Research

Publications · ORCID · dblp · HAL · theses.fr

Affiliation: associate researcher, SIG team (IRIT), in collaboration with Olivier Teste.

Research areas

1. Ontology alignment and explainability

My current research mainly focuses on ontology merging and knowledge alignment, in particular within the framework of the international OAEI (Ontology Alignment Evaluation Initiative) campaign.

Beyond algorithmic and formal aspects, I am especially interested in issues related to explainability and understanding the results produced by automated systems:

• how to explain a correspondence or alignment produced by an algorithm;
• how to verify or justify a model resulting from machine learning;
• how to make these results interpretable and usable by humans, especially in sensitive or critical contexts.

These topics, which lie at the intersection of data mining and knowledge engineering, are nowadays often associated (in a deliberately simplified way) with what is commonly referred to as artificial intelligence (AI).
However, my interest lies less in the buzzword than in the understanding, control, and responsibility of the systems that emerge from it.

2. Accessibility, disability and AI systems

My PhD dealt with text entry for visually impaired users; accessibility has never left my work since, and since January 2025 it has become personal as well. I now work on the way generative AI systems handle disability: which guardrails they apply, to whom, and with what asymmetries; how those design choices concretely affect the people involved; and how to design digital tools that are simple, robust and genuinely usable by everyone.

This area relies on an experimental approach: paired prompts, annotated datasets, reproducible figures. See in particular Gemini, ChatGPT, Claude and disability.

3. Generative AI and engineering education

As a teacher in an engineering school, I am interested in how students and teachers actually use generative AI, in its limits, and in the responsibilities it entails in education. See Training Engineers in the Age of AI and Two hours in Baybay.

Background

PhD (2017)

During my PhD, I worked on issues related to disability, particularly visual impairment and text input on mobile devices.
My research aimed at better understanding the real difficulties faced by users, and at proposing more accessible interaction methods, taking cognitive, motor, and perceptual constraints into account.

This work followed a user-centered approach, combining experimentation, prototyping, and evaluation, with particular attention paid to the robustness and simplicity of interactions.
PhD supervised by Mathieu Raynal (IRIT).

Internship at EPFL (LIA)

Before that, my first research experience focused on sentiment analysis (emotion mining), during an internship at the LIA laboratory of EPFL.

This work addressed the analysis of online social interactions, combining network analysis and textual content analysis.
In particular, it highlighted the limitations of purely topological approaches (centrality, degree, etc.) for characterizing user behavior, and demonstrated the value of incorporating information derived from the emotional content of messages.

Special attention was given to the detection of antagonistic behaviors and their impact on community dynamics, opening perspectives toward behavior analysis, automatic moderation, and the study of complex social interactions.

Contact

For any question or collaboration related to research, you can contact me at the following address:

📧 Philippe.Roussille@irit.fr