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Petiya, Sean. "A Linked Open Data model for describing comic book sequences: Exploring semantic enrichment opportunities with graphic medicine." Art Libraries Journal 48. 3 2023. Accessed 12Oct. 2025. <https://www.cambridge.o ... BA7A97A0EB2A610FECC197C>. Added by: joachim (12/10/2025, 20:30) Last edited by: joachim (12/10/2025, 23:25) |
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| Resource type: Web Article Language: en: English Peer reviewed DOI: 10.1017/alj.2023.13 BibTeX citation key: Petiya2023 Email resource to friend View all bibliographic details  |
Categories: General Keywords: Digitalization, Illness, Medicine Creators: Petiya Collection: Art Libraries Journal |
Views: 15/461
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| Attachments | URLs https://www.cambri ... 7A0EB2A610FECC197C |
| Abstract |
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Applying a Linked Open Data (LOD) approach to modeling the visual structure and content of comic books and graphic novels enables the description of these works to be enhanced through the process of semantic enrichment. This strategy may be particularly impactful for graphic medicine, a non-exclusive genre of comics that communicate medical and healthcare information, including personal stories of illness. However, the metadata for these works may lack references to healthcare-related vocabulary, thesauri or ontology that would more precisely describe their contents. This material may include pages and panels that illustrate specific medical topics, such as symptoms, side-effects or treatments — subjects that often overlap with other healthcare challenges, including mental health and illness. Exploring an LOD approach for describing comics content may potentially enhance the discoverability of this material and its ability to be remixed and reused, and better connected to other information resources.
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