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


Publications


2010


O. Gries and R. Möller. Gibbs Sampling in Probabilistic Description Logics with Deterministic Dependencies. In Thomas Lukasiewicz, Rafael Penaloza, and Anni-Yasmin Turhan, editors, Proc. International Workshop on Uncertainty in Description Logics (UnIDL-2010), 2010.
Bibtex entry  Paper (PDF)

Abstract

In many applications there is interest in representing both probabilistic and deterministic dependencies. This is especially the case in applications using Description Logics (DLs), where ontology engineer- ing usually is based on strict knowledge, while there is also the need to represent uncertainty. We introduce a Markovian style of probabilis- tic reasoning in first-order logic known as Markov logic and investigate the opportunities for restricting this formalism to DLs. In particular, we show that Gibbs sampling with deterministic dependencies specified in an appropriate fragment remains correct, i.e., probability estimates approx- imate the correct probabilities. We propose a Gibbs sampling method incorporating deterministic dependencies and conclude that this incor- poration can speed up Gibbs sampling significantly. This work is supported by the European Union project CASAM (FP7-217061).


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Michael Wessel. Meta-level reasoning engine, Report on meta-level reasoning for disambiguation and preference elicitation. Technical report, CASAM Project Deliverable D3.4, 2010.
Bibtex entry  Paper (PDF)

Abstract

In the CASAM deliverable D3.3 an agent was presented that builds interpretations upon multimedia annotations by incrementally consuming analysis results as well as input from a human annotator. These interpretations are based on background knowledge of a specific domain, e.g. an environmental domain as it was exemplarily chosen for the CASAM project. As a result of the interpretation process, multiple interpretation alternatives are possible. A preference measure for the alternatives is realised by a probabilistic scoring function. As an extension of the interpretation agent, a mechanism for meta-level reasoning is presented with the aim to disambiguate interpretation alternatives. This is achieved by generating queries from a set of interpretation alternatives and stating them to the human annotator. Queries themselves are ranked by an importance value, representing the benefit of an answer to a query for the disambiguation process. After a revision of the interpretation process the query generation mechanism is explained, followed by a detailed description of different query types, together with the format they are communicated in. Furthermore, the processing of responses to queries is addressed.


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Michael Wessel. A Probabilistic Abduction Engine for Media Interpretation. In Thomas Lukasiewicz, Rafael Penaloza, and Anni-Yasmin Turhan, editors, Proc. International Workshop on Uncertainty in Description Logics (UnIDL-2010), 2010.
Bibtex entry  Paper (PDF)

Abstract

For multimedia interpretation, and in particular for the combined interpretation of information coming from di erent modalities, a semantically well-founded formalization is required in the context of an agent-based scenario. Low-level percepts, which are represented symbolically, de ne the observations of an agent, and interpretations of content are de ned as explanations for the observations. We propose an abduction-based formalism that uses description logics for the ontology and Horn rules for de ning the space of hypotheses for explanations (i.e., the space of possible interpretations of media content), and we use Markov logic to de ne the motivation for the agent to generate explanations on the one hand, and for ranking di erent explanations on the other. This work has been funded by the European Community with the project CASAM (Contract FP7-217061 CASAM) and by the German Science Foundation with the project PRESINT (DFG MO 801/1-1).


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Michael Wessel. A Probabilistic Abduction Engine for Media Interpretation based on Ontologies. In J. Alferes, P. Hitzler, and Th. Lukasiewicz, editors, Proc. International Conference on Web Reasoning and Rule Systems (RR-2010), 2010.
Bibtex entry  Paper (PDF)

Abstract

We propose an abduction-based formalism that uses description logics for the ontology and Horn rules for de ning the space of hypotheses for explanations, and we use Markov logic to de ne the motivation for the agent to generate explanations on the one hand, and for ranking di erent explanations on the other. The formalism is applied to media interpretation problems in a agent-oriented scenario.


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Michael Wessel. A Probabilistic Abduction Engine for Media Interpretation (Extended Version). Technical report, Hamburg University of Technology, 2010.
Bibtex entry  Paper (PDF)

Abstract

For multimedia interpretation, and in particular for the combined interpretation of information com- ing from different modalities, a semantically well-founded formalization is required in the context of an agent-based scenario. Low-level percepts, which are represented symbolically, define the observations of an agent, and interpretations of content are defined as explanations for the observations. We propose an abduction-based formalism that uses description logics for the ontology and Horn rules for defining the space of hypotheses for explanations (i.e., the space of possible interpretations of media content), and we use Markov logic to define the motivation for the agent to generate explanations on the one hand, and for ranking different explanations on the other. This work has been funded by the European Community with the project CASAM (Contract FP7-217061 CASAM) and by the German Science Foundation with the project PRESINT (DFG MO 801/1-1).


Oliver Gries, Ralf Möller, Anahita Nafissi, Maurice Rosenfeld, Kamil Sokolski, and Michael Wessel. Probabilistic abduction engine: Report on algorithms and the optimization techniques used in the implementation. Technical report, CASAM Project Deliverable D3.3, 2010.
Bibtex entry  Paper (PDF)

Abstract

For multimedia interpretation, a semantically well-founded formalization is required. In accordance with previous work, in CASAM a well-founded abduction-based approach is pursued. Extending previous work, abduction is controlled by probabilistic knowledge, and it is done in terms of firstorder logic. This report describes the probabilistic abduction engine and the optimization techniques for multimedia interpretation. It extends deliverable D3.2 by providing a probabilistic scoring function for ranking interpretation alternatives. Parameters for the CASAM Abduction Engine (CAE) introduced already in D3.2 are now appropriately formalized such that CAE is better integrated into the probabilistic framework. In addition, this deliverable describes how media interpretation services can be provided that work incrementally, i.e., are able to consume new analysis results, or new input from a human annotator, and produce notifications for additional interpretation results or, in some cases, revision descriptions for previous interpretations. Incremental processing is nontrivial and is realized using an Abox di erence operator, which is used to interpretation results obtained for extended inputs with one(s) previously obtained such that notifications about additions and revisions can be computed.


Alissa Kaplunova, Ralf Moeller, Sebastian Wandelt, and Michael Wessel. Towards Scalable Instance Retrieval over Ontologies. In Bi Yaxin and Williams Mary-Anne, editors, Knowledge Science, Engineering and Management, Fourth International Conference, KSEM 2010, Proceedings, volume 6291 of Lecture Notes in Computer Science. Springer, 2010.
Bibtex entry  Paper (PDF)

Abstract

In this paper, we consider the problem of query answering over large multimedia ontologies. Traditional reasoning systems may have problems to deal with large amounts of expressive ontological data (terminological as well as assertional data) that usually must be kept in main memory. We propose to overcome this problem with a new so-called lter and re ne paradigm for ontology-based query answering. The contribution of this paper is twofold: (1) For both steps, algorithms are presented. (2) We evaluate our approach on real world multimedia ontologies from the BOEMIE project. The research was funded by the EU commission ( http://www.boemie.org/ ).


S. Wandelt and R. Möller. Distributed Island-based Query Answering for Expressive Ontologies. In Volker Haarslev, David Toman, and Grant Weddell, editors, Proceedings of the 2010 International Workshop on Description Logics (DL2010), volume 573 of CEUR-WS, pages 185–196, 2010.
Bibtex entry  Paper (PDF)

Abstract

Scalability of reasoning systems is one of the main criteria which will determine the success of Semantic Web systems in the future. The focus of recent work is either on (a) expressive description logic systems which rely on in-memory structures or (b) not-so-expressive ontology languages, which can be dealt with by using database technologies. In this paper we introduce a method to perform query answering for semi-expressive ontologies without the limit of in-memory structures. Our main idea is to compute small and characteristic representations of the assertional part of the input ontology. Query answering is then more eciently performed over a reduced set of these small representations.We show that query answering can be distributed in a network of description logic reasoning systems in order to support scalable reasoning. Our initial results are encouraging.


S. Wandelt and R. Möller. Sound Summarizations for Alchi Ontologies - How to Speedup Instance Checking and Instance Retrieval. In Second International Conference on Agents and Artificial Intelligence (ICAART). INSTICC Press, 2010.
Bibtex entry  Paper (PDF)

Abstract

In the last years, the vision of the Semantic Web fostered the interest in reasoning over ever larger sets of assertional statements in ontologies. In this senario, state-of-the-art description logic reasoning systems cannot deal with real-world ontologies any longer, since they rely on in-memory structures. In these scenarios it will become more important to rely on unsound or incomplete reasoning structures, to obtain a set of candidates/obvious solutions to queries, i.e. only apply state-of-the-art reasoning systems to the computationally hard solutions. In this paper we propose a summarization-based approach which is always sound, but possibly incomplete. We think that this technique will support description logic systems to deal with the steadily growing amounts of assertional data.


Sebastian Wandelt and Ralf Möller. Distributed Island-Based Query Answering for Expressive Ontologies. In Paolo Bellavista, Ruay-Shiung Chang, Han-Chieh Chao, Shin-Feng Lin, and Peter M. A. Sloot, editors, Advances in Grid and Pervasive Computing, 5th International Conference, GPC 2010, Hualien, Taiwan, May 10-13, 2010. Proceedings, volume 6104 of Lecture Notes in Computer Science, pages 461–470. Springer, 2010.
Bibtex entry  Paper (PDF)

Abstract

Scalability of reasoning systems is one of the main criteria which will determine the success of Semantic Web systems in the future. The focus of recent work is either on (a) systems which rely on in-memory structures or (b) not so expressive ontology languages, which can be dealt with by using database technologies. In this paper we introduce a method to perform query answering for semi-expressive ontologies without the limit of in-memory structures. Our main idea is to compute small and characteristic representations of the assertional part of the input ontology. Query answering is then more eciently performed over a reduced set of these small represenations. We show that query answering can be distributed in a network of description logic reasoning systems to scale for reasoning. Our initial results are encouraging.


Sebastian Wandelt and Ralf Möller. Distributed Island-based Query Answering for Semi-Expressive Ontologies (Extended Version). Technical report, Hamburg University of Technology, 2010.
Bibtex entry  Paper (PDF)

Abstract

Scalability of reasoning systems is one of the main criteria which will determine the success of Semantic Web systems in the future. The focus of recent work is either on (a) systems which rely on in-memory structures or (b) not so expressive ontology languages, which can be dealt with by using database technologies. In this paper we introduce a method to perform query answering for semi-expressive ontologies without the limit of in-memory structures. Our main idea is to compute small and characteristic representations of the assertional part of the input ontology. Query answering is then more efficiently performed over a reduced set of these small representations. We show that query answering can be distributed in a network of description logic reasoning systems to scale for reasoning. Our initial results are encouraging.


Sebastian Wandelt, Ralf Möller, and Michael Wessel. Towards Scalable Instance Retrieval over Ontologies (Extended Version). Journal of Software and Informatics, 2010.
Bibtex entry  Paper (PDF)

Abstract

In this paper, we consider the problem of query answering over multimedia ontologies. Traditional reasoning systems may have problems to deal with large amounts of expressive ontological data (terminological as well as assertional data) that usually must be kept in main memory. We propose to overcome this problem with a new so-called filter and refine paradigm for ontology-based query answering. The contribution of this paper is twofold: (1) For both steps, algorithms are presented. (2) We evaluate our approach on real world multimedia ontologies from the BOEMIE project.


Acknowledgments
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