RSS: A framework enabling ranked search on the semantic web

The semantic web not only contains resources but also includes the heterogeneous relationships among them, which is sharply distinguished from the current web. As the growth of the semantic web, specialized search techniques are of significance. In this paper, we present RSS—a framework for enabling ranked semantic search on the semantic web. In this framework, the heterogeneity of relationships is fully exploited to determine the global importance of resources. In addition, the search results can be greatly expanded with entities most semantically related to the query, thus able to provide users with properly ordered semantic search results by combining global ranking values and the relevance between the resources and the query. The proposed semantic search model which supports inference is very different from traditional keyword-based search methods. Moreover, RSS also distinguishes from many current methods of accessing the semantic web data in that it applies novel ranking strategies to prevent returning search results in disorder. The experimental results show that the framework is feasible and can produce better ordering of semantic search results than directly applying the standard PageRank algorithm on the semantic web.     

基于语义目录的个性化检索模型研究

针对传统信息检索方法的缺点提出了基于语义目录的个性化检索模型,说明了其原理、工作流程及关键技术。与传统的信息检索方法相比,该模型不仅在查准率和查全率方面都有所提高,还能根据用户需要完成个性化的信息检索。     

语义检索

语义检索是信息检索的发展趋势。本文介绍了三个语义检索系统－UMLS、Semantic web以及WordNet的结构、特点和原理。     

Computing semantic similarity based on novel models of semantic representation using Wikipedia

Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.     

基于语义Wed的企业信息集成

本文在分析当前各种企业信息集成系统的特点和不足的基础之上，提出基于语义Wed的企业信息集成的思想，介绍了语义Wed的定义、关键技术、结构，总结了基于语义Wed的企业信息集成的优势，最后给出了实现思想和框架。     

基于Ontology的异构信息集成研究

语义异构问题是当前信息集成领域的研究热点。本文讨论了信息集成的方法及其存在的一些问题,介绍了本体的概念、本体在信息集成中的角色,提出了一种基于本体的信息集成架构,旨在实现对语义异构信息源的集成。     

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.     

A review of ranking approaches for semantic search on Web

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.     

Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study

This paper proposes collaborative filtering as a means to predict semantic preferences by combining information on social ties with information on links between actors and semantics. First, the authors present an overview of the most relevant collaborative filtering approaches, showing how they work and how they differ. They then compare three different collaborative filtering algorithms using articles published by New York Times journalists from 2003 to 2005 to predict preferences, where preferences refer to journalists’ inclination to use certain words in their writing. Results show that while preference profile similarities in an actor’s neighbourhood are a good predictor of her semantic preferences, information on her social network adds little to prediction accuracy.     

基于本体的信息过滤系统的研究与实现

研究的是在特定领域提取概念本体,以本题库作为知识管理的基础,再通过语义网OWL技术和本体技术对知识单元进行集成,通过构建敏感信息本题库,以本体和分布式技术为基础,通过语义推理来研究信息过滤系统。     

网络环境下的知识挖掘

当前网络信息大爆炸，要从大量信息中获得所需知识就需要运用知识挖掘方法。本文首先对数据、信息、知识三个概念进行了区分，阐述了网络知识挖掘的概念及类型，并从数据仓库、语义网络和XML等底层信息加工组织方法上对网络知识挖掘进行探讨。     

A knowledge-based semantic framework for query expansion

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.     

The ethics of information transparency

The paper investigates the ethics of information transparency (henceforth transparency). It argues that transparency is not an ethical principle in itself but a pro-ethical condition for enabling or impairing other ethical practices or principles. A new definition of transparency is offered in order to take into account the dynamics of information production and the differences between data and information. It is then argued that the proposed definition provides a better understanding of what sort of information should be disclosed and what sort of information should be used in order to implement and make effective the ethical practices and principles to which an organisation is committed. The concepts of “heterogeneous organisation” and “autonomous computational artefact” are further defined in order to clarify the ethical implications of the technology used in implementing information transparency. It is argued that explicit ethical designs, which describe how ethical principles are embedded into the practice of software design, would represent valuable information that could be disclosed by organisations in order to support their ethical standing.     

基于语义的多传感器电力系统信息融合安全框架研究

电力系统的多传感器系统信息融合过程中普遍存在的异质异构性增加了信息融合的难度,并造成了信息融合的歧义。为了解决这一问题,提出了一种信息自动融合框架,主要创新之处在于信息融合过程中的语义处理方法。对框架中的全局语义本体定义、传感器局部信息源向全局本体的映射方法、语义信息征订与发布机制、信息融合规则进行了介绍。本框架可以有效地实现多传感器系统中信息自动融合,获得全面、安全、准确和细粒度的全局信息。     

叙词表转换为Ontology的研究

Ontology为需要共享某一领域信息的研究人员提供了通用的词表，而这正是传统的叙词表在信息检索中所起的作用。本文对叙词表向OntologY转换进行了综合性介绍，分别介绍了Ontology定义及作用、叙词表和Ontology的联系、Ontology所包含的内容和建立步骤以及叙词表转换为Ontology的研究和进展，最后结合一个具体实例加以阐述。     

网络环境下教育信息资源的分类与利用模式

网络环境下的教育信息资源是和教育有关的显性信息资源和隐性信息资源的统称,包括教育主体与教育客体资源、课内教育信息资源与课外教育信息资源、教育条件资源与教育环境资源等;通过教学模式、图书馆模式、网络模式、实践模式、信息素养模式等来达到有效利用教育信息资源的目的,推进教育信息化的科学发展。     

基于Web3.0的信息资源共建共享应用研究及实现

Web3.0是Web1.0与Web2.0的继承与发展,文章首先介绍了Web3.O及其相关概念,在此基础上,分析了Web3.0中所采用的语义网的概念及其主要相关技术,并将其与现有的实例相结合,探讨了如何建立一个基于Web3.0技术的信息共享平台。     

基于本体的信息系统引论

Since Tim Bemers-Lee, current W3C chairman, first proposed the concept of Semantic Web, it is be-coming a hot topic in computer information processing area. Ontologies are playing a key role in the Semantic Web, ex-tending syntactic interoperability to semantic intemperability by providing a source of shared and precisely defined terms.The paper analyzes the requirement of information systems for ontology languages. The current popular ontology languages are also discussed.     

复合图书馆信息资源建设的三大策略

复合图书馆信息资源建设应采用印刷型信息资源与数字化信息资源互补、信息资源拥有与信息资源存取并重、实体馆藏信息资源与虚拟馆藏信息资源整合等策略。     

论电子政务信息资源共享的影响因素

电子政务信息资源共享是一项复杂的系统工程,同时也是电子政务建设的重要内容。在电子政务信息资源共享的构建过程中,不可避免地受一系列因素所影响。本文分析电子政务信息资源共享的六项影响因素：电子政务信息资源共享的需求迫度、电子政务信息资源共享的机制健全度、电子政务信息资源共享的服务效度、电子政务信息资源共享的资金盈度、电子政务信息资源共享的技术成熟度以及电子政务信息资源共享的观念深度。