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

HotMatch results for OEAI 2012

Abstract. HotMatch is a multi-strategy matcher developed by a group of students at Technische Universität Darmstadt in the course of a hands-on training. It implements various matching strategies. The tool version submitted to OAEI 2012 combines different basic matching strategies, both element-based and structure-based, and a set of filters for removing faulty mappings. 1 Presentation of the system 1.1 State, purpose, general statement HotMatch 1 has been developed by a group of students in the course of a semantic web hands-on training conducted at TU Darmstadt. The students were asked to develop and implement different matching algorithms. For OAEI 2012, we have combined a large number of those matching algorithms into one tool. To give an overview of our approaches, all matchers are depicted in figure 1. In contrast to matchers, filters are used to remove mapping elements found by previous matchers. 1.2 Specific techniques used HotMatch provides a library of different matching algorithms and filters