The Experts below are selected from a list of 487677 Experts worldwide ranked by ideXlab platform
C. Maria Keet - One of the best experts on this subject based on the ideXlab platform.
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The African wildlife Ontology tutorial ontologies.
Journal of biomedical semantics, 2020Co-Authors: C. Maria KeetAbstract:BACKGROUND Most tutorial ontologies focus on illustrating one aspect of Ontology development, notably language features and automated reasoners, but ignore Ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercise they carry out. Not providing good examples holistically causes the propagation of sub-optimal Ontology development, which may negatively affect the quality of a real domain Ontology. RESULTS We identified 22 requirements that a good tutorial Ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an Ontology engineering course or tutorial since 2010 and is included in a recent Ontology engineering textbook with relevant examples and exercises. CONCLUSION The African Wildlife Ontology provides a wide range of options concerning examples and exercises for Ontology engineering well beyond illustrating just language features and automated reasoning. It assists in demonstrating tasks concerning Ontology quality, such as alignment to a foundational Ontology and satisfying competency questions, versioning, and multilingual ontologies.
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The African Wildlife Ontology tutorial ontologies: requirements, design, and content.
arXiv: Artificial Intelligence, 2019Co-Authors: C. Maria KeetAbstract:Background. Most tutorial ontologies focus on illustrating one aspect of Ontology development, notably language features and automated reasoners, but ignore Ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercises they carry out. Not providing good examples holistically causes the propagation of sub-optimal Ontology development, which may negatively affect the quality of a real domain Ontology. Results. We identified 22 requirements that a good tutorial Ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an Ontology engineering course or tutorial since 2010 and is included in a recent Ontology engineering textbook with relevant examples and exercises. Conclusion. The African Wildlife Ontology provides a wide range of options concerning examples and exercises for Ontology engineering well beyond illustrating only language features and automated reasoning. It assists in demonstrating tasks about Ontology quality, such as alignment to a foundational Ontology and satisfying competency questions, versioning, and multilingual ontologies.
Marta Sabou - One of the best experts on this subject based on the ideXlab platform.
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Crowd-based Ontology engineering with the uComp Protégé plugin
Sprachwissenschaft, 2016Co-Authors: Gerhard Wohlgenannt, Marta Sabou, Florian HanikaAbstract:Crowdsourcing techniques provide effective means for solving a variety of Ontology engineering problems. Yet, they are mainly used as external support to Ontology engineering, without being closely integrated into the work of Ontology engineers. In this paper we investigate how to closely integrate crowdsourcing into Ontology engineering practices. Firstly, we show that a set of basic crowdsourcing tasks are used recurrently to solve a range of Ontology engineering problems. Secondly, we present the uComp Protege plugin that facilitates the integration of such typical crowdsourcing tasks into Ontology engineering from within the Protege Ontology editor. An evaluation of the plugin in a typical Ontology engineering scenario where ontologies are built from automatically learned semantic structures, shows that its use reduces the working times for the Ontology engineers 11 times, lowers the overall task costs by 40% to 83% depending on the crowdsourcing settings used and leads to data quality comparable with that of tasks performed by Ontology engineers. Evaluations on a large anatomy Ontology confirm that crowdsourcing is a scalable and effective method: good quality results (accuracy of 89% and 99%) are obtained while achieving cost reductions of 75% from the Ontology engineer costs and providing comparable overall task duration.
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EKAW - The uComp Protégé Plugin: Crowdsourcing Enabled Ontology Engineering
Lecture Notes in Computer Science, 2014Co-Authors: Florian Hanika, Gerhard Wohlgenannt, Marta SabouAbstract:Crowdsourcing techniques have been shown to provide effective means for solving a variety of Ontology engineering problems. Yet, they are mainly being used as external means to Ontology engineering, without being closely integrated into the work of Ontology engineers. In this paper we investigate how to closely integrate crowdsourcing into Ontology engineering practices. Firstly, we show that a set of basic crowdsourcing tasks are used recurrently to solve a range of Ontology engineering problems. Secondly, we present the uComp Protege plugin that facilitates the integration of such typical crowdsourcing tasks into Ontology engineering work from within the Protege Ontology editing environment. An evaluation of the plugin in a typical Ontology engineering scenario where ontologies are built from automatically learned semantic structures, shows that its use reduces the working times for the Ontology engineers 11 times, lowers the overall task costs with 40% to 83% depending on the crowdsourcing settings used and leads to data quality comparable with that of tasks performed by Ontology engineers.
Florian Hanika - One of the best experts on this subject based on the ideXlab platform.
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Crowd-based Ontology engineering with the uComp Protégé plugin
Sprachwissenschaft, 2016Co-Authors: Gerhard Wohlgenannt, Marta Sabou, Florian HanikaAbstract:Crowdsourcing techniques provide effective means for solving a variety of Ontology engineering problems. Yet, they are mainly used as external support to Ontology engineering, without being closely integrated into the work of Ontology engineers. In this paper we investigate how to closely integrate crowdsourcing into Ontology engineering practices. Firstly, we show that a set of basic crowdsourcing tasks are used recurrently to solve a range of Ontology engineering problems. Secondly, we present the uComp Protege plugin that facilitates the integration of such typical crowdsourcing tasks into Ontology engineering from within the Protege Ontology editor. An evaluation of the plugin in a typical Ontology engineering scenario where ontologies are built from automatically learned semantic structures, shows that its use reduces the working times for the Ontology engineers 11 times, lowers the overall task costs by 40% to 83% depending on the crowdsourcing settings used and leads to data quality comparable with that of tasks performed by Ontology engineers. Evaluations on a large anatomy Ontology confirm that crowdsourcing is a scalable and effective method: good quality results (accuracy of 89% and 99%) are obtained while achieving cost reductions of 75% from the Ontology engineer costs and providing comparable overall task duration.
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EKAW - The uComp Protégé Plugin: Crowdsourcing Enabled Ontology Engineering
Lecture Notes in Computer Science, 2014Co-Authors: Florian Hanika, Gerhard Wohlgenannt, Marta SabouAbstract:Crowdsourcing techniques have been shown to provide effective means for solving a variety of Ontology engineering problems. Yet, they are mainly being used as external means to Ontology engineering, without being closely integrated into the work of Ontology engineers. In this paper we investigate how to closely integrate crowdsourcing into Ontology engineering practices. Firstly, we show that a set of basic crowdsourcing tasks are used recurrently to solve a range of Ontology engineering problems. Secondly, we present the uComp Protege plugin that facilitates the integration of such typical crowdsourcing tasks into Ontology engineering work from within the Protege Ontology editing environment. An evaluation of the plugin in a typical Ontology engineering scenario where ontologies are built from automatically learned semantic structures, shows that its use reduces the working times for the Ontology engineers 11 times, lowers the overall task costs with 40% to 83% depending on the crowdsourcing settings used and leads to data quality comparable with that of tasks performed by Ontology engineers.
Zheng Quan - One of the best experts on this subject based on the ideXlab platform.
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Research of video semantic retrieval system based on Ontology
Journal of Computer Applications, 2010Co-Authors: Zheng QuanAbstract:Retrieving video content in the semantic level can break "semantic gap" and increase the utilization efficiency of video content.The authors made use of the annotation and reasoning ability of Ontology,studied video semantic retrieval,fully mined the structural and semantic information of the video content,and built the hierarchical semantic indexing,which can greatly strengthen the system's semantic retrieval ability.The Ontology structure of Video Semantic Retrieval System(OVSR)integrated domain Ontology,video Ontology and core Ontology,and has a strong expanding and interoperability ability.The authors mainly discussed OVSR's Ontology structure,video semantic model and indexing model,and studied the user's searching rewriting algorithm and Ontology reasoning algorithm.
Adam Pease - One of the best experts on this subject based on the ideXlab platform.
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Towards a standard upper Ontology
Proceedings of the international conference on Formal Ontology in Information Systems - FOIS '01, 2001Co-Authors: Ian Niles, Adam PeaseAbstract:The Suggested Upper Merged Ontology (SUMO) is an upper level\nOntology that has been proposed as a starter document for The\nStandard Upper Ontology Working Group, an IEEE-sanctioned working\ngroup of collaborators from the fields of engineering, philosophy,\nand information science. The SUMO provides definitions for\ngeneral-purpose terms and acts as a foundation for more specific\ndomain ontologies. In this paper we outline the strategy used to\ncreate the current version of the SUMO, discuss some of the\nchallenges that we faced in constructing the Ontology, and describe\nin detail its most general concepts and the relations between them.
