义。目前的计算机环境还无法模拟外指意义,界定“语义≈概念”就成为现实可行的选择。语义就是内置于计算机的Ontology中的概念。语义Web是一种抽象的信息集合,其中的信息由URI定位、用XML表示、以多种方式传输。Ontology的重要性体现在概念之间的关联,即从任一个概念出发均可到达若干其它概念及其属性,从而实现语义推理。构建语义Web的基础工作有二：信息的内容全部用“概念”进行标记;计算机内置“概念系统”即Ontology。     

Embedding Gestalt laws in Markov random fields

The goal of this paper is to study a mathematical framework of 2D object shape modeling and learning for middle level vision problems, such as image segmentation and perceptual organization. For this purpose, we pursue generic shape models which characterize the most common features of 2D object shapes. In this paper, shape models are learned from observed natural shapes based on a minimax entropy learning theory. The learned shape models are Gibbs distributions defined on Markov random fields (MRFs). The neighborhood structures of these MRFs correspond to Gestalt laws-colinearity, cocircularity, proximity, parallelism, and symmetry. Thus, both contour-based and region-based features are accounted for. Stochastic Markov chain Monte Carlo (MCMC) algorithms are proposed for learning and model verification. Furthermore, this paper provides a quantitative measure for the so-called nonaccidental statistics and, thus, justifies some empirical observations of Gestalt psychology by information theory. Our experiments also demonstrate that global shape properties can arise from interactions of local features     

Identifying incompleteness in privacy policy goals using semantic frames

Requirements Engineering - Companies that collect personal information online often maintain privacy policies that are required to accurately reflect their data practices and privacy goals. To be...     

基于语义关联的中文网页主题词提取方法研究*

提出了一种基于语义关联的中文网页主题词提取方法,首先借助滑动窗口和“知网”计算词语间的语义相似度,形成候选名词对集合;然后基于该集合生成无向图表示词语间的语义联系,并通过该无向图对主题词权重进行建模;最后选取权值较高的名词作为主题词。实验结果表明,相比未建立语义关联的主题词提取方法,本方法在查准率、召回率和F1测度值上均有一定的提高,当提取主题词个数为7时,本方法召回率和F1测度值达到最大值,且分别较传统方法最大值提高了12.5%和9.53%。     

语义网中基于本体的语义信任计算研究

根据语义网中信息拥有的属性特征以及节点之间交互方式,在将信息语义信任度定义为传播节点信任度和传播信息内容信任度基础上,提出了一种信息语义信任度计算算法。该算法由三部分组成：一是每个节点通过自身本体对知识的形式化说明计算信息内容信任度;二是通过节点之间信息交互计算节点信任度;三是在前两种计算基础上综合计算得到最终信息语义可信任度。进而提出一种信息传播动态信任控制机制。最后进行了仿真实验,实验结果表明,本论文研究成果能够使信息在语义信任计算基础上进行有效传播。     

基于语义Web技术的属性访问控制模型*

基于语义Web技术和扩展访问控制标记语言(XACML),提出了一种具有语义的属性访问控制模型.该模型利用XACML,可实现基于属性的访问控制;而利用语义Web技术,可降低属性策略定义和维护的复杂性,同时也可保护用户的敏感属性.     

Identifying Extract Class refactoring opportunities using structural and semantic cohesion measures

Approaches for improving class cohesion identify refactoring opportunities using metrics that capture structural relationships between the methods of a class, e.g., attribute references. Semantic metrics, e.g., C3 metric, have also been proposed to measure class cohesion, as they seem to complement structural metrics. However, until now semantic relationships between methods have not been used to identify refactoring opportunities. In this paper we propose an Extract Class refactoring method based on graph theory that exploits structural and semantic relationships between methods. The empirical evaluation of the proposed approach highlighted the benefits provided by the combination of semantic and structural measures and the potential usefulness of the proposed method as a feature for software development environments.     

A framework for semantic grouping in P2P databases

Sharing of structured data in decentralized environments is a challenging problem, especially in the absence of a global schema. Social network structures map network links to semantic relations between participants in order to assist in efficient resource discovery and information exchange. In this work, we propose a scheme that automates the process of creating schema synopses from semantic clusters of peers which own autonomous relational databases. The resulting mediated schemas can be used as global interfaces for relevant queries. Active nodes are able to initiate the group schema creation process, which produces a mediated schema representative of nodes with similar semantics. Group schemas are then propagated in the overlay and used as a single interface for relevant queries. This increases both the quality and the quantity of the retrieved answers and allows for fast discovery of interest groups by joining peers. As our experimental evaluations show, this method increases both the quality and the quantity of the retrieved answers and allows for faster discovery of semantic groups by joining peers.     

The semantic Web in one day

To gain momentum, technologies for building private semantic Webs or parts of the World Wide semantic Web must become a commodity and easy to integrate. To determine just how far semantic Web technologies have come, we wanted to create a snapshot of what you could do by applying and assembling existing semantic Web technologies - in one day.     

A semantic based Web page classification strategy using multi-layered domain ontology

World Wide Web is a continuously growing giant, and within the next few years, Web contents will surely increase tremendously. Hence, there is a great requirement to have algorithms that could accurately classify Web pages. Automatic Web page classification is significantly different from traditional text classification because of the presence of additional information, provided by the HTML structure. Recently, several techniques have been arisen from combinations of artificial intelligence and statistical approaches. However, it is not a simple matter to find an optimal classification technique for Web pages. This paper introduces a novel strategy for vertical Web page classification, which is called Classification using Multi-layered Domain Ontology (CMDO). It employs several Web mining techniques, and depends mainly on proposed multi-layered domain ontology. In order to promote the classification accuracy, CMDO implies a distiller to reject pages related to other domains. CMDO also employs a novel classification technique, which is called Graph Based Classification (GBC). The proposed GBC has pioneering features that other techniques do not have, such as outlier rejection and pruning. Experimental results have shown that CMDO outperforms recent techniques as it introduces better precision, recall, and classification accuracy.     

语义网在文本分类中的应用

随着因特网上信息的大量增加,如果不依靠自动分类而完全通过手工进行文本分类,文本分类是不可能完成的。因此,文本自动分类成为一个重要的研究领域。首先介绍语义网及其相关技术,最后介绍基于本体技术的语义网的自动分类器。     

Two-stage ELM for phishing Web pages detection using hybrid features

Increasing high volume phishing attacks are being encountered every day due to attackers’ high financial returns. Recently, there has been significant interest in applying machine learning for phishing Web pages detection. Different from literatures, this paper introduces predicted labels of textual contents to be part of the features and proposes a novel framework for phishing Web pages detection using hybrid features consisting of URL-based, Web-based, rule-based and textual content-based features. We achieve this framework by developing an efficient two-stage extreme learning machine (ELM). The first stage is to construct classification models on textual contents of Web pages using ELM. In particular, we take Optical Character Recognition (OCR) as an assistant tool to extract textual contents from image format Web pages in this stage. In the second stage, a classification model on hybrid features is developed by using a linear combination model-based ensemble ELMs (LC-ELMs), with the weights calculated by the generalized inverse. Experimental results indicate the proposed framework is promising for detecting phishing Web pages.     

Rule identification using ontology while acquiring rules from Web pages

As research on the Semantic Web actively progresses, a more intelligent Web environment is expected in various domains including rule-based systems and intelligent agents. However, rule acquisition is still a bottleneck in the utilization of rule-based systems. To extract rules from Web pages, the framework of eXtensible Rule Markup Language (XRML) has been developed. XRML allows the identification of rules from Web pages and generates rules automatically. However, the knowledge engineer's burden is still high because rule identification requires considerable manual work. In order to reduce the knowledge engineer's burden, we proposed an ontology-based methodology of enhanced rule identification. First, we have designed an ontology OntoRule for automated rule identification. Also, we proposed a procedure of rule identification using OntoRule. Lastly, we showed the performance of our approach with an experiment.     

AnnoTerra: building an integrated earth science resource using semantic Web technologies

We discuss about AnnoTerra, a prototype knowledge-based system that uses semantic Web technologies to present enhanced earth science news feeds by making focused semantic searches on NASA resource catalogs using earth science concepts and relationships.     

Determining the titles of Web pages using anchor text and link analysis

Determining the titles of Web pages is an important element in characterizing and categorizing the vast number of Web pages. There are a few approaches to automatically determining the titles of Web pages. As an R&D project for Naver, the operator of Naver (Korea’s largest portal site), we developed a new method that makes use of anchor texts and analysis of links among Web pages. In this paper, we describe our method and show experiment results of its performance.     

基于改进语义熵最大化模型的服务发现算法

语义匹配与发现是语义Web的核心内容之一。提出一种新的基于语义熵的服务发现与匹配算法。该算法通过引入语义熵的概念,把最大熵原理运用到语义识别与匹配领域,并对传统的熵最大模型进行了经验修正。通过实验对比分析,可以看出修正后的最大熵模型在服务发现计算方面具有较好的性能,该模型在一个真实的中文语义Web的语义识别项目中得到了应用,也体现出较好的精确度和性能。     

Modeling with the semantic Web in the geosciences

Earth system science deals with complex systems that pose many significant representation challenges. Ontologies as knowledge repositories have been developed to support the primary goal of sharing knowledge in a manner that aids understanding. DARPA is currently developing an extension to OWL called SWRL (semantic Web rule language), which lets to express some aspects of rules and process behaviors. The semantic Web facilitates researchers' collaboration and model components automated discovery and use. Spatial data plays a key role in modeling the earth system as the input to models and as a measure against which results are validated.     

改进的语义Web服务匹配方法

针对目前语义Web服务匹配方法中缺少对服务行为约束的考虑及传统推理匹配的结果中同一级别不能再区分的缺点,设计了一种带服务行为约束的基于推理和语义相似度计算的匹配方法。方法分为三步：第一步进行服务行为约束兼容性检测;第二步对待匹配的两个服务进行推理;第三步在第二步基础上进行本体概念间的语义相似度计算,得出待匹配服务的精确匹配度。这样每个服务都有了一个精确的匹配度值,避免了传统方法中多个服务因拥有同一个匹配度等级而不能再区分的不足。实验结果表明,该方法较之传统的OWL-S/UDDI匹配方法在查准率和查全率方面得到了很大改进。