StringsAuto and MapSSS Results for OAEI 2013

Abstract. StringsAuto and MapSSS are two closely related ontology alignment systems. The StringsAuto matcher seeks to explore the limits of a syntactic-only approach to alignment. The MapSSS system then expands on this work by embedding the syntactic matching of StringsAuto within a more complete alignment system that also makes use of semantic and structural information. In this paper we describe the basic operation of the two systems and discuss their performance in the OAEI 2013 evaluation. 1 Presentation of the system 1.1 State, purpose, general statement The vast majority of ontology alignment systems use some form of string similarity metric. Our overall goal with StringsAuto and MapSSS is to explore the importance of the choice of a particular string metric. StringsAuto consists only of string metrics, while MapSSS uses strategically chosen string metrics within the context of a more fully-featured alignment system. In [1] we analyzed the performance of eleven string similarity metrics (TF-IDF

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