PhD -'Design of an Ontological Framework for Capturing Knowledge Related to Business Models' F/M

about the role

Problem Statement

Transforming our business models is a necessary path to achieve Orange's net-zero carbon goals. However, it affects a vital dimension for the company: revenue and margin generation. In the absence of reference models, suitable transformation examples, and analytical tools, it is difficult to engage in a reflection on the changes to be made to our business models (e.g., subscription models with unlimited voice or data, specific networks for each operator versus shared networks, etc.).

How can we help the operator find effective and sustainable ways to achieve its economic objectives while integrating its impacts? How can a multidisciplinary approach, integrating an ontological formalization of business models, a systemic analysis process, and analysis support software, help a team responsible (and by extension, the operator) for an income statement identify objectives and means to evolve their business model while integrating key impacts for the sustainability of the activity?

Scientific Objective – Results and Challenges

The objective of the PhD thesis is to apply artificial intelligence methodologies and tools to the strategic analysis of a company's economic activity. Indeed, enterprise knowledge graphs are major assets for organizations, providing a framework conducive to the standardization of concepts and the emergence of effective information search methods. They can offer a framework for modeling business models. There is also an opportunity to use large language models to support the capture of knowledge from project stakeholders and facilitate the analysis and querying of business models.

The objectives of this PhD thesis are crystallized in the following tasks:

• Propose an ontological modeling for complex systemic analysis models covering multiple business domains by managing the use of vocabularies from cross-cutting themes. This task will involve capturing knowledge from business entities through questionnaires and interviews, and implementing methods for formalizing and developing relevant ontologies.
• Identify virtuous interactions between language models and enterprise knowledge graphs for effective knowledge capture and develop tools to populate an enterprise knowledge graph from business document corpora.
• Enhance the knowledge of the graph using GraphRAG techniques and the reasoning and planning capabilities of large language models to address the weaknesses of analytical tools across all dimensions of business models.