Authoring Semantic Annotations for Non-Visual Access to Graphics

PLHÁK, Jaromír, Tomas MURILLO-MORALES and Klaus MIESENBERGER. Authoring Semantic Annotations for Non-Visual Access to Graphics. Journal on Technology and Persons with Disabilities. California State University, Northridge, 2018, vol. 2018, No 6, p. 399-414. ISSN 2330-4219.

Basic information

• Original name: Authoring Semantic Annotations for Non-Visual Access to Graphics
• Authors: PLHÁK, Jaromír (203 Czech Republic, guarantor, belonging to the institution), Tomas MURILLO-MORALES (724 Spain) and Klaus MIESENBERGER (40 Austria).
• Edition: Journal on Technology and Persons with Disabilities, California State University, Northridge, 2018, 2330-4219.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10200 1.2 Computer and information sciences
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• RIV identification code: RIV/00216224:14330/18:00108835
• Organization unit: Faculty of Informatics
• Keywords in English: Blind;Low Vision;Software;Ontology;SVG;Web

Abstract

Semantically-enhanced graphics are annotated with formal underpinnings in order to augment them with the semantics of what they depict. Among their many potential uses they provide means for more efficient accessibility of graphical data going beyond the traditional use of textual alternative descriptions, such as natural language interfaces. However, no efficient way of authoring these graphics currently exists. This paper aims to bridge the gap between authoring graphics and enhancing them with semantic formal structures in the form of ontologies by introducing Semantic Annotator for Inkscape (SAI), an authoring tool that allows for seamless addition of semantics to an SVG file supported by a given upper ontology in RDF format. The traditional disjointed approach of authoring a vector image and editing its supporting ontology using independent software tools has thus been unified into a single workspace, improving the efficiency of authoring semantically-enhanced graphics. Evaluation of SAI has shown greatly improved annotation times of semantically-enhanced graphics that can be later used for efficient non-visual natural-language-based content retrieval.