Mathieu D'aquin

Nicolas Hubert, Pierre Monnin, Mathieu d'Aquin et al.

Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education or medicine, access to public datasets is even more limited. To remedy the aforementioned issues, we release PyGraft, a Python-based tool that generates highly customized, domain-agnostic schemas and KGs. The synthesized schemas encompass various RDFS and OWL constructs, while the synthesized KGs emulate the characteristics and scale of real-world KGs. Logical consistency of the generated resources is ultimately ensured by running a description logic (DL) reasoner. By providing a way of generating both a schema and KG in a single pipeline, PyGraft's aim is to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML), or more generally KG processing. In graph-based ML in particular, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks. PyGraft is available at: https://github.com/nicolas-hbt/pygraft.

Annanda Sousa, Karen Young, Mathieu D'aquin et al.

Automatic Emotion Detection (ED) aims to build systems to identify users' emotions automatically. This field has the potential to enhance HCI, creating an individualised experience for the user. However, ED systems tend to perform poorly on people with Autism Spectrum Disorder (ASD). Hence, the need to create ED systems tailored to how people with autism express emotions. Previous works have created ED systems tailored for children with ASD but did not share the resulting dataset. Sharing annotated datasets is essential to enable the development of more advanced computer models for ED within the research community. In this paper, we describe our experience establishing a process to create a multimodal annotated dataset featuring children with a level 1 diagnosis of autism. In addition, we introduce CALMED (Children, Autism, Multimodal, Emotion, Detection), the resulting multimodal emotion detection dataset featuring children with autism aged 8-12. CALMED includes audio and video features extracted from recording files of study sessions with participants, together with annotations provided by their parents into four target classes. The generated dataset includes a total of 57,012 examples, with each example representing a time window of 200ms (0.2s). Our experience and methods described here, together with the dataset shared, aim to contribute to future research applications of affective computing in ASD, which has the potential to create systems to improve the lives of people with ASD.

Mathieu d'Aquin

Ontologies represent the conceptual knowledge of a domain. At the core of an ontology is the taxonomy of concepts and subconcepts that represent specific entities, which can be complex to build. In many cases, information is available in the form of records describing the characteristics of relevant entities, i.e., tabular data. Identifying patterns and similarities in such data can serve as a basis for identifying concepts and organizing them. However, doing so manually can be challenging, and purely automatic approaches, such as agglomerative clustering or relying on a large language model to analyze the data, can leave the user with overwhelming results and little control. In this paper, we describe a tool that enables the progressive and interactive construction of a taxonomy of concepts by identifying clusters as well as their intentional definitions. To do so, we rely on weighted self-organizing maps as a clustering method because they enable the creation of an arbitrary number of clusters that are distinct with respect to the distributions of values of specific characteristics of the clustered entities. We show that, by integrating this mechanism and others for rapidly creating concepts that group together instances from tabular data, this tool represents a middle ground between purely manual analysis and automatic methods for building ontological taxonomies.

Mathieu d'Aquin, Emmanuel Nauer

It has been reliably shown that the similarity of word embeddings obtained from popular neural models such as BERT approximates effectively a form of semantic similarity of the meaning of those words. It is therefore natural to wonder if those embeddings contain enough information to be able to connect those meanings through ontological relationships such as the one of subsumption. If so, large knowledge models could be built that are capable of semantically relating terms based on the information encapsulated in word embeddings produced by pre-trained models, with implications not only for ontologies (ontology matching, ontology evolution, etc.) but also on the ability to integrate ontological knowledge in neural models. In this paper, we test how embeddings produced by several pre-trained models can be used to predict relations existing between classes and properties of popular upper-level and general ontologies. We show that even a simple feed-forward architecture on top of those embeddings can achieve promising accuracies, with varying generalisation abilities depending on the input data. To achieve that, we produce a dataset that can be used to further enhance those models, opening new possibilities for applications integrating knowledge from web ontologies.

Barbara Gendron, Gaël Guibon, Mathieu d'Aquin

Conversational agents based on Large Language Models (LLMs) have recently emerged as powerful tools for human-computer interaction. Nevertheless, their black-box nature implies challenges in predictability and a lack of personalization, both of which can be addressed by controlled generation. This work proposes an end-to-end method to obtain modular and explainable control over LLM outputs through ontological definitions of aspects related to the conversation. Key aspects are modeled and used as constraints; we then further fine-tune the LLM to generate content accordingly. To validate our approach, we explore two tasks that tackle two key conversational aspects: the English proficiency level and the polarity profile of the content. Using a hybrid fine-tuning procedure on seven state-of-the-art, open-weight conversational LLMs, we show that our method consistently outperforms pre-trained baselines, even on smaller models. Beyond quantitative gains, the framework remains model-agnostic, lightweight, and interpretable, enabling reusable control strategies that can be extended to new domains and interaction goals. This approach enhances alignment with strategy instructions and demonstrates the effectiveness of ontology-driven control in conversational systems.

Barbara Gendron, Gaël Guibon, Mathieu D'aquin

The controllability of Large Language Models (LLMs) when used as conversational agents is a key challenge, particularly to ensure predictable and user-personalized responses. This work proposes an ontology-based approach to formally define conversational features that are typically qualitative in nature. By leveraging a set of linguistic descriptors, we derive quantitative definitions for qualitatively-defined concepts, enabling their integration into an ontology for reasoning and consistency checking. We apply this framework to the task of proficiency-level control in conversations, using CEFR language proficiency levels as a case study. These definitions are then formalized in description logic and incorporated into an ontology, which guides controlled text generation of an LLM through fine-tuning. Experimental results demonstrate that our approach provides consistent and explainable proficiency-level definitions, improving transparency in conversational AI.

Mathieu d'Aquin

Building taxonomies is often a significant part of building an ontology, and many attempts have been made to automate the creation of such taxonomies from relevant data. The idea in such approaches is either that relevant definitions of the intension of concepts can be extracted as patterns in the data (e.g. in formal concept analysis) or that their extension can be built from grouping data objects based on similarity (clustering). In both cases, the process leads to an automatically constructed structure, which can either be too coarse and lacking in definition, or too fined-grained and detailed, therefore requiring to be refined into the desired taxonomy. In this paper, we explore a method that takes inspiration from both approaches in an iterative and interactive process, so that refinement and definition of the concepts in the taxonomy occur at the time of identifying those concepts in the data. We show that this method is applicable on a variety of data sources and leads to taxonomies that can be more directly integrated into ontologies.

Mathieu d'Aquin

In sufficiently complex tasks, it is expected that as a side effect of learning to solve a problem, a neural network will learn relevant abstractions of the representation of that problem. This has been confirmed in particular in machine vision where a number of works showed that correlations could be found between the activations of specific units (neurons) in a neural network and the visual concepts (textures, colors, objects) present in the image. Here, we explore the use of self-organizing maps as a way to both visually and computationally inspect how activation vectors of whole layers of neural networks correspond to neural representations of abstract concepts such as `female person' or `realist painter'. We experiment with multiple measures applied to those maps to assess the level of representation of a concept in a network's layer. We show that, among the measures tested, the relative entropy of the activation map for a concept compared to the map for the whole data is a suitable candidate and can be used as part of a methodology to identify and locate the neural representation of a concept, visualize it, and understand its importance in solving the prediction task at hand.

Andrea Barraza-Urbina, Mathieu d'Aquin

Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS Task Environment, identify the differences between Environments, datasets and simulations; and most importantly, understand what needs to be shared about Environments to achieve reproducible experiments. The work presents itself as valuable initial groundwork, open to discussion and extensions.