Domain-Specific OWL Ontology Visualization with OWLGrEd

Abstract. Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) elements, displaying controlled natural language (CNL) explanations of OWL axioms corresponding to the selected visual notation elements. Thus, the domain experts will benefit from both the high-level overview provided by the graphical notation and the detailed textual explanations of particular elements in the diagram.

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“…We refer to [2,1] for a more detailed explanation of the OWLGrEd notation and editor, as well as the principles of its visual extensions [4].…”

Section: Mandatorycourse Subclassof Inverse Teaches Only Professormentioning

confidence: 99%

Towards Self-explanatory Ontology Visualization with Contextual Verbalization

Liepins

Bojārs

Grūzītis

et al. 2016

Communications in Computer and Information Science

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“…This structural information can be encoded using Web Ontology Language (OWL) files [15]. OWL was created as a description logic language and it is intended for automatic reasoning; nevertheless, its axioms can also be used to construct a graphical overview of the described resource [16]. However, the OWL standard does not require all axioms necessary for such reconstruction.…”

Section: Introductionmentioning

confidence: 99%

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource

et al. 2015

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BackgroundSemantic web technologies have a tremendous potential for the integration of heterogeneous data sets. Therefore, an increasing number of widely used biological resources are becoming available in the RDF data model. There are however, no tools available that provide structural overviews of these resources. Such structural overviews are essential to efficiently query these resources and to assess their structural integrity and design, thereby strengthening their use and potential.ResultsHere we present RDF2Graph, a tool that automatically recovers the structure of an RDF resource. The generated overview allows to create complex queries on these resources and to structurally validate newly created resources.ConclusionRDF2Graph facilitates the creation of complex queries thereby enabling access to knowledge stored across multiple RDF resources. RDF2Graph facilitates creation of high quality resources and resource descriptions, which in turn increases usability of the semantic web technologies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0038-9) contains supplementary material, which is available to authorized users.

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“…Nevertheless, more and more tools are built to facilitate editing of ontologies [45,11], as ontologies are subject to change. This leads to the development of visualization plugins that show the (updated) ontology inside the tool [39].…”

Section: Ontology Visualizationsmentioning

confidence: 99%

Specification and Implementation of Mapping Rule Visualization and Editing: MapVOWL and the RMLEditor

et al. 2018

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Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values.

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Domain-Specific OWL Ontology Visualization with OWLGrEd

Cited by 11 publications

References 3 publications

Towards Self-explanatory Ontology Visualization with Contextual Verbalization

Towards Self-explanatory Ontology Visualization with Contextual Verbalization

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource

Specification and Implementation of Mapping Rule Visualization and Editing: MapVOWL and the RMLEditor

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