Authoring Semantic Annotations for Non-Visual Access to Graphics

PLHÁK, Jaromír, Tomas MURILLO-MORALES a Klaus MIESENBERGER. Authoring Semantic Annotations for Non-Visual Access to Graphics. Journal on Technology and Persons with Disabilities. California State University, Northridge, 2018, roč. 2018, č. 6, s. 399-414. ISSN 2330-4219.

Základní údaje

Originální název

Authoring Semantic Annotations for Non-Visual Access to Graphics

Autoři

PLHÁK, Jaromír (203 Česká republika, garant, domácí), Tomas MURILLO-MORALES (724 Španělsko) a Klaus MIESENBERGER (40 Rakousko)

Vydání

Journal on Technology and Persons with Disabilities, California State University, Northridge, 2018, 2330-4219

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Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Kód RIV

RIV/00216224:14330/18:00108835

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

Blind;Low Vision;Software;Ontology;SVG;Web

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Anotace

V originále

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.