The problem of semantics is one of the pressing issues both in effectively representing the meaning of a domain in information systems artifacts (data and software), and in supporting large-scale sharing of information across multiple independent sources, such as those available over the Internet. 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 semantics of information provided by independent sources. This research will develop new approaches to specifying the semantics of information sources and to reconciling independent sources, and will evaluate the effectiveness of these approaches.
We will build on earlier work by focusing on three areas. First, we will develop methods to enhance the semantics of information models by exploiting the inference capabilities afforded through the classification of instances. Second, we will apply the concept of classification-based inference to provide a semantic layer to tagging mechanisms that have recently emerged as popular tools for annotating content in social networking contexts (e.g., del.icio.us and Flickr.com). Third, we will apply and evaluate the methods developed in the first two phases of research to business contexts. In particular, we will use the classification principles to develop domain ontologies for the online travel industry in conjunction with an industry partner based in Asia. As the availability and scope of networked resources grow, the need for effective methods and tools to locate and manage information is becoming more critical to both individuals and organizations. This research aims to contribute to information engineering by supporting the effective classification of information resources in order to improve the ability to search, filter, and use these resources.