# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 7 Conclusions and Future Work  
The goal of conceptual modeling, the core of model-based systems engineering (MBSE), is to establish a framework that facilitates understanding of the problem space, synthesis of possible solutions, and analyses of identified solutions. Conceptual modeling creates a coherent representation of a system and its operating domain, including interactions with other systems and with the system's environment [48]. Indeed, MBSE brings much value to systems, with focus on their early lifecycle stages. Yet, when deep analyses that rely on the mathematical representation of the model are required, MBSE lacks rigorous, effective methods. In this work, we show a way to realize the potential of graph models [49] to benefit MBSE. Graph models are configurations of nodes and connections that occur in a plethora of applications. Graphs can represent both diverse physical networks, such as electrical circuits, roadways, or organic molecules, and interactions that might occur in ecosystems, databases, and flow of control in computer programs. Much research has been done on graph theory applications and graph query optimizations. There is also rich literature on conceptual modeling and its applications in MBSE methods, but research seeking to merge the two fields is scarce. Combining conceptual models with graphs can be highly effective for knowledge representation, as such a combination can provide valuable answers to complex questions and insights into the modeled system. This work is innovative in that it capitalizes on the synergy emanating from the integration of OPM-based conceptual models and graph databases (GDBs), specifically Neo4j. OPM enables rich formal knowledge representation in both diagrams and text, while Neo4j is based on the formality and robustness of graph theory. Put together, they synergistically make a powerful tool for knowledge management and systems analysis. We demonstrate that a single query or a combination of a few queries on a graph extracted from a conceptual OPM model can provide insights and conclusions that are not obvious or even impossible to draw using just a visual inspection of a non-trivial model. We present examples of querying system models from various disparate domains, including nuclear plants, household, IT, and air safety, showing the value of queries to gain a better understanding of new systems or to perform post-mortem analyses to explain past disasters, such as the Three Mile Island accident, to enhance these systems' robustness and avoid similar mishaps in the future. We present an integrated solution whereby the GDB capability is fully immersed in the OPM conceptual modeling tool OPCloud, presenting to the modeler a familiar view of OPM diagrams and the complementary textual OPL sentences in a subset of English or many other natural languages, catering to the dual channel processing multimedia assumption [50] and enhancing the user experience. In future work, we would like to examine the value of querying as a means to explain and answer questions about the model in the context of a large knowledge base that can be linked with the graph representation of the model, be it general or domain specific. This approach extends querying beyond the limited set of concepts defined by each model in isolation. A common definition of an artificial intelligence (AI) system is a system that is able \"to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation\" [51]. In the spirit of this definition, our goal is to use the querying approach presented in this work as a key component in AI applications. Specifically, we plan to use the graph representation of anOPM model as a key ingredient in UniKOM--Universal Knowledge Management--an innovative system idea that uses deep learning and language models to convert free natural language text to an OPM model. Conceptualization, D.D., D.M., and U.S.; methodology, D.D. and D.M. ; software, D.M. ; validation, D.M., U.S. and D.D.