Semantic Tension Language (STL): A Theoretical Framework for Structured and Interpretable Knowledge Representation

This paper introduces the Semantic Tension Language (STL), an original framework developed by the author to represent and interpret meaning through structured semantic relations.
STL proposes a dynamic model that bridges symbolic logic and neural representation by encoding tension, anchoring, and modulation as formal elements of cognition.
The paper outlines the theoretical foundation, structural syntax, and interpretive mechanisms of STL, demonstrating how it enables interpretable knowledge representation and reflexive AI architectures.
This work also discusses connections to RDF triple models and symbolic–connectionist integration, offering a new approach toward structural semantics and machine understanding.
Supplementary materials and the open-source implementation are available in the Semantic Tension Language GitHub repository