2 research outputs found
A knowledge based approach to integration of products, processes and reconfigurable automation resources
The success of next generation automotive companies will depend upon their ability to adapt to
ever changing market trends thus becoming highly responsive. In the automotive sector, the
assembly line design and reconfiguration is an especially critical and extremely complex job. The
current research addresses some of the aspects of this activity under the umbrella of a larger
ongoing research project called Business Driven Automation (BDA) project. The BDA project
aims to carry out complete virtual 3D modeling-based verifications of the assembly line for new
or revised products in contrast to the prevalent practice of manual evaluation of effects of product
change on physical resources. [Continues.
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