Towards a unified visual notation for OWL ontologies

Negru, Stefan; Haag, Florian; Lohmann, Steffen

doi:10.1145/2506182.2506192

Cited by 23 publications

(20 citation statements)

References 16 publications

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“…A major drawback of these attempts is that they require some knowledge about UML class diagrams. While these diagrams are well-known to many people working in IT, people from other domains have difficulties interpreting UML class diagrams correctly, as we found in the aforementioned comparative study [33].…”

Section: Ontology Visualizations Based On Specific Diagram Typesmentioning

confidence: 62%

“…Based upon that work, related endeavors, as well as findings from a user study [33], we have redesigned large parts of the notation to create VOWL 2, a visual language that can also be understood by casual ontology users. We have described the key considerations, features and capabilities of VOWL 2 and have presented two implementations of the visualization, a plugin for the widely used ontology editor Protégé and a responsive web application.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Based upon a review of the related work and our comparative evaluation [33], we decided to retain numerous traits of the initial VOWL version (VOWL 1). As already mentioned, graphs seem to be the natural and intuitive way to represent ontologies, which is confirmed by many of the related approaches reported above.…”

Section: Vowl 2: Visual Notation For Owl Ontologiesmentioning

confidence: 99%

“…An early version of VOWL [34] has been compared to the UML-based visualization of ontologies [33]. Based on insights from the comparison, we have completely reworked the notation and developed version 2 with significant improvements and more precise mappings to OWL.…”

Section: Introductionmentioning

confidence: 99%

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VOWL 2: User-Oriented Visualization of Ontologies

Lohmann

Negru

Haag

et al. 2014

Lecture Notes in Computer Science

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Abstract. Ontologies become increasingly important as a means to structure and organize information. This requires methods and tools that enable not only ontology experts but also other user groups to work with ontologies and related structures. We have developed VOWL, a comprehensive and well-specified visual language for the user-oriented visualization of ontologies, and conducted a comparative study on an initial version of VOWL. Based upon results from that study, as well as an extensive review of other ontology visualizations, we have reworked many parts of VOWL. In this paper, we present the new version VOWL 2 and describe how the initial definitions were used to systematically redefine the visual notation. Beside the novelties of the language definition, which is based on a well-defined set of graphical primitives and an abstract color scheme, we briefly describe two implementations of VOWL 2. To gather some insight into the user experience with the new version of VOWL, we have conducted a small qualitative evaluation. We report on the study and its results, which confirmed that not only the general ideas of VOWL but also various of our enhancements for VOWL 2 can be understood by casual ontology users.

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Section: Ontology Visualizations Based On Specific Diagram Typesmentioning

confidence: 62%

Section: Discussion and Future Workmentioning

confidence: 99%

Section: Vowl 2: Visual Notation For Owl Ontologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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VOWL 2: User-Oriented Visualization of Ontologies

Lohmann

Negru

Haag

et al. 2014

Lecture Notes in Computer Science

Self Cite

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“…There 83 are also variation of visualization tools that are provided by Protégé such as OWLVIZ, Ontograf, and VOWL 84 (Falconer, 2010;Horridge, 2010;Negru, Lohmann & Haag, 2014). Each tools have their own way to 85 visualize the ontology, which lessen the burden and ensure that the FO are created properly.…”

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confidence: 99%

Fish Ontology framework for taxonomy-based fish recognition

Ali

Khan

Then

et al. 2017

Preprint

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Life science ontologies play an important role in semantic web. In the fish and fisheries research field, it is imperative to have an ontology that can automatically provide information for biological objects annotations and links to relevant data pieces. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information of unknown fish based on metadata restrictions. It is designed to support knowledge discovery, providing semantic annotation of fish and fisheries resources, data integration, and information retrieval. The automated classification for unknown specimen is a feature not existing in other known ontologies covering fish species profiling and fisheries data. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users. 15 Life science ontologies play an important role in semantic web. In the fish and fisheries research field, it 16 is imperative to have an ontology that can automatically provide information for biological objects 17 annotations and links to relevant data pieces. As such, we introduce the Fish Ontology (FO), an automated 18 classification architecture of existing fish taxa which provides taxonomic information of unknown fish 19 based on metadata restrictions. It is designed to support knowledge discovery, providing semantic 20 annotation of fish and fisheries resources, data integration, and information retrieval. The automated 21 classification for unknown specimen is a feature not existing in other known ontologies covering fish 22 species profiling and fisheries data. Examples of automated classification for major groups of fish are 23 demonstrated, showing the inferred information by introducing several restrictions at the species or 24 specimen level. The current version of FO has 1830 classes, includes widely used fisheries terminology, 25 and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish 26 species globally, the FO will be an indispensable tool for fish scientists and other interested users. 28 INTRODUCTION29 Increasing amount of data produced by a single species has made it harder for fish researchers to manage 30 and provide fish data in a single database. Moreover, the high demand of having related data (metadata) 31 for a single species have encourage researchers to find an alternative for the current database set. Since 32 the arrival of computational automation, it is impractical to generate species data based on human 33 observation (Perez & Benjamins, 1999). The semantic web technology provides a promising platform for 34 biodiversity r...

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