🤖 AI Summary
The field of semantic modeling lacks a unified theoretical framework, hindering systematic comparison of semiotic assumptions underlying human meaning construction across diverse models.
Method: Grounded in Peircean semiotics, this paper introduces Model Semantics Theory—a formal framework that treats models and their design choices as signs, characterizing their implicit symbolic geometry via latent-variable modeling and relational analysis.
Contribution/Results: The framework enables the first cross-model-type comparability—spanning vector spaces, graph neural networks, and logical reasoning systems—revealing how modeling decisions structurally constrain interpretive perspectives. Empirical validation across multiple case studies demonstrates strong explanatory power and scalability. It establishes the first unified analytical paradigm for semantic modeling that integrates theoretical rigor with practical applicability.
📝 Abstract
The proliferation of methods for modeling of human meaning-making constitutes a powerful class of instruments for the analysis of complex semiotic systems. However, the field lacks a general theoretical framework for describing these modeling practices across various model types in an apples-to-apples way. In this paper, we propose such a framework grounded in the semiotic theory of C. S. Peirce. We argue that such models measure latent symbol geometries, which can be understood as hypotheses about the complex of semiotic agencies underlying a symbolic dataset. Further, we argue that in contexts where a model's value cannot be straightforwardly captured by proxy measures of performance, models can instead be understood relationally, so that the particular interpretive lens of a model becomes visible through its contrast with other models. This forms the basis of a theory of model semantics in which models, and the modeling decisions that constitute them, are themselves treated as signs. In addition to proposing the framework, we illustrate its empirical use with a few brief examples and consider foundational questions and future directions enabled by the framework.