Generative Zero-Shot Learning approach (GZSL) has demonstrated significant potential in 3D point cloud semantic segmentation tasks. GZSL leverages generative models like GANs or VAEs to synthesize realistic features (real features) of unseen classes. This allows the model to label unseen classes during testing, despite being trained only on seen classes. In this context, we introduce the Generalized Zero-Shot Learning based-upon Mixture-of-Experts (GZSL-MoE) model. This model incorporates Mixture-of-Experts layers (MoE) to generate fake features that closely resemble real features extracted using a pre-trained KPConv (Kernel Point Convolution) model on seen classes. The main contribution of this paper is the integration of Mixture-of-Experts into the Generator and Discriminator components of the Generative Zero-Shot Learning model for 3D point cloud semantic segmentation, applied to the COVERED dataset (CollabOratiVE Robot Environment Dataset) for Human-Robot Collaboration (HRC) environments. By combining the Generative Zero-Shot Learning model with Mixture-of- Experts, GZSL-MoE for 3D point cloud semantic segmentation provides a promising solution for understanding complex 3D environments, especially when comprehensive training data for all object classes is unavailable. The performance evaluation of the GZSL-MoE model highlights its ability to enhance performance on both seen and unseen classes. Keywords Generalized Zero-Shot Learning (GZSL), 3D Point Cloud, 3D Semantic Segmentation, Human-Robot Collaboration, COVERED (CollabOratiVE Robot Environment Dataset), KPConv, Mixture-of Experts

Animating a Knowledge Base: From Ontologies to Generative AI Models From Expert Systems and the Semantic W eb to Generative AI: Model - Driven and Data - Driven Approaches in Area Studies In a context where the social sciences and humanities are experimenting with non - anthropocentric analytical frames, this article proposes a semiotic (structural) reading of the hybridization between symbolic AI and neural (or sub - symbolic) AI based on a field of application: the design and use of a knowledge base for area studies. W e describe the LaCAS ecosystem - Open Archives in Linguistic and Cultural Studies (thesaurus; RDF/OWL ontology; LOD services; harvesting; expertise; publication), deployed at Inalco (National Institute for Oriental Languages and Civilizations) in Paris with the Okapi (Open Knowledge and Annotation Interface) software environment from Ina (National Audiovisual Institute), which now has around 160,000 documentary r esources and ten knowledge macro - domains grouping together several thousand knowledge objects. W e illustrate this approach using the knowledge domain "Languages of the world" (~540 languages) and the knowledge object "Quechua (language)". On this basis, we discuss the controlled integration of neural tools, more specifically generative tools, into the life cycle of a knowledge base: assistance with data localization/qualification, index extraction and aggregation, property suggestion and testing, dynamic file generation, and engineering of contextualized prompts (generic, contextual, explanatory, adjustment, procedural) aligned with a domain ontology. W e outline an ecosystem of specialized agents capable of animating the database while respe cting its symbolic constraints, by articulating model - driven and data - driven methods .

1) Introduction and Conceptual Framework: This document explores the concept of information design by dividing it into two major practices: defining the meaning of a corpus of textual data and its visual or multimodal representation. It draws on expertise in enriching textual corpora, particularly audiovisual ones, and transforming them into multiple narrative formats. The text highlights a crucial distinction between the semantic content of a domain and the modalities of its graphic expression, illustrating this approach with concepts rooted in structural semiotics and linguistics traditions. 2) Modeling and Conceptual Design: The article emphasizes the importance of semantic modeling, often achieved through conceptual networks or graphs. These tools enable the structuring of knowledge within a domain by accounting for relationships between concepts, contexts of use, and specific objectives. Stockinger also highlights the constraints and challenges involved in creating dynamic and adaptable models, integrating elements such as thesauri or interoperable ontologies to facilitate the analysis and publication of complex corpora. 3) Applications and Multimodal Visualization: The text concludes by examining the practical application of these models in work environments like OKAPI, developed to analyze, publish, and reuse audiovisual data. It also discusses innovative approaches such as visual storytelling and document reengineering, which involve transforming existing content into new resources tailored to various contexts. These methods emphasize interoperability, flexibility, and the intelligence of communication systems, paving the way for richer and more collaborative use of digital data. The content of this document was presented during the "Semiotics of Information Design" Day organized by Anne Beyaert-Geslin of the University of Bordeaux Montaigne (MICA laboratory) on June 21, 2018, in Bordeaux.

In this study, we propose a corpus analysis of an area of the French lexicon that is both dense and highly structured: the vocabulary of family relationships. Starting with a lexicon of 25 nouns designating the main relationships (son, cousin, mother, grandfather, sister-in-law etc.), we examine how these terms are positioned in relation to each other through distributional analyses based on the use of these terms in corpora. We show that distributional information can capture certain features that organize this vocabulary (descent, alliance, siblings, genre), in ways that vary according to the different corpora compared.

The digitisation campaigns carried out by libraries and archives in recent years have facilitated access to documents in their collections. However, exploring and exploiting these documents remain difficult tasks due to the sheer quantity of documents available for consultation. In this article, we show how the semantic annotation of the textual content of study corpora of archival documents allow to facilitate their exploitation and valorisation. First, we present a methodological framework for the construction of new interfaces based on textual semantics, then address the current technological obstacles and their potential solutions. We conclude by presenting a practical case of the application of this framework Keywords.

In the present paper, we argue that Terminological Knowledge Bases (TKB) are all the more useful for addressing various needs as they do not fulfill formal criteria. Moreover, they intend to clarify the terminology of a given domain by illustrating term uses in various contexts. Thus we designed a TKB structure including 3 linked features: terms, concepts and texts, that present the peculiar use of each term in the domain. Note that concepts are represented into frames whose non-formal description is standardized. Associated with this structure, we defined modeling criteria at the conceptual level. Finaly, we discuss the situation of TKB with regard to ontologies, and the use of TKB for the development of AI systems.

This work is done as part of a research master's thesis project. The goal is to generate SPARQL queries based on user-supplied keywords to query RDF graphs. To do this, we first transformed the input ontology into an RDF graph that reflects the semantics represented in the ontology. Subsequently, we stored this RDF graph in the Neo4j graphical database to ensure efficient and persistent management of RDF data. At the time of the interrogation, we studied the different possible and desired interpretations of the request originally made by the user. We have also proposed to carry out a sort of transformation between the two query languages SPARQL and Cypher, which is specific to Neo4j. This allows us to implement the architecture of our system over a wide variety of BD-RDFs providing their query languages, without changing any of the other components of the system. Finally, we tested and evaluated our tool using different test bases, and it turned out that our tool is comprehensive, effective, and powerful enough.

We are developing a system that aims to perf orm the same inferences as a human reader, on car-crash reports. More precisely, we expect it to determine the causes of the accident as they appear from the text. We describe the genera l semantic framework in which our study takes place, the linguistic and semantic levels of analysis, and the inference rules used by the system.

We show in this paper that, on the one hand, named entities can be designated using different denominations and that, on the second hand, names denoting named entities are polysemous. The analysis cannot be limited to reference resolution but should take into account naming strategies, which are mainly based on two linguistic operations: synecdoche and metonymy. Lastly, we present a model that explicitly represents the different denominations in discourse, unifying the way to represent linguistic knowledge and world knowledge.

Diagrammatic, analogical or iconic representations are often contrasted with linguistic or logical representations, in which the shape of the symbols is arbitrary. The aim of this paper is to make a case for the usefulness of diagrams in inferential knowledge representation systems. Although commonly used, diagrams have for a long time suffered from the reputation of being only a heuristic tool or a mere support for intuition. The first part of this paper is an historical background paying tribute to the logicians, psychologists and computer scientists who put an end to this formal prejudice against diagrams. The second part is a discussion of their characteristics as opposed to those of linguistic forms. The last part is aimed at reviving the interest for heterogeneous representation systems including both linguistic and diagrammatic representations.