提出分层表征框架，解决多视角知识表达差异问题。
Language and Knowledge Representation: A Stratified Approach
- 构建概念、语言、知识、数据四层表征结构，容纳表达异质性。
- 用通用知识核心与领域语言统一语言与概念层面差异。
- 通过kTelos方法实现可迭代复用的知识表征，适用于科研项目。
本论文提出表征异质性问题，指出不同观察者会以分层方式使用不同概念、语言与知识来编码同一现实。为此提出自上而下解决方案：（一）建立涵盖概念、语言、知识和数据四层的表征形式；（二）采用通用知识核心（UKC）、UKC命名空间与领域语言应对概念与语言异质性；（三）引入语言目的论与知识目的论处理知识层面差异；（四）利用现有LiveKnowledge目录推动语言与知识表征的迭代复用与共享；（五）提出kTelos方法整合上述组件，实现无异质性语言与知识表征生成。论文还展示了在两个国际项目——DataScientia（数据目录）与JIDEP（材料建模）中的原型应用，并展望未来研究方向。
原文摘要 · Abstract (English)
The thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observers encode different representations of the same target reality in a stratified manner using different concepts, language and knowledge (as well as data). The thesis then advances a top-down solution approach to the above stratified problem of representation heterogeneity in terms of several solution components, namely: (i) a representation formalism stratified into concept level, language level, knowledge level and data level to accommodate representation heterogeneity, (ii) a top-down language representation using Universal Knowledge Core (UKC), UKC namespaces and domain languages to tackle the conceptual and language level heterogeneity, (iii) a top-down knowledge representation using the notions of language teleontology and knowledge teleontology to tackle the knowledge level heterogeneity, (iv) the usage and further development of the existing LiveKnowledge catalog for enforcing iterative reuse and sharing of language and knowledge representations, and, (v) the kTelos methodology integrating the solution components above to iteratively generate the language and knowledge representations absolving representation heterogeneity. The thesis also includes proof-of-concepts of the language and knowledge representations developed for two international research projects - DataScientia (data catalogs) and JIDEP (materials modelling). Finally, the thesis concludes with future lines of research.
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