Knowledge management in biomedical libraries: A semantic web approach

In recent years, technological advances in high-throughput techniques and efficient data gathering methods, coupled with a world-wide effort in computational biology, have resulted in an enormous amount of life science data available in repositories devoted to biomedical literature. These repositories lack the ability to attain an effective and accurate search. Using semantic technologies as the key for interoperation enables searching and processing of biomedical literature in a more efficient way. However, emerging semantic applications take for granted specific knowledge that biomedical researchers may not have. This paper presents design principles for easy-to-use biomedical semantic applications by means of ontology-based annotations and faceted search. The proposed approach is backed with a usable prototype that shows the breakthroughs of adding these principles to a biomedical digital library where identifying and searching information are critical aspects for non-semantic Web experts.     

基于Ontology的信息检索技术研究

随着Web 的迅速发展,网上信息资源越来越丰富,网络已经成为了一个全球最大的信息库。而用户要从中得到所需的信息一般是通过各种信息检索工具。但是现有的信息检索工具都存在着检索精度不高等问题。本文针对这些问题,提出了将Ontology 融合到信息检索技术中的思路。利用Ontology 中拥有的领域知识,可以大大提高检索系统对自然语言文本的理解能力,同时方便用户以自然语言的方式提出检索请求,从而提高检索的效果。     

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

The increasing amount of unstructured text published on the Web is demanding new tools and methods to automatically process and extract relevant information. Traditional information extraction has focused on harvesting domain-specific, pre-specified relations, which usually requires manual labor and heavy machinery; especially in the biomedical domain, the main efforts have been directed toward the recognition of well-defined entities such as genes or proteins, which constitutes the basis for extracting the relationships between the recognized entities. The intrinsic features and scale of the Web demand new approaches able to cope with the diversity of documents, where the number of relations is unbounded and not known in advance. This paper presents a scalable method for the extraction of domain-independent relations from text that exploits the knowledge in the semantic annotations. The method is not geared to any specific domain (e.g., protein–protein interactions and drug–drug interactions) and does not require any manual input or deep processing. Moreover, the method uses the extracted relations to compute groups of abstract semantic relations characterized by their signature types and synonymous relation strings. This constitutes a valuable source of knowledge when constructing formal knowledge bases, as we enable seamless integration of the extracted relations with the available knowledge resources through the process of semantic annotation. The proposed approach has successfully been applied to a large text collection in the biomedical domain and the results are very encouraging.     

基于RDF的动态语义检索算法

在语义Web中,基于RDF,文章描述一个动态的语义检索算法(DSSAtheDynamicSemanticSearchAlgo-rithm),该算法通过分布式的资源相关库来发现动态资源之间的语义相关性,该算法不同于传统的语义检索算法,这是因为该算法引入了分布式的资源相关库,它是在用户进行语义查询的过程中动态建立起来的,随着资源的内容和状态的变化而不断变化,可以动态更新,最真实地反映资源的语义信息。文章确信该算法在保证查全率的基础上,会提高语义查询的查准率,并缩短返回查询结果的时间。     

China's e-science knowledge grid environment

The Internet and World Wide Web are milestones in the history of information sharing. Scientists are increasingly relying on them to support their research. Knowledge is the basis of realizing intelligent services. The knowledge grid is a mechanism that can synthesize knowledge from data through mining and reference methods and enable search engines to make references, answer questions, and draw conclusions from masses of data. The knowledge grid infrastructure supports e-science through a set of relevant application services and semantic resources. We have developed a semantic-link-making tool for users to conveniently describe their understandings of provided resources and background knowledge.     

Relationship Web: Blazing Semantic Trails between Web Resources

Using keywords as inputs to search engines and receiving documents as responses remains the prevalent way to access information on the Web. Although a shift toward entity awareness is a fairly recent trend in information access, such methods remain devoid of semantics, which are increasingly recognized as the lynchpin of search, integration, and analysis. We argue that relationships are at the heart of semantics, and, as such, we envision a Web of relationships to relate content across Web resources. Under this powerful new paradigm, information access over the Web would switch from a mere document-retrieval operation to an information framework that supports insight elicitation and semantic analytics over Web resources. In this column, we outline our vision and discuss how recent Improvements in content extraction and semantic annotation will ultimately help us realize this relationship Web.     

