Towards Conceptual Modeling Semantics: Eventizing Tarski's Truth Schema

TL;DR

This paper explores formal semantics for a simple diagrammatic modeling language called the thinging machine, inspired by Tarski's truth schema, aiming to improve understanding and applicability of conceptual models.

Contribution

It introduces a novel semantic framework for the thinging machine model, extending Tarski's truth schema to enhance conceptual modeling semantics.

Findings

Initial results indicate the viability of the semantic approach.

The model facilitates deeper understanding of the diagrammatic language.

The approach opens new questions for formal semantics in modeling languages.

Abstract

Modeling languages in software engineering (e.g., UML) evolved from software systems modeling where denotational and operational kinds of semantics are the traditional subjects of research and practice. According to some authors, although a large portion of the static semantics (e.g., UML) seems to have reached a consensus, the dynamic semantics of activities, interactions, and state machines poses a major challenge. Central to semantics is the relationship between a sentence and the (actual) world. Carefully examining semantics-related issues in the modeling languages field to avoid problems that may affect practical applicability is important. One effort in this direction is OMG s release of a 2020 draft specification for Foundational UML (fUML), with the base semantics specifying executions that are executable in the same sense as a program in a traditional programming language. Additionally, efforts within academia have sought to develop an alternative approach to modeling languages using formal semantics (e.g., using Russell s theory of types and Tarski s declarative semantics). This paper aims at a similar exploratory venture of developing semantics, only for a much more modest diagrammatic modeling language, called the thinging machine model. The model promotes a deep understanding of the scrutinized modeling language and leads to considerably fruitful questions. Constructing the thinging machine model seems to facilitate progress in this direction, and the initial results in this paper indicate the viability of the approach.