Prof. Dr. Ralf Möller
Software, Technology and Systems Group (STS),
Hamburg University of Technology (TUHH)


Publications


2011


Sofia Espinosa, Atila Kaya, and Ralf Möller. Knowledge-Driven Multimedia Information Extraction and Ontology Evolution, volume 6050 of LNCS, chapter Logical Formalization of Multimedia Interpretation, pages 110–133. Springer, 2011.
Bibtex entry  Paper (PDF)

Abstract

The primary goal of this chapter is to present logical formalizations of interpretation. The chapter presents pioneering work on logic-based scene interpretation that has a strong in uence on multimedia interpretation. Early approaches are discussed in more detail to analyze the main reasoning techniques. More recent approaches, which are more formal and therefore harder to understand, are referred to by providing references to the literature such that the reader can get an overview over the research field of logic-based media interpretation.


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Sebastian Wandelt. Dealing Efficiently with Ontology-Enhanced Linked Data for Multimedia. Technical report, Hamburg University of Technology, 2011.
Bibtex entry  Paper (PDF)

Abstract

In order to provide automatic ontology-based multimedia annotation for producing linked data, scalable high-level media interpretation processes are required. In this paper we shortly describe an abductive media interpretation agent, and based on a Multimedia Content Ontology we introduce partitioning techniques for huge sets of time-related annotation assertions such that interpretation as well as retrieval processes refer to manageable sets of metadata.


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Michael Wessel. Media Interpretation and Companion Feedback for Multimedia Annotation. Technical report, Hamburg University of Technology, 2011.
Bibtex entry  Paper (PDF)

Abstract

Companion technology supports, for instance, context-specific dialogues between a user and a computational system. We explore this technology in the context of cooperative computer-aided semantic annotation of multimedia (CASAM). Reasoning-based media interpretation exploits user input and multimedia analysis results to propose high-level media annotations (called interpretations). In this paper it is shown how queries are generated that are addressed to users of the system, with the goal to exploit answers such that the number of internal interpretations is substantially reduced. Queries are associated with so-called importance values specifying the expected degree of disambiguation for interpretation alternatives.


Ralf Möller. Zur Rolle der Logik bei der Entwicklung Intelligenter Systeme. KI-Zeitschrift, 25(4):309–311, 2011.
Bibtex entry  Paper (PDF)


Özgür L. Özçep and Ralf Möller. Combining Lightweight Description Logics with the Region Connection Calculus. Technical report, Institute for Softwaresystems (STS), Hamburg University of Technology, 2011. Available online at http://www.sts.tu-harburg.de/tech-reports/papers.html.
Bibtex entry  Paper (PDF)


Didier Verna and Ralf Möller, editors. Proc. European Lisp Symposium. Hamburg University of Technology, 2011.
Bibtex entry  Paper (PDF)


Sebastian Wandelt and Ralf Möller. Islands and Query Answering for ALCHI-Ontologies. In Proc. Third International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Heidelberg, Germany, volume 128 of CCIS, pages 224–236. Springer, 2011.
Bibtex entry  Paper (PDF)


Sebastian Wandelt and Ralf Möller. Sound and Complete SHI Instance Retreival for 1 Billion ABox Assertions. In A. Fokuoe, Th. Liebig, and Y. Guo, editors, Workshop on Scalable Semantic Web Systems, pages 75–89, 2011.
Bibtex entry  Paper (PDF)

Abstract

In the last years, reasoning over very large ontologies became more and more important. In our contribution, we propose a reasoning infrastructure, which can perform instance checking and instance retrieval over ontologies with semi-expressive terminological knowledge and large assertional parts. The key idea is to 1) use modularizations of the assertional part, 2) use some kind of intermediate structure to find similarities between individual modules, and 3) store information efficiently on external memory. For evaluation purposes, experiments on benchmark and real world ontologies were carried out. We show that our reasoning infrastructure can handle up to 1 billion ABox assertions. To the best of our knowledge this is the first system to provide sound and complete reasoning over SHI ontologies with such a huge number of assertions.


Acknowledgments
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