Web-Based Semantic Similarity: An Evaluation in the Biomedical Domain

Computation of semantic similarity between concepts is a very common problem in many language related tasks and knowledge domains. In the biomedical field, several approaches have been developed to deal with this issue by exploiting the structured knowledge available in domain ontologies (such as SNOMED-CT or MeSH) and specific, closed and reliable corpora (such as clinical data). However, in recent years, the enormous growth of the Web has motivated researchers to start using it as the corpus to assist semantic analysis of language. This paper proposes and evaluates the use of the Web as background corpus for measuring the similarity of biomedical concepts. Several ontology-based similarity measures have been studied and tested, using a benchmark composed by biomedical terms, comparing the results obtained when applying them to the Web against approaches in which specific clinical data were used. Results show that the similarity values obtained from the Web for ontology-based measures are at least and even more reliable than those obtained from specific clinical data, showing the suitability of the Web as information corpus for the biomedical domain.     

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.     

基于WWW的聚类引擎研究

Today, search engine is the most commonly used tool for Web information retrieval, data mining may discover knowledge in large data. With the era of information and digital of media, Web data mining is becoming one of the hottest topics. By combining information retrieval technology with data mining technology, a prototype system of search engine is designed and implemented in this paper. It can group Web search results in a semantic, online and tree way, in order to help users find relevant Web information easier and faster.     

COWB: A cloud-based framework supporting collaborative knowledge management within biomedical communities

Despite the large acceptance of Semantic Web technologies and their key role in bioinformatics, some concerns begin to emerge about their suitability for supporting the requirements of collaborative environments where a research community shares and creates new knowledge. The paper discusses these concerns and proposes COWB (COllaborative Workspaces in Biomedicine), a framework which supports collaborative knowledge management in the context of biomedical communities. COWB is grounded on a multi-layer ontology-centric model. It harnesses both the semantic knowledge captured from ontologies and the functional knowledge about resources which extend the domain knowledge and support its management. Public and private workspaces provide an accessible representation of the collective knowledge that is incrementally created and allow the knowledge to cross the boundaries of closed local information. The paper presents the deployment of COWB in a cloud platform which severely curtails issues associated with scalability and performance. The paper shows the suitability of the proposed approach and aims to suggest how exploiting the potential of the Semantic Web technologies in the context of emergent technologies including Web 2.0, NoSQL databases and the cloud paradigm.     

Making agents secure on the semantic Web

Agents were designed to collaborate and share information. While highly desirable for interoperability, this feature is scary from the security perspective. Illegal inferences, supported by semantic Web technology and ontologies, might enable users to access unauthorized information. In addition to semantic associations and replicated data with different sensitivity, malicious agents could also exploit statistical inferences. Although each agent in a system might behave in a desired and secure way, their combined knowledge could be used to disclose sensitive data. The research community must therefore develop and implement techniques that allow control over released data. To answer the questions related to information availability (scalability), data correctness (integrity), and access control in the presence of illegal inferences and undesired collaborations (confidentiality), researchers in semantic Web technologies (XML, RDF, DAML, and multiagent systems) and information system security need to collaborate. Indeed, given the Web's openness, dynamic nature, and diverse user population, developing secure Web services will require the collaboration of experts in different fields from both industry and academia. In turn, the intelligent Web of the future will facilitate unheard of support for collaborations and information management.     

基于语义网的高效信息查询方法

为了提高Web信息检索的准确率,提出一种基于语义网的高效信息查询方法。首先从本体库中提取目标资源与查询关键字之间的语义路径,通过分析语义路径所包含的属性的权重和识别能力,分别计算每个语义路径的权重;然后,根据资源与查询关键字之间的语义路径的权重、数量和特异性,分别计算每个资源与各关键字之间的语义相关性,并结合关键字的涵盖范围和识别能力综合计算每个资源与关键字集之间的语义相关性;最后,以该相关性为依据对所有资源进行排序和输出。实验结果表明,与OntoLook、tf*idf和TMSubtree三种语义网查询算法相比,基于语义网的高效信息查询方法的平均正确率分别提高了69.0、25.0和21.0个百分点;平均召回率分别提高了77.1、28.3和24.3个百分点;平均F测度值分别提高了72.4、26.4和22.4个百分点。实验结果表明:该方法不仅能够有效提升语义查询的准确率,而且对隐性信息也有很好的查询效果。     

语义网站点的发现与排序

随着语义网中RDF数据的大量涌现,语义搜索引擎为用户搜索RDF数据带来了便利.但是,如何自动地发现包含语义网信息资源的站点,并高效地在语义网站点中收集语义网信息资源,一直是语义搜索引擎所面临的问题.首先介绍了语义网站点的链接模型.该模型刻画了语义网站点、语义网信息资源、RDF模型和语义网实体之间的关系.基于该模型讨论了语义网实体的归属问题,并进一步定义了语义网站点的发现规则;另外,从站点链接模型出发,定义了语义网站点依赖图,并给出了对语义网站点进行排序的算法.将相关算法在一个真实的语义搜索引擎中进行了初步测试.实验结果表明,所提出的方法可以有效地发现语义网站点并对站点进行排序.     

基于本体的物流行业知识库的研究*

构建了基于本体的物流行业信息化知识库。应用语义Web和Web服务技术研究物流知识资源,设计了物流领域本体,实现了文本摘要、规则抽取、本体整合等关键功能,解决物流系统中语义和重用问题,提高了物流企业的知识共享和协作能力。这不仅为物流行业信息化奠定了坚实基础,而且为知识库的复用创造了条件。     

Towards Deeper Understanding of the Search Interfaces of the Deep Web

Many databases have become Web-accessible through form-based search interfaces (i.e., HTML forms) that allow users to specify complex and precise queries to access the underlying databases. In general, such a Web search interface can be considered as containing an interface schema with multiple attributes and rich semantic/meta-information; however, the schema is not formally defined in HTML. Many Web applications, such as Web database integration and deep Web crawling, require the construction of the schemas. In this paper, we first propose a schema model for representing complex search interfaces, and then present a layout-expression based approach to automatically extract the logical attributes from search interfaces. We also rephrase the identification of different types of semantic information as a classification problem, and design several Bayesian classifiers to help derive semantic information from extracted attributes. A system, WISE-iExtractor, has been implemented to automatically construct the schema from any Web search interfaces. Our experimental results on real search interfaces indicate that this system is highly effective.     

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。     

踩点式信息搜索的新方法

从小偷踩点获取藏金信息中受到启发，提出了一种互联网信息智能搜索新方法。能够从已经分好类的特定领域网站中，准确高效地搜索出隐藏于其内部的目标网页。把所有的搜索网页根据检索信息分成两类：一类是信息点，一类是信息路径。采用信息路径特征与信息点信息量特征描述有机结合而形成的一种新的搜索知识表示方法。基于这种知识表示方法，智能搜索方法不仅能够对网站中网页进行深度优先的智能搜索，而且还能够通过对其搜索过程和结果的自学习来获取更多更好的搜索知识。     

Web资源的多粒度语义标注及其应用技术研究

当前的Web搜索引擎获得的搜索结果都是基于关键字标注的Web文档、页面或链接,不支持对文档内部信息的检索。为支持Wcb资源内部信息的检索,研究多粒度语义标注,即按树根结点、分支结点、叶子结点及资源信息元为粒度单位对Web资源进行组织管理,并在此基础上探讨基于本体的搜索技术。初步的分析和实验表明,这样可以提高从形式多样的海量Web资源中获取所需信息的效率。     

基于本体的教学知识库研究与应用

知识库是智能教学系统的基础。由于教学知识库的描述标准不统一,知识表示方法也不同,所以导致教学知识难以共享和互操作。将本体引入教学领域知识库建模过程,建立概念共享模型,提供概念语义空间,不仅可以解决智能教学系统中的知识共享和互操作问题,而且易于实现基于本体的语义检索系统,从而大大提高系统的查全率和查准率。