Exploring Meta-Modelling Languages to Improve Graph-based Systems for Complex Domain Modelling: A Study of ML2 and PURO Seminář KEG - Zekeri Adams

Knowledge graphs have emerged as powerful tools shaping the landscape of artificial intelligence and intelligent systems. Operating on a graph-based data model, they play a pivotal role in integrating, managing, and deriving value from diverse datasets on a large scale. The semantics of knowledge graphs goes beyond the structuring of objects and relationships in nodes and edges. Most real-life domains are complex where classes are instances of other classes. As a result of modelling by different experts in these complex domains, it has resulted in entity ambiguation where the same entity is modelled differently by knowledge engineering experts. Also, owing to the importance ontologies in structuring knowledge graphs as it provides consistency and efficient reasoning mechanism for humans and machines to make relevant inference in graph-based system, it is important of have a unified ontological framework for modelling in such complex domain as this will enhance semantics interoperability, data integration and visualization.

Our investigation delves into two meta-modelling languages, ML2 and PURO, both equipped with higher-order constructs tailored for modelling subject domains with intricate features. These languages are formalized in first-order logics, providing a structured framework to accommodate complexities often absent in graph-based models within such domains. Our objective is to synthesize the unique attributes of PURO and ML2 to create a comprehensive framework for modelling in knowledge graphs across complex domains. Through the utilization of the PURO modeler, we demonstrate the practical significance of these languages by identifying issues in multi-level taxonomic structures, using segments from Wikidata as illustrative examples.