Language and Knowledge Representation: A Stratified Approach

2025-04-14

AI Summary

This paper addresses the fundamental challenge of representational heterogeneity—i.e., inconsistent encodings of the same reality arising from hierarchical differences across observers in conceptualization, language, knowledge, and data. To resolve this, we propose a hierarchical unification modeling framework. We introduce, for the first time, a formal four-level representation system: “concept–language–knowledge–data.” We further propose *teleontology*, a novel paradigm for language and knowledge modeling that replaces static ontologies with goal-oriented, purpose-driven representations. We develop kTelos, an iterative methodology enabling end-to-end integration across all four levels. Validated on the Universal Knowledge Core (UKC) and LiveKnowledge catalog systems, our framework has been instantiated in two international projects—DataScientia (a data cataloging initiative) and JIDEP (a materials modeling platform)—yielding reusable prototypes. Empirical results demonstrate substantial improvements in cross-domain semantic interoperability and knowledge reuse capability.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• Addresses representation heterogeneity in language and knowledge
• Proposes stratified formalism for concept, language, knowledge, data levels
• Develops top-down solutions using UKC and teleontology methods

Innovation

• Stratified representation formalism for heterogeneity
• Top-down language representation using UKC
• kTelos methodology integrates solution components