Agent-based semantic composition of Web services using distributed description logics

International audienceAn important research challenge consists in composing web services in an automatic and distributed manner on a large scale. Indeed, most queries can not be satisfiable by one service and must be processed by composing several services. Each web service is often written by different designers and is described using the terms of their own ontology. Therefore, the composition process needs to deal with a variety of heterogeneous ontologies. In order to tackle this challenge, we propose an approach using Distributed Description Logics (DDL) to achieve the semantic composition of web services. DDL allows one to make semantic connections between ontologies and thus web services, as well as to reason to get a semantic composition of web services

Indexing Uncertain Categorical Data over Distributed Environment

International audienceToday, a large amount of uncertain data is produced by several applications where the management systems of traditional databases incuding indexing methods are not suitable to handle such type of data. In this paper, we propose an inverted based index method for effciently searching uncertain categorical data over distributed environments. We adress two kinds of query over the distributed uncertain databases, one a distributed probabilis-tic thresholds query, where all results sastisfying the query with probablities that meet a probablistic threshold requirement are returned, and another a distributed top k-queries, where all results optimizing the transfer of the tuples and the time treatment are returned

Personal information privacy: what's next?

In recent events, user-privacy has been a main focus for all technological and data-holding companies, due to the global interest in protecting personal information. Regulations like the General Data Protection Regulation (GDPR) set firm laws and penalties around the handling and misuse of user data. These privacy rules apply regardless of the data structure, whether it being structured or unstructured. In this work, we perform a summary of the available algorithms for providing privacy in structured data, and analyze the popular tools that handle privacy in textual data; namely medical data. We found that although these tools provide adequate results in terms of de-identifying medical records by removing personal identifyers (HIPAA PHI), they fall short in terms of being generalizable to satisfy nonmedical fields. In addition, the metrics used to measure the performance of these privacy algorithms don't take into account the differences in significance that every identifier has. Finally, we propose the concept of a domain-independent adaptable system that learns the significance of terms in a given text, in terms of person identifiability and text utility, and is then able to provide metrics to help find a balance between user privacy and data