Using classification principles to support semantic integration in information engineering

语义问题是跨源（如通过Internet提供的源）有效、大规模共享信息的紧迫问题之一。语义网的前景近年来受到广泛关注。 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。 拟议的研究将开发新的，基于理论的机制，提供语义规范的信息源和协调独立的来源，并将评估这些方法的有效性。该研究计划将集中在两个领域。首先，它将开发的方法，以提高结构化数据源，如传统的关系数据库的语义，通过利用推理能力可能作为分类的实例。也就是说，当我们对事物进行分类时，我们可以推断出关于事物的信息，而不是用于分类的信息。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，该研究将使用分类产生的推理能力来为协作标记机制提供语义层，这些机制最近成为在社会书签环境中注释资源的流行工具（例如，del.icio.us和Flickr.com）。我们将评估这些方法的有效性，以促进检索有关资源的信息在流行的社会书签网站，并在知识管理application.As的可用性和网络资源的范围增长，需要有效的方法和工具来管理信息变得更加重要。本研究的目的是有助于信息工程的语义网支持有效的搜索，过滤和使用的在线资源。

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.