OWL-QL—a language for deductive query answering on the Semantic Web

This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL query language (OWL-QL) as a candidate standard language and protocol for query–answering dialogues among Semantic Web computational agents using knowledge represented in the W3Cs ontology web language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query–answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute.     

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.     

Semantic Web-based geospatial knowledge transformation

Earth and space science research and applications typically involve collecting and analyzing large volumes of geospatial data much of which is derived from other existing data by applying a scientific workflow. Such a step-by-step process can be viewed as a process of geospatial knowledge transformation, which often involves hypotheses, inferences and integrations to derive user-specific data products from the knowledge of domain experts. Our research is focused on reducing the transformation effort by providing component inference and integration tools. The Semantic Web envisions a new standardized information infrastructure to enable interoperable machine-to-machine interactions and automatic or semi-automatic service chaining for deriving knowledge over networks. This paper describes a generic framework and implementation of how the Semantic Web proceeds through the life cycle of geospatial knowledge transformation, from geospatial modeling (knowledge formalization), through model instantiation (service chain) to model execution (data product). Our approach relies on semantic integrations. A number of ontologies used to capture domain knowledge are introduced in this paper as the basis of knowledge bases for describing and reasoning geospatial data and services. Also, a semantically enabled geospatial catalog service is described to enable more effective discovery, automation and integration of geospatial data and services.     

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.     

基于本体的网络数据工作平台NetData

近年来,网格、语义网络等新技术迅速发展并日臻成熟。互联网发展焦点开始从信息的发布和互联转向知识的交互框架。随着语义网络迅速发展,世界各地各个领域的研究爱好者组成虚拟社区,对同一领域的知识信息一起协作研究。其中,对数据的整理、保存、检索、分析是实现语义网络远景的基础工作。本文为了帮助研究社区的研究人员更有效方便地加入社区的研究,利用长期帮助中国中医研究院建设专业结构化数据库群的项目中所取得的经验,结合了语义网络和数据库网格的研究,设计并初步实现了基于本体的网络数据工作平台。     

Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation

We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a ‘virtuous cycle’ between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous ‘Cranfield’ evaluation of Semantic Web search.     

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Web标准的信息港研究

数字城市是以Intemet为平台的,实现空间信息互操作为核心的城市信息管理系统,却受到跨行业应用的基础数据不同组织形式制约。数据的组织与表现松耦合关系是实现空间信息互操作的重要环节,也是适应语义网络计算标准的要求。Web标准将页面行为与表现分开,为信息港实现资源的一体化管理,满足用户按需的3A（Anybody,Anytime,Anywhere）视化服务,达到开放、共享根本目标提供了架构和实现的途径,同时隐含了制约信息港门户性能的关键问题和解决途径。     

Mining the Semantic Web

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.     

ON KNOWLEDGE GRID AND GRID INTELLIGENCE: A SURVEY

The next generation Web Intelligence (WI) aims at enabling users to go beyond the existing online information search and knowledge queries functionalities and to gain, from the Web, practical wisdom for problem solving. To support such a Wisdom Web, we envision that a grid-like computing infrastructure with intelligent service agencies is needed, where these agencies can interact, self-organize, learn, and evolve their course of actions, identities, and interrelationships for new knowledge creation, as well as scientific and social evolution. In this paper, we first provide an overview of recent development in WI and Semantic/Knowledge Grid. Then, the fundamental capabilities of the Wisdom Web as well as the conceptual architecture of an intelligent Grid for supporting it are described. Technical challenges for realizing Grid Intelligence are highlighted and the recent advancements in related research areas are reviewed.     

RDF数据浏览的研究综述

随着语义网的快速发展,目前Web上语义网数据已经达到相当的规模,成为重要的信息和知识来源.因此,RDF数据浏览的研究开始得到广泛关注.通过对比传统Web信息浏览和RDF数据浏览两个问题,指出RDF数据浏览的5个重要问题:确定浏览子图的模式、数据的收集、大规模数据的处理、数据的组织方式以及数据的呈现方式.基于这些挑战,我们调研了多个系统和不同的解决方案.最后,总结了目前的研究现况,讨论存在的挑战,并提出未来的研究方向.     

Aligning Semantic Web applications with network access controls

Access controls for Semantic Web applications are commonly considered at the level of the application-domain and do not necessarily consider the security controls of the underlying infrastructure to any great extent. Low-level network access controls such as firewalls and proxies are considered part of providing a generic network infrastructure that hosts a variety of Semantic Web applications and is independent of the application-level access control services. For example, it is unusual to include firewall policy rules in an application policy that constrain the kinds of application information different principals may access. As a consequence, an improperly configured infrastructure may unintentionally hinder the normal operation of a Semantic Web application. Simply opening a firewall for HTTP and HTTPS services does not necessarily result in a proper configuration. Taking an ontology-based approach, this paper considers how a firewall configuration should be analyzed with respect to the Semantic Web application(s) that it hosts.     

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.     

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.     

The design and evaluation of accessibility on web navigation

Web sites have been deployed to create and sustain business competitiveness in a trend of emerging Web technologies and growing e-commerce. One critical success factor of e-commerce is the ability to allow information to be retrieved from a Web site in an efficient and effective manner. Such ability, being determined by both the Web site structure and the Web page organization, can be measured in terms of accessibility and popularity of Web pages. The relationship between accessibility and popularity of web pages is dynamic in nature and can be analyzed to enhance a Web design. Having observed the lack of means to measure information retrieval of a Web site, this paper purports to introduce a guideline to evaluate Web page accessibility based on several structural-based accessibility models where an innovative accessibility–popularity (A–P) analysis is deployed to measure and, thereby, to modify a Web structure. Both push (i.e. demand driven) strategies and pull (i.e. design driven) strategies are incorporated into such guideline. Further, accessibility models are analyzed and compared in order to identify appropriate applications for each model. The paper is concluded by a summary of future directions of the accessibility models.     

Mass argumentation and the semantic web

The World Wide Web (WWW) can be seen as an ideal platform for enhancing argumentative expression and communication, due to its ubiquity and openness. Much argumentation takes place on personal blogs and on unstructured or semi-structured discussion forums. Recently, an increasing number of Web 2.0 applications provide specific support for large-scale socially-contributed argumentative content. When compared with traditional methods of Web discourse, these tools enable better visualisation, navigation and analysis of the ‘state of the debate’ by participants and, potentially, by automated tools. In this paper, I outline some potential benefits of Semantic Web techniques in supporting mass-scale, socially-contributed argument tagging. I also present some recent research in this direction.     

Explaining answers from the Semantic Web: the Inference Web approach

The Semantic Web lacks support for explaining answers from web applications. When applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up versus derived. Many users also do not know how implicit answers were derived. The Inference Web (IW) aims to take opaque query answers and make the answers more transparent by providing infrastructure for presenting and managing explanations. The explanations include information concerning where answers came from (knowledge provenance) and how they were derived (or retrieved). In this article we describe an infrastructure for IW explanations. The infrastructure includes: IWBase — an extensible web-based registry containing details about information sources, reasoners, languages, and rewrite rules; PML — the Proof Markup Language specification and API used for encoding portable proofs; IW browser — a tool supporting navigation and presentations of proofs and their explanations; and a new explanation dialogue component. Source information in the IWBase is used to convey knowledge provenance. Representation and reasoning language axioms and rewrite rules in the IWBase are used to support proofs, proof combination, and Semantic Web agent interoperability. The Inference Web is in use by four Semantic Web agents, three of them using embedded reasoning engines fully registered in the IW. Inference Web also provides explanation infrastructure for a number of DARPA and ARDA projects.