Using classification principles to support semantic integration in information engineering

点击翻译按钮获取中文摘要

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.