GENOME：一种面向史诗本体建模的通用方法论
GENOME: A GENeric methodology for Ontological Modelling of Epics
AI总结：
本文提出GENOME，首个专门针对史诗迭代式本体建模的通用方法论，融合跨学科规范并支持复用或从零建模，通过《摩诃婆罗多》案例验证其本体完备性与性能有效性。
摘要：
史诗的本体知识建模虽然是一个已有具体多语言和多文化作品支撑的成熟研究领域，但仍存在两个关键不足。首先，迄今为止开发的所有史诗本体模型都是按照临时性方法论设计的，最常见的是将现有的通用本体开发方法论进行组合。其次，这些临时性方法论都没有考虑在可用的情况下，通过复用现有史诗本体模型来进行丰富。本文针对上述不足提出了一个统一解决方案，即GENOME的设计与开发——这是首个专门用于史诗迭代式本体建模的方法论，并且有可能推广到数字人文领域不同研究方向的工作中。GENOME建立在跨学科基础之上，包括史诗的规范准则、知识建模最佳实践、应用可满足性规范以及认知生成性问题。它也是首个（不仅在史诗建模领域，而且在一般意义上）具有足够灵活性的方法论，能够在实践中整合通过复用或从零开始进行知识建模的两种选择。GENOME的可行性通过一个简短的首次实施得到验证，即通过复用现有本体对印度史诗《摩诃婆罗多》进行本体建模。初步结果令人鼓舞，GENOME生成的模型在本体论完备性和性能胜任度方面均表现良好。
英文摘要：
Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer 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. The paper presents, as a unified solution to the above shortcomings, the design and development of GENOME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GENOME 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 GENOME 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 GENOME-produced model being both ontologically thorough and performance-wise competent
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
