Authors:
Maximilian Stäbler
1
;
Tobias Müller
2
;
Frank Köster
3
and
Christoph Schlueter-Langdon
4
Affiliations:
1
Institute for AI Safety and Security, DLR, Wilhelm-Runge-Straße 10, 89081 Ulm, Germany
;
2
SAP SE, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany
;
3
Institute for AI Safety and Security, DLR, Lilienthalplatz 7, 38108 Braunschweig, Germany
;
4
Drucker School of Business, Claremont Graduate University, 1021 N Dartmouth Ave, Claremont, CA 91711, U.S.A.
Keyword(s):
Knowledege Representation, Ontologie Alignment, Semantic Interoperability, Graph Analysis.
Abstract:
The increasing linkage of different data sources and data ecosystems underlines the need for high-quality and well-structured data. Unambiguous descriptions of data (meta-data) promote a common understanding of the data among different users. New ontologies and data schemas are constantly being developed for this purpose. While there are new ways to align, merge or match these ontologies and data schemas, the context of the data, which is important for a clear understanding, is often not taken into account. This work addresses this problem by analyzing a graph consisting of 1,615 data attributes from 13 domains and 828 different ontologies. The results show how overlapping and partially synonymous ontologies, both from the same domain and from different domains, are. The results show the complexity for users in creating unique descriptions of data and why new approaches and methods are needed to achieve semantic interoperability.