Une mesure de similarité sémantique utilisant des résultats de psychologie

National audienceL'utilisation d'ontologies, c'est-à-dire de bases de connaissances, en recherche d'information est devenue une voie très explorée. Cela permet de dépasser de nombreux problèmes liés aux comparaisons terme à terme entre documents ou entre documents et requêtes, en passant à un niveau d'abstraction supérieur qui n'est pas soumis aux limitations intrinsèques à l'utilisation de mots-clés. De nombreuses techniques utilisent désormais les ontologies (expansion de requêtes, désambiguïsation sémantique, etc.) dans le but d'obtenir de meilleurs résultats en recherche d'information. Un problème récurrent de ces applications est la mesure de proximité entre concepts dans une ontologie. Elle a été étudiée par de nombreux auteurs, et deux grandes approches se sont détachées : les approches basées sur les arcs, c'est-à-dire sur la structure de l'ontologie, et les approches utilisant le contenu informatif des concepts, donc en passant par des corpus renseignant l'importance des concepts dans un document. Nous avons eu besoin de comparer les mesures classiques de distance entre concepts dans une ontologie. Des résultats de psychologie nous ont amenés à en choisir une qui respecte plus la manière dont un humain juge la proximité entre entités

Echange d'Information grâce à des caractérisations sémantiques

National audienceUsing ontologies allows agents to put meaning behind the terms appearing in the information exchanges. Supporting us on this fact, we propose an entity named focus allowing to represent various kinds of information contents : documents, data bases, services, agents' capabilities, etc. A focus consists of a weighting of the concepts of an ontology in order to indicate which meanings are important for the agent which has set it up. To help the capture of the values in the focus we present a procedure to spread the weightings in the focus. Then we define a measure of relevance of a focus compared to another. The originality of our approach lies in the fact that the focus is an exchangeable entity between the agents and does not require a centralization of the data collections

What's left can't be right -- The remaining positional incompetence of contrastive vision-language models

Contrastive vision-language models like CLIP have been found to lack spatial understanding capabilities. In this paper we discuss the possible causes of this phenomenon by analysing both datasets and embedding space. By focusing on simple left-right positional relations, we show that this behaviour is entirely predictable, even with large-scale datasets, demonstrate that these relations can be taught using synthetic data and show that this approach can generalise well to natural images - improving the performance on left-right relations on Visual Genome Relations

Query interpretation to help peers understand each others in semantically heterogeneous systems

National audienceIn semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the ``shared'' parts of the ontologies) but, in some sense, limited to them. Then, how the ``unshared'' parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared

Improving Interoperability Using Query Interpretation in Semantic Vector Spaces

International audienceIn semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the ``shared'' parts of the ontologies) but, in some sense, limited to them. Then, how the ``unshared'' parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared

Mutations des collections numériques à l’heure du web de données (Les)

Mémoire du master Archives numériques portant sur le développement du web de données et son impact sur les collections numériques

A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval

Medical image retrieval could be based on the text describing the image as the caption or the title. The use of text terms to retrieve images have several disadvantages such as term-disambiguation. Recent studies prove that representing text into semantic units (concepts) can improve the semantic representation of textual information. However, the use of conceptual representation has other problems as the miss or erroneous semantic relation between two concepts. Other studies show that combining textual and conceptual text representations leads to better accuracy. Popularly, a score for textual representation and a score for conceptual representation are computed and then a combination function is used to have one score. Although the existing of many combination methods of two scores, we propose in this paper a new combination method based on adaptive version of the genetic algorithm. Experiments are carried out on Medical Information Retrieval Task of the ImageCLEF 2009 and 2010. The results confirm that the combination of both textual and conceptual scores allows best accuracy. In addition, our approach outperforms the other combination methods

Towards the extraction of robust sign embeddings for low resource sign language recognition

Isolated Sign Language Recognition (SLR) has mostly been applied on datasets containing signs executed slowly and clearly by a limited group of signers. In real-world scenarios, however, we are met with challenging visual conditions, coarticulated signing, small datasets, and the need for signer independent models. To tackle this difficult problem, we require a robust feature extractor to process the sign language videos. One could expect human pose estimators to be ideal candidates. However, due to a domain mismatch with their training sets and challenging poses in sign language, they lack robustness on sign language data and image-based models often still outperform keypoint-based models. Furthermore, whereas the common practice of transfer learning with image-based models yields even higher accuracy, keypoint-based models are typically trained from scratch on every SLR dataset. These factors limit their usefulness for SLR. From the existing literature, it is also not clear which, if any, pose estimator performs best for SLR. We compare the three most popular pose estimators for SLR: OpenPose, MMPose and MediaPipe. We show that through keypoint normalization, missing keypoint imputation, and learning a pose embedding, we can obtain significantly better results and enable transfer learning. We show that keypoint-based embeddings contain cross-lingual features: they can transfer between sign languages and achieve competitive performance even when fine-tuning only the classifier layer of an SLR model on a target sign language. We furthermore achieve better performance using fine-tuned transferred embeddings than models trained only on the target sign language. The embeddings can also be learned in a multilingual fashion. The application of these embeddings could prove particularly useful for low resource sign languages in the future