Using classification principles to support semantic integration in information engineering

使用分类原则支持信息工程中的语义集成

• 批准号: 155362-2007
• 负责人: Parsons, Jeffrey
• 金额: $ 1.46万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=365361
• 关键词: Using; classification; principles; support; semantic

The problem of semantics is one of the pressing issues in effective, large-scale sharing of information across sources, such as those available over the Internet. The prospect of a "Semantic Web" has received considerable attention in recent years.  Addressing the problem of semantics effectively requires overcoming two challenges: (1) specifying semantics for a single information source; and (2) providing mechanisms for reconciling the semantic specifications provided by independent and heterogeneous sources.  The proposed research will develop new, theory-based mechanisms for providing semantic specifications to information sources and for reconciling independent sources, and will evaluate the effectiveness of these approaches. The research program will focus on two areas. First, it will develop methods to enhance the semantics of structured data sources, such as traditional relational databases, by exploiting the inferential capabilities possible as a result of classifying the instances. That is, when we classify something, we can infer information about the thing beyond what was used to classify it. The effectiveness of these methods will be evaluated using available heterogeneous data sources. Second, the research will use the inferential capabilities resulting from classification to provide a semantic layer to collaborative tagging mechanisms that have recently emerged as popular tools to annotate resources in social bookmarking contexts (e.g., del.icio.us and Flickr.com). We will evaluate these methods by assessing their effectiveness in facilitating the retrieval of information about resources on popular social bookmarking sites, and in Knowledge Management applications.As the availability and scope of networked resources grow, the need for effective methods and tools to manage information is becoming more critical. This research aims to contribute to information engineering for the Semantic Web by supporting the effective search, filtering, and use of online resources.

语义问题是跨资源（如Internet上可用的资源）有效、大规模共享信息的紧迫问题之一。近年来，“语义网”的前景受到了相当大的关注。有效地解决语义问题需要克服两个挑战：(1)为单个信息源指定语义；(2)提供协调独立和异构源提供的语义规范的机制。提出的研究将开发新的、基于理论的机制，为信息源提供语义规范和协调独立来源，并将评估这些方法的有效性。该研究项目将侧重于两个领域。首先，它将开发方法来增强结构化数据源（如传统关系数据库）的语义，方法是利用对实例进行分类后可能产生的推理能力。也就是说，当我们对某物进行分类时，我们可以推断出除了用来分类之外的信息。这些方法的有效性将使用可用的异构数据源进行评估。其次，该研究将利用分类产生的推理能力，为协作标记机制提供语义层，协作标记机制最近成为在社交书签环境中注释资源的流行工具（例如，del.ici.us和Flickr.com）。我们将通过评估这些方法在促进流行的社会书签网站和知识管理应用程序上的资源信息检索方面的有效性来评估这些方法。随着网络资源的可用性和范围的增长，对管理信息的有效方法和工具的需求变得越来越重要。本研究旨在通过支持在线资源的有效搜索、过滤和使用，为语义网的信息工程做出贡献。