Health data sharing between Health Information Systems (HIS) is a challenge for the coordination of care and biomedical research. Semantic interoperability enables the sharing of the meaning of health data when the exchange is based on computer interpretable representations. It relies on the development of reference systems - information models ("templates") and terminologies - allowing a formal representation of health data based on international standards. Operational emergence of semantic interoperability primarily depends on HIS’ ability to integrate these systems of reference and also the adoption by healthcare professionals, of solutions to capture clinical information based on adapted user interfaces. As such, the interface terminologies should be taken into account and also their formal representation using the reference terminologies. The objective of the Tersan project is the development of solutions for semantic annotation of clinical data structures (ordered or structured results documents for laboratory, pathology, radiology, etc.) according to systems of reference for semantic interoperability enabling their sharing and exploitation by HIS from distinct health facilities. For this, we need to develop a management platform for systems of reference, and implement tools and services that enable their integration within the HIS. The innovative characteristic of the project is to manage within this platform, in addition to health reference terminologies (such as ICD10/ICD11, ADICAP, LOINC, SNOMED 3.5 VF, CCAM, etc.), other types of semantic resources (models of data structures ("templates") and interface terminologies) which are essential to the emergence of an effective and large scale use of these reference terminologies. It is planned, in respect of governance of the ASIP Santé, to contribute to the implementation of a strategy for production, maintenance and sustainable distribution of national systems of reference for semantic interoperability. Operationally, the Tersan project will demonstrate, in the field of laboratory, pathology or radiology orders, that synchronization between HIS of systems of reference including interface terminologies, allows easy implementation of cross institutions processes (such as ordering an investigation in a health facility, making it in a second one, and integrating the results of this investigation in a third one). In addition, the project will demonstrate that health data entry in accordance with the systems of reference will allow, in every institution, standardized exploitation for personalized access to medical knowledge, clinical guidelines or ongoing clinical trials. The issues addressed in this project require research in the field of Information and Communication Technologies (ICT) (knowledge engineering, model-driven engineering, terminology and ontology engineering, information retrieval) to be conducted in a multidisciplinary context (health professionals, information science specialist, knowledge engineers, and terminologists).

The HYBRINFOX project aims to contribute to the fight against online misinformation by studying and developing possible synergies between symbolic AI and deep learning approaches for the detection of fake news (aka. infox). The main lever is the identification of vague information, likely to introduce or promote bias (subjectivity, evaluativity). This project is a continuation of a RAPID program entitled DIEKB ('Disinformation Identification in Evolving Knowledge Bases', 2019-2022) between the CoLoR team at INSTITUT JEAN-NICOD (Paul Egré, Benjamin Icard, Thomas Souverain), MONDECA (Ghislain Atemezing) and AIRBUS (Sylvain Gatepaille, Guillaume Gadek, Souhir Gabiche, Paul Guélorget). This research produced promising results whose success calls for new resources and for scaling up (funding of two postdocs, integration of the current prototypes). This development justifies in particular the association of a new partner, the LinkMedia team of IRISA (represented by Vincent Claveau), specialized in deep learning and automatic language processing for the identification of fake news. The leading hypothesis behind this project is that some lexical markers of semantic vagueness, in particular evaluative adjectives, which favor subjective interpretations, constitute a relevant cue of the potentially false, biased, or unreliable character of some texts. This hypothesis was tested end of 2021 with the development of a symbolic AI algorithm, the VAGO tool, and by comparing it with a deep learning based algorithm, the FAKE-CLF classifier. The VAGO tool provides a measure of the vagueness versus precision of a text, and the subjectivity (opinion) versus objectivity (factual character) of a text. Comparison with the results of the FAKE-CLF classifier shows a positive correlation between subjectivity scores measured by VAGO and falsity scores predicted by FAKE-CLF. This result opens up several avenues of hybridization between the two methods, which the HYBRINFOX program proposes to develop. The ambition of the project is both scientific and industrial: first, we aim to make the deep learning method exemplified in classifiers like FAKE-CLF explicable through symbolic AI and the use of explicit semantic rules. Then, the goal is to leverage the symbolic AI method developed with VAGO to improve the performance of the deep learning models, and conversely to enrich the lexicon of VAGO as the underlying typology in order to refine the identification of textual falsity cues. Finally, the goal is to better define the boundary between truthful and non-verbatim uses of linguistic vagueness in discourse, by training and testing deep learning-based algorithms on more or less vague or precise corpora. By associating research partners (IJN, IRISA) and industrial partners (Mondeca, Airbus), the project will test the developed tools on novel use cases including for defense applications.

The Internet of Things connects physical devices offering sensing or actuating with their vicinity. The ever-growing capabilities of devices allow to imagine new architectures including them as first class citizens. New added-value applications can then be envisioned in smart agriculture, smart buildings, smart cities, energy and water management, e-health and ageing well... The Web of Things (WoT) allows to describe the devices semantics, bridging the gap between the different domain and service descriptions. In today WoT architectures, physical devices can be located at distance from systems that perform reasoning. A centralised approach does not take advantage of the devices capabilities and induces suboptimal data transfers as well as server overload. Besides, many devices are now smart enough to discover each other, exchange data, and collectively make decisions. CoSWoT objectives are to propose a distributed WoT-enabled software architecture embedded on constrained devices with two main characteristics: (1) it will use ontologies to specify declaratively the application logic of devices and the semantics of the exchanged messages; (2) it will add reasoning functionalities to devices, so as to distribute processing tasks among them. Doing so, the development of applications including devices of the WoT will be highly simplified: our platform will enable the development and execution of intelligent and decentralised smart WoT applications despite the heterogeneity of devices. In CoSWoT, WoT applications will rely on a platform hosting the base services. Besides traditional services, it will host extensions that correspond to two scientific barriers: (1) the use of ontologies as a generalised model for exchanges between heterogeneous devices. A joint statement from AIOTI WG3, IEEE P2413, oneM2M, W3C positions ontologies as key enablers for semantic interoperability on the WoT. However research questions remain concerning (i) the adequation of existing ontologies to the target application domains; (ii) the applicability of theoretical principles developed in a variety of protocols and standards, in the context of data streams; (iii) the discovery of heterogeneous devices, their services and how to solicit them. (2) distributed and embedded incremental reasoning. Devices become powerful enough to offer storage and processing; new architectures appear, based on edge computing including devices such as sensors and actuators. The data streams provided by sensors require to perform incremental reasoning tasks. Research questions remain on (i) how to embed reasoning in devices with various capacities, it requires specific optimisations; (ii) how to efficiently distribute reasoning tasks among devices. Smart agriculture is a typical application domain of such WoT architectures, where the surveillance of cultivated fields requires various sensors that push streaming data, which must be collected and reasoned upon to take decisions executed by actuators. Smart buildings is another such typical application domain where added-value application services involve other verticals such as energy management, e-health, or ageing well. We will define use cases and requirements for smart agriculture and smart buildings, run simulations, and then lead real experiments. The CoSWoT platform will foster the decoupling of the development of software and the development of hardware, so as to ease the emergence of a new economic sector in the digital industry around WoT applications development, disconnected from the development of the smart devices themselves.