Annual Report of the University, 2005-2006, Volumes 1-7

PROPOSED POLICIES The Office of Government & Community Relations is in charge of advancing the University\u27s interests at all levels of federal, state and local government. The following policy guidelines for working with University units will achieve a coordinated and effective institutional advancement program. • To inform the Office of Government & Community Relations of all planned contacts and correspondence with elected officials and policy-making employees of federal, state and local government, including those who are alumni or friends of the University. Those items which pertain to sponsored research should be coordinated with the Vice President for Research. • To consult the Office of Government & Community Relations on any verbal or written statements made on behalf of the University that concern federal, state or local policies, legislation or regulations. • To advise the Office of Government & Community Relations on any activities, conferences, seminars, lectures or projects that involve the community and/or impact the University area. • Faculty or staff members who contact federal, state or local policy-making employees as experts in a specific field, or who act on behalf of themselves or another organization, should include a disclaimer which clearly states that they are not acting on behalf of the University

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications