A Graph Language Modeling Framework for the Ontological Enrichment of Conceptual Models

Abstract

Conceptual models (CMs) offer a structured way to organize and communicate information in information systems. However, current models lack adequate semantics of the terminology of the underlying domain model, leading to inconsistent interpretations and uses of information. Ontology-driven conceptual modeling languages provide primitives for articulating these domain notions based on the ontological categories, i.e., stereotypes put forth by upper-level (or foundational) ontologies. Existing CMs have been created using ontologically-neutral languages (e.g., UML, ER). Enriching these models with ontological categories can better support model evaluation, meaning negotiation, semantic interoperability, and complexity management. However, manual stereotyping is prohibitive, given the sheer size of the legacy base of ontologically-neutral models. In this paper, we present a graph language modeling framework for conceptual models that combines finetuning pre-trained language models to learn the vector representation of OntoUML models’ data and then perform a graph neural networks-based node classification that exploits the model’s graph structure to predict the stereotype of model classes and relations. We show with an extensive comparative evaluation that our approach significantly outperforms existing stereotype prediction approaches.