GOMME: A Generic Ontology Modelling Methodology for Epics

Ontological knowledge modelling of epic texts, though being an established research arena backed by concrete multilingual and multicultural works, still suffers from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most...

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Veröffentlicht in:Journal of information science theory and practice Jg. 11; H. 1; S. 61 - 78
Hauptverfasser: Varadarajan, Udaya, Tiwari, Amit, Bagchi, Mayukh, Satija, M P
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Daejeon Korea Institute of Science and Technology Information 01.03.2023
한국과학기술정보연구원
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ISSN:2287-9099, 2287-4577
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Zusammenfassung:Ontological knowledge modelling of epic texts, though being an established research arena backed by concrete multilingual and multicultural works, still suffers from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. This paper presents, as a unified solution to the above shortcomings, the design and development of GOMME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GOMME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms, and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GOMME is validated via a first brief implementation of ontological modelling of the Indian epic Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GOMME-produced model being both ontologically thorough and competent performance-wise.
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ISSN:2287-9099
2287-4577
DOI:10.1633/JISTaP.2023.11.1.5