Visual analysis of large heterogeneous social networks by semantic and structural abstraction

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies     

POLYPHONET: An advanced social network extraction system from the Web

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, to detect groups of persons, and to obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.

Several studies have used search engines to extract social networks from the Web, but our research advances the following points: first, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social network mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Iterative Social Network Mining is proposed. It utilizes simple modules using Google and is characterized by scalability and relate–identify processes: identification of each entity and extraction of relations are repeated to obtain a more precise social network.     

A tie strength based model to socially-enhance applications and its enabling implementation: mySocialSphere

The growing omnipresence of the Social Web and the increasingly number of services in the Cloud have created a favourable atmosphere to develop socially-enhanced services, that is, services which are aware of the social dimension of the users to improve their experience in the Cloud. This paper introduces a model and an architecture for socially-enhanced services by mining interaction information across different Social Web sites. Most of the existing social applications require knowing who are the users socially-linked to each individual by exploring topological connections in social networks or, even, calculating the interactions network that underlies social sites. However these approaches are, on the one hand, hardly scalable when the number of users grows in the interaction network and, on the other hand, tightly coupled to the social application and so hardly reusable. The key contribution of this paper is a user-centred model whose goal is not to infer the aforementioned interaction network, but to build users’ social spheres. That is, assessing the strength and the context of the user’s ties by using signs of interaction available from social sites APIs (private messages, retweets, mentions, …) with user’s permission. To this aim, contrary to previous approaches, we take into account (i) different interaction types and contexts, (ii) the time in which interactions occur, (iii) the people involved in them and (iv) the interactions rhythms with the rest of user’s contacts. A prototype of this service has been implemented in order to, not only validate the tie strength model, but also to deploy some pilot experiences.     

Semantic information integration and question answering based on pervasive agent ontology

The traditional search engines return a large number of relative web pages rather than accurate answers. However, in a question answering system, users could use sentences in daily life to raise questions. The question answering system will analyze and comprehend these questions and return answers to users directly. Aiming at the problems in current network environment, such as low precision of question answering, imperfect expression of domain knowledge, low reuse rate and lack of reasonable theory reference models, we put forward the information integration method of semantic web based on pervasive agent ontology (SWPAO) method, which will integrate, analyze and process enormous web information and extract answers on the basis of semantics. With SWPAO method as the clue, we mainly study the method of concept extraction based on uniform semantic term mining, pervasive agent ontology construction method on account of multi-points and the answer extraction in view of semantic inference. Meanwhile, we present the structural model of the question answering system applying ontology, which adopts OWL language to describe domain knowledge base from where it infers and extracts answers by Jena inference engine, thus the precision of question answering in QA system could be improved. In the system testing, the precision has reached 86%, and recalling rate is 93%. The experiment indicates that this method is feasible and it has the significance of reference and value of further study for the question answering systems.     

eScience in the Social Cloud

Social networks offer great potential for fostering collaboration between individuals and amongst groups. This potential collaborative environment is not only applicable for recreation, but can also provide considerable value to diverse research communities. For this reason scientists are increasingly utilizing social networking concepts in projects to form groups, share information, publicize their work and communicate with their peers. This article describes two different approaches to supporting eScience, by providing scientific computing and collaboration within what we term the Social Cloud. In our first approach the social network is used as a collaborative overlay, in combination with the ad hoc creation of infrastructure composed of virtual machine clusters built from resources contributed, by the users, to the Social Cloud. Our second approach is based around the principle of volunteer computing, where the Social Cloud provides researchers with a platform to exploit social networks by reaching out to non technical users who would otherwise be unlikely to donate computational time for scientific and other research. In this article we specifically explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through the use of incentives in existing social networks.     

基于本体的Web服务发现方法研究

针对传统Web服务发现准确率低下的问题,提出一种基于本体的服务发现方法.该方法首先基于领域本体构建了Web服务描述模型,并应用OWL-S描述Web服务增强了Web服务的语义信息,然后从基本信息语义相似度、功能语义相似度等方面综合计算Web服务相似度,最后基于Web服务相似度进行请求服务与目标服务的匹配.给出了基于本体的服务发现相关算法,并设计了基于三层架构的服务发现原型系统.实验结果表明,该方法可提高Web服务发现的查全率及查准率.     

Searching and ranking method of relevant resources by user intention on the Semantic Web

As the information on the Internet dramatically increases, more and more limitations in information searching are revealed, because web pages are designed for human use by mixing content with presentation. In order to overcome these limitations, the Semantic Web, based on ontology, was introduced by W3C to bring about significant advancement in web searching. To accomplish this, the Semantic Web must provide search methods based on the different relationships between resources.In this paper, we propose a semantic association search methodology that consists of the evaluation of resources and relationships between resources, as well as the identification of relevant information based on ontology, a semantic network of resources and properties. The proposed semantic search method is based on an extended spreading activation technique. In order to evaluate the importance of a query result, we propose weighting methods for measuring properties and resources based on their specificity and generality. From this work, users can search semantically associated resources for their query, confident that the information is valuable and important. The experimental results show that our method is valid and efficient for searching and ranking semantic search results.     

SweetWiki: A semantic wiki

Everyone agrees that user interactions and social networks are among the cornerstones of “Web 2.0”. Web 2.0 applications generally run in a web browser, propose dynamic content with rich user interfaces, offer means to easily add or edit content of the web site they belong to and present social network aspects. Well-known applications that have helped spread Web 2.0 are blogs, wikis, and image/video sharing sites; they have dramatically increased sharing and participation among web users. It is possible to build knowledge using tools that can help analyze users’ behavior behind the scenes: what they do, what they know, what they want. Tools that help share this knowledge across a network, and that can reason on that knowledge, will lead to users who can better use the knowledge available, i.e., to smarter users. Wikipedia, a wildly successful example of web technology, has helped knowledge-sharing between people by letting individuals freely create and modify its content. But Wikipedia is designed for people—today's software cannot understand and reason on Wikipedia's content. In parallel, the “semantic web”, a set of technologies that help knowledge-sharing across the web between different applications, is starting to gain attraction. Researchers have only recently started working on the concept of a “semantic wiki”, mixing the advantages of the wiki and the technologies of the semantic web. In this paper we will present a state-of-the-art of semantic wikis, and we will introduce SweetWiki, an example of an application reconciling two trends of the future web: a semantically augmented web and a web of social applications where every user is an active provider as well as a consumer of information. SweetWiki makes heavy use of semantic web concepts and languages, and demonstrates how the use of such paradigms can improve navigation, search, and usability.     

How is the Semantic Web evolving? A dynamic social network perspective

Finding how the Semantic Web has evolved can help understand the status of Semantic Web community and predict the diffusion of the Semantic Web. One of the promising applications of the Semantic Web is the representation of personal profiles using Friend of a Friend (FOAF). A key characteristic of such social networks is their continual change. However, extant analyses of social networks on the Semantic Web are essentially static in that the information about the change of social networks is neglected. To address the limitations, we analyzed the dynamics of a large-scale real-world social network in this paper. Social network ties were extracted from both within and between FOAF documents. The former was based on knows relations between persons, and the latter was based on revision relations. We found that the social network evolves in a speckled fashion, which is highly distributed. The network went through rapid increase in size at an early stage and became stabilized later. By examining the changes of structural properties over time, we identified the evolution patterns of social networks on the Semantic Web and provided evidence for the growth and sustainability of the Semantic Web community.     

基于语义P2P的UDDI组合服务发现模型

随着Web服务数目的增长和对实时性需求的增加,单一集中式的UDDI服务注册中心难以满足实际需求,而基于某个领域的UDDI虽然查找迅速却难以满足所有用户的要求,于是如何组织一个逻辑上完整的服务注册中为Web服务应用的一个新研究方向.基于P2P的底部架构,将服务注册中心作为P2P网络内基本节点,提出了一个根据本体信息对服务注册节点进行语义分类查找的Web服务发现模型.在模型的基础上,提出了在P2P层支持语义的一种发现的方法.提出的Web服务发现模型既结合了集中式Web服务发现方式和分布式Web服务方式发现的优点,又创新性地在P2P发现方面加入了本体信息.     

Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection

With the rapidly growing number of available Cloud services, to fulfill the need for ordinary users to select accurate services has become a significant challenge. However, as a Cloud service environment encompasses many uncertainties that may hinder users to make sound decisions, it is highly desirable to handle fuzzy information when choosing a suitable service in an uncertain environment. In this paper, we present a novel fuzzy decision-making framework that improves the existing Cloud service selection techniques. In particular, we build a fuzzy ontology to model uncertain relationships between objects in databases for service matching, and present a novel analytic hierarchy process approach to calculate the semantic similarity between concepts. We also present a multi-criteria decision-making technique to rank Cloud services. Furthermore, we conduct extensive experiments to evaluate the performance of the fuzzy ontology-based similarity matching. The experimental results show the efficiency of the proposed method.     

基于语义主题相似度的Web教育资源查询方法

针对传统Web教育主体难以获得高可用教育资源的问题,提出了一种面向语义主题相似度的Web教育资源查询方法。该方法建立了本体概念语义网络（Ontology Concept Semantic Network,OCSN）,在此基础上,设计了基于语义主题相似度匹配的概念检索方法：在检索前主动将教育资源根据其语义和主题组织到本体概念语义网络中,然后建立一个基于语义特性的Web教育资源发现的垂直搜索引擎,并通过构造满足条件的相似度函数,将对应的语义距离映射为相似度,有效地提高了查询效率。实验结果表明此方法能够提高Web教育资源的查准率和查全率。     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

基于本体的Web服务发现模型研究

针对传统的Web服务发现机制查准率低的缺点,结合本体技术与OWL-S语义描述语言设计一个Web服务发现模型。该模型主要包括查询处理器、领域本体库和发现引擎三个模块,通过在领域内共同的语义理解基础上抽取服务查询信息中所需的功能信息以及对服务广告信息的语义描述,实现针对服务功能信息的语义匹配,从而提高服务的查准率。     

基于本体分类的Web服务合成的研究及应用

阐述了一个基于本体分类的Web服务合成模型,扩展标准的WSDL服务描述,将Web服务及其参数严格映射到本体概念分类层次,使服务描述保存了语义信息,领域本体过滤确保了合成服务的有效性,基于用户的需求描述实现了从现有Web服务中动态发现、匹配和组合服务的功能的算法,根据用户的服务质量要求,模型还能从生成的合成服务链中筛选出最佳服务组合。整个模型在一定程度上实现了服务合成的自动化。     

专业教学资源库本体模型构建

针对当前专业教学资源库的可重用性低、难以提供智能服务等问题,本文提出构建本体专业教学资源库。该方法将语义web思想与本体技术引入专业资源库构建中,并以构建《计算机网络专业教学资源库》为例,阐述了构建基于本体的专业教学资源库模型的整个流程,并选用Jena推理机,对资源语义的一致性进行检查。研究表明,对教学资源库的框架描述上升到知识级,有利于对教学资源库的共享与语义查询。     

基于本体的学前教育资源建模研究

由于传统web资源存在着先天的局限性,网络中的资源描述缺乏语义信息,往往很难进行语义层面上的推理和检索。本体是共享概念模型的明确的形式化的规范说明,通过概念之间的关系来描述概念的语义。本文通过将本体构建技术应用于学前教育学科资源的构建中,构建的学前教育学科实验本体,初步实现了对该实验本体的查询和推理。