Collective knowledge systems: Where the Social Web meets the Semantic Web

What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the “collective intelligence” of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.     

Automatic integration of Web search interfaces with WISE-Integrator

An increasing number of databases are becoming Web accessible through form-based search interfaces, and many of these sources are database-driven e-commerce sites. It is a daunting task for users to access numerous Web sites individually to get the desired information. Hence, providing a unified access to multiple e-commerce search engines selling similar products is of great importance in allowing users to search and compare products from multiple sites with ease. One key task for providing such a capability is to integrate the Web search interfaces of these e-commerce search engines so that user queries can be submitted against the integrated interface. Currently, integrating such search interfaces is carried out either manually or semiautomatically, which is inefficient and difficult to maintain. In this paper, we present WISE-Integrator - a tool that performs automatic integration of Web Interfaces of Search Engines. WISE-Integrator explores a rich set of special metainformation that exists in Web search interfaces and uses the information to identify matching attributes from different search interfaces for integration. It also resolves domain differences of matching attributes. In this paper, we also discuss how to automatically extract information from search interfaces that is needed by WISE-Integrator to perform automatic interface integration. Our experimental results, based on 143 real-world search interfaces in four different domains, indicate that WISE-Integrator can achieve high attribute matching accuracy and can produce high-quality integrated search interfaces without human interactions.Received: 2 January 2004, Accepted: 25 March 2004, Published online: 12 August 2004Edited by: M. Carey     

Piazza: mediation and integration infrastructure for Semantic Web data

The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to provide interoperability—both between sites with different terminologies and with existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world’s data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies.This paper describes the Piazza system, which addresses these challenges. Piazza offers a language for mediating between data sources on the Semantic Web, and it maps both the domain structure and document structure. Piazza also enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies. Mappings in Piazza are provided at a local scale between small sets of nodes, and our query answering algorithm is able to chain sets mappings together to obtain relevant data from across the Piazza network. We also describe an implemented scenario in Piazza and the lessons we learned from it.     

基于树合并的Deep Web查询接口集成

随着在线数据库应用的流行,整个互联网已经被迅速"深化".对于某一特定领域的deep Web,不同的站点往往会提供不同查询能力的查询接口.为了能够集成同一领域内的各个数据源,首先要解决的问题就是查询接口的集成.但是面对数量众多和结构各异的查询接口,基于人工或规则的集成方案显然无法适用.针对这种状况,提出了基于树合并的查询接口全自动集成方法.树结构可以充分体现查询接口属性之间的结构关系,通过树的合并来实现查询接口的集成.该方法不但能保持各个原始接口的结构特征,还具备良好的扩展性,以适应不断频繁更新的查询接口.最后通过多个领域的测试数据证明了本方法的有效性.     

The two cultures: Mashing up Web 2.0 and the Semantic Web

A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s strengths. We believe that future Web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.     

基于本体的Deep Web查询接口分类

目前对于分类问题,主要工作集中在文本或Web文档的分类研究,而很少有对deep Web查询接口的分类研究.deep Web源包括查询接口和查询结果,大量的deep Web源的存在,对它们查询接口的分类是通向deep Web分类集成和检索的关键步骤.本分提出一种deep Web本体分类方法,包括:分类本体的概念模型和由此产生的deep Web空间向量模型(VSM).试验表明,这种分类方法具有良好的分类效果,平均准确率达到91.6%,平均查全率达到92.4%.     

Using the Semantic Web for linking and reusing data across Web 2.0 communities

Large volumes of content (bookmarks, reviews, videos, etc.) are currently being created on the “Social Web”, i.e. on Web 2.0 community sites, and this content is being annotated and commented upon. The ability to view an individual's entire contribution to the Social Web would be an interesting and valuable service, particularly important as social networks are often being formed through created content and things that people have in common (“object-centred sociality”). SIOC is a Semantic Web research project that aims to describe online communities on the Social Web. This paper describes how SIOC and the Semantic Web can enable linking and reuse scenarios of data from Web 2.0 community sites, and introduces a SIOC Types module to further specify the type of content items and act as a “glue” between user posts and the content items created and annotated by users.     

Metcalfe's law, Web 2.0, and the Semantic Web

The power of the Web is enhanced through the network effect produced as resources link to each other with the value determined by Metcalfe's law. In Web 2.0 applications, much of that effect is delivered through social linkages realized via social networks online. Unfortunately, the associated semantics for Web 2.0 applications, delivered through tagging, is generally minimally hierarchical and sparsely linked. The Semantic Web suffers from the opposite problem. Semantic information, delivered through ontologies of varying amounts of expressivity, is linked to other terms (within or between resources) creating a link space in the semantic realm. However, the use of the Semantic Web has yet to fully realize the social schemes that provide the network of users. In this article, we discuss putting these together, with linked semantics coupled to linked social networks, to deliver a much greater effect.     

Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

SemWebDL: A privacy-preserving Semantic Web infrastructure for digital libraries

Recent advances in digital libraries have been closely intertwined with advances in Internet technologies. With the advent of the Web, digital libraries have been able to reach constituencies previously unanticipated. Because of the wide deployability of Web-accessible digital libraries, the potential for privacy violations has also grown tremendously. The much touted Semantic Web, with its agent, service, and ontology technologies, is slated to take the Web to another qualitative level in advances. Unfortunately, these advances may also open doors for privacy violations in ways never seen before. We propose a Semantic Web infrastructure, called SemWebDL, that enables the dynamic composition of disparate and autonomous digital libraries while preserving user privacy. In the proposed infrastructure, users will be able to pose more qualitative queries that may require the ad hoc collaboration of multiple digital libraries. In addition to the Semantic Web-based infrastructure, the quality of the response would rest on extraneous information in the form of a profile. We introduce the concept of communities to enable subject-based cooperation and search speedup. Further, digital libraries heterogeneity and autonomy are transcended by a layered Web-service-based infrastructure. Semantic Web-based digital library providers would advertise to Web services, which in turn are organized in communities accessed by users. For the purpose of privacy preservation, we devise a three-tier privacy model consisting of user privacy, Web service privacy, and digital library privacy that offers autonomy of perspectives for privacy definition and violation. We propose an approach that seamlessly interoperates with potentially conflicting privacy definitions and policies at the different levels of the Semantic Web-based infrastructure. A key aspect in the approach is the use of reputations for outsourcing Web services. A Web service reputation is associated with its behavior with regard to privacy preservation. We developed a technique that uses attribute ontologies and information flow difference to collect, evaluate, and disseminate the reputation of Web services.     

Deep Web数据集成研究综述

随着World Wide Web(WWW)的飞速发展,Deep Web中蕴含了海量的可供访问的信息,并且还在迅速地增长.这些信息要通过查询接口在线访问其后端的Web数据库.尽管丰富的信息蕴藏在Deep Web中,由于Deep Web数据的异构性和动态性,有效地把这些信息加以利用是一件十分挑战性的工作.Deep Web数据集成至今仍然是一个新兴的研究领域,其中包含有若干需要解决的问题.总体来看,在该领域已经开展了大量的研究工作,但各个方面发展并不均衡.文中提出了一个Deep Web数据集成的系统架构,依据这个系统架构对Deep Web数据集成领域中若干关键研究问题的现状进行了回顾总结,并对未来的研究发展方向作了较为深入的探讨分析.     

WritingResearchWriting: The Semantic Web and the Future of the Research Project

In this essay, I explore one advancement in particular—the Semantic Web—and discuss ways in which it stands to change students’ interaction with information in digital spaces, and how new forms of interaction could impact the teaching of writing. I go back to the earliest days of the Web in order to remind readers how information retrieval has evolved in the digital age. I also provide a general discussion of the vision, current state, and possible futures of the Semantic Web, and I focus on how the Semantic Web might alter the research process and, more importantly, the research-writing relationship. I also offer suggestions for teaching composition in advance of and for the Semantic Web. Although the Semantic Web is still several years off, I contend that research and writing will merge into a single process, thus presenting new opportunities for the teaching of writing then and, perhaps more importantly, now. These opportunities include helping students to identify the challenges of information retrieval and overload, to see research as more about establishing links and connections between information sources and less about finding the “right” sources, and to write for the Semantic Web—and, in doing so, teach students that researching and writing are inseparable.     

基于语义的Web挖掘

基于语义的Web挖掘是使用从现有Web数据中抽取的语义或直接使用Web数据中已有的语义结构来帮助Web挖掘。它有效地结合了语义网和Web挖掘两个领域的研究成果,既可以通过开发新的语义结构来帮助Web挖掘,又可以利用挖掘结果促进语义网的创建。本文介绍了基于语义的Web挖掘的基本思想和研究现状,分析了语义网和Web挖掘相结合的优势,并详细论述了国际上关于利用数据挖掘技术创建语义网,利用语义挖掘Web数据和直接挖掘语义网三个方面的研究工作。     

Wrapping of Web Sources with restricted Query Interfaces by Query Tunneling

Information sources in the World Wide Web usually offer two different schemes to their users, an Interface Schema which the user can query and a Result Schema which the user can browse. Often the Interface Schema is more restricted than the Result Schema, moreover many sources offer keyword-search interfaces only. Thus query capabilities of such sources are very small and a useful integration into a mediator-based information system using query capabilities is almost impossible. We propose the Query Tunnelling architecture for the wrapping of these restricted web sources. Wrapping of sources by Query Tunneling hides restrictive query interfaces and makes such sources fully queryable based on their result schema. The process of Query Tunneling is divided into two main steps, Query Relaxation to make a higher order query suitable to a restricted interface and Result Restriction in order to filter the results using the original query.     

Web 2.0 Injection Infection Vulnerability Class

ABSTRACT

Web 2.0 defines a changing trend in the use of World Wide Web application development and web design technology. Web 2.0 design concepts have led to the evolution of a web culture that has allowed social-networking and ease of design use of non-secure component applications to enter the business domain of the enterprise. These Web 2.0 component applications are then commingled with other business legacy applications including databases. This article focuses on the taxonomy of the injection infection class of vulnerabilities associated with Web 2.0 application security issues.     

Web information fusion: A review of the state of the art

In this paper, we introduce and overview advances in the field of Web information fusion and integration. As it is such a broad and diverse topic that is researched in many different fields, we choose to provide a unified view by focusing on selected survey articles that extensively cover earlier research contributions. Given the important role that ontologies are playing in Web information fusion and the emergence and fast development of the Semantic Web and Web 3.0 technologies, a separate section is devoted to the topic of ontology research and the Semantic Web. Then, in the section on Web-based support systems, several applications that are enabled as the result of advances in Web information fusion are discussed.     

Learning to match ontologies on the Semantic Web

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible on the Web scale. Hence the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies and describe experiments that show the promise of the approach.Received: 16 December 2002, Accepted: 16 April 2003, Published online: 17 September 2003Edited by: Edited by B.V. Atluri, A. Joshi, and Y. Yesha     

Adaptive systems in the era of the semantic and social web, a survey

In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.     

Bridging the semantic Web and Web 2.0 with Representational State Transfer (REST)

Semantic Web technologies must integrate with Web 2.0 services for both to leverage each others strengths. We argue that the REST-based design methodologies [R.T. Fielding, R.N. Taylor, Principled design of the modern web architecture, ACM Trans. Internet Technol. (TOIT) 2 (2) (2002) 115–150] of the web present the ideal mechanism through which to align the publication of semantic data with the existing web architecture. We present the design and implementation of two solutions that combine REST-based design and RDF [D. Beckett (Ed.), RDF/XML Syntax Specification (Revised), W3C Recommendation, February 10, 2004] data access: one solution for integrating existing web services and one server-side solution for creating RDF REST services. Both of these solutions enable SPARQL [E. Prud’hommeaux, A. Seaborne (Eds.), SPARQL Query Language for RDF, W3C Working Draft, March 26, 2007] to be a unifying data access layer for aligning the Semantic Web and Web 2.0.