Results 1 - 10
of
16
Formalizing Multimedia Interpretation based on Abduction over Description Logic Aboxes ⋆
"... Abstract. The paper describes how interpretations of multimedia documents can be formally derived using abduction over domain knowledge represented in an ontology. The approach uses an expressive ontology specification language, namely description logics in combination with logic programming rules, ..."
Abstract
-
Cited by 12 (1 self)
- Add to MetaCart
(Show Context)
Abstract. The paper describes how interpretations of multimedia documents can be formally derived using abduction over domain knowledge represented in an ontology. The approach uses an expressive ontology specification language, namely description logics in combination with logic programming rules, and formalizes the multimedia interpretation process using a combined abduction and deduction operation. We describe how the observables as well as the space of abducibles can be formally defined. The approach is evaluated using examples from text processing, but can also be applied to interpret content in other modalities. 1
Automatic Identity Recognition in the Semantic Web ⋆
"... Abstract. The OKKAM initiative 1 has recently highlighted the need of moving from the traditional web towards a “web of entities”, where real-world objects descriptions could be retrieved, univocally identified, and shared over the web. In this paper, we propose our vision of the entity recognition ..."
Abstract
-
Cited by 10 (0 self)
- Add to MetaCart
(Show Context)
Abstract. The OKKAM initiative 1 has recently highlighted the need of moving from the traditional web towards a “web of entities”, where real-world objects descriptions could be retrieved, univocally identified, and shared over the web. In this paper, we propose our vision of the entity recognition problem and, in particular, we propose methods and techniques to capture the “identity ” of a real entity in the Semantic Web. We claim that automatic techniques are needed to compare different RDF descriptions of a domain with the goal of automatically detect heterogeneous descriptions of the same real-world objects. Problems and techniques to solve them are discussed together with some experimental results on a real case study on web data. 1
A Probabilistic Abduction Engine for Media Interpretation Based on Ontologies
- In Pascal Hitzler and Thomas Lukasiewicz, 148 editors, RR, volume 6333 of Lecture Notes in Computer Science
"... For multimedia interpretation, and in particular for the combined interpretation of information coming 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 observ ..."
Abstract
-
Cited by 9 (6 self)
- Add to MetaCart
(Show Context)
For multimedia interpretation, and in particular for the combined interpretation of information coming 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
E.: Ontology Population and Enrichment: State of the Art
- Berlin / Heidelberg
, 2011
"... Abstract. Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project 1, due to the management of multimedia resources ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
(Show Context)
Abstract. Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project 1, due to the management of multimedia resources and the multi-modal semantic interpretation that they require. The purpose of this chapter is to present a survey of the most relevant methods, techniques and tools used for the task of ontology learning. Adopting a practical perspective, an overview of the main activities involved in ontology learning is presented. This breakdown of the learning process is used as a basis for the comparative analysis of existing tools and approaches. The comparison is done along dimensions that emphasize the particular interests of the BOEMIE project. In this context, ontology learning in BOEMIE is treated and compared to the state of the art, explaining how BOEMIE addresses problems observed in existing systems and contributes to issues that are not frequently considered by existing approaches.
Ontology-Enhanced Linked Data for Multimedia ⋆
, 2011
"... 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 ..."
Abstract
- Add to MetaCart
(Show Context)
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. 2Dealing Efficiently with
Dealing Efficiently with Ontology-Enhanced Linked Data for Multimedia ⋆
"... 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 ..."
Abstract
- Add to MetaCart
(Show Context)
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 timerelated annotation assertions such that interpretation as well as retrieval processes refer to manageable sets of metadata.
BOEMIE: Reasoning-based Information Extraction
"... Abstract. This paper presents a novel approach for exploiting an ontology in an ontology-based information extraction system, which substitutes part of the extraction process with reasoning, guided by a set of automatically acquired rules. 1 ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. This paper presents a novel approach for exploiting an ontology in an ontology-based information extraction system, which substitutes part of the extraction process with reasoning, guided by a set of automatically acquired rules. 1
Birthday: 31.08.1982
"... "Präferenzbasierte Szeneninterpretation " selbständig verfasst habe und keine anderen ..."
Abstract
- Add to MetaCart
(Show Context)
"Präferenzbasierte Szeneninterpretation " selbständig verfasst habe und keine anderen
Short Paper: Non-Taxonomic Concept Addition to Ontologies
"... Abstract. Concept addition, an ontology evolution’s edit operation, includes adding taxonomic (hierarchical structure) and non-taxonomic (concept properties) relations. Generating concept properties requires information extraction from various sources, such as WordNet. Other than semantic similariti ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. Concept addition, an ontology evolution’s edit operation, includes adding taxonomic (hierarchical structure) and non-taxonomic (concept properties) relations. Generating concept properties requires information extraction from various sources, such as WordNet. Other than semantic similarities generated by WordNet, self-information generated from existing non-taxonomic relations has aided non-taxonomic relation addition to ontologies. Evaluation is based on using an ontology as gold standard and detaching and reattaching the nodes. Non-taxonomic relation generation without accessing an enormous amount of information has proven to be quite difficult; the results displayed in this work are an indication of this difficulty.
Bootstrapping Ontology Evolution with Multimedia Information Extraction
"... a new approach towards the automation of knowledge acquisition from multimedia content. In particular, it developed and demonstrated the notion of evolving multimedia ontologies, which is used for the extraction, fusion and interpretation of information from content of various media types (audio, vi ..."
Abstract
- Add to MetaCart
(Show Context)
a new approach towards the automation of knowledge acquisition from multimedia content. In particular, it developed and demonstrated the notion of evolving multimedia ontologies, which is used for the extraction, fusion and interpretation of information from content of various media types (audio, video, images and text). BOEMIE adopted a synergistic approach that combines multimedia extraction and ontology evolution in a bootstrapping process. This process involves, on the one hand, the continuous extraction of semantic information from multimedia content in order to populate and enrich the ontologies and, on the other hand, the deployment of these ontologies to enhance the robustness of the extraction system. Thus, in addition to annotating multimedia content with semantics, the extracted knowledge is used to expand our understanding of the domain and extract even more useful knowledge. The methods and technologies developed in BOEMIE were tested in the domain of athletics, using large sets of annotated content and evaluation by domain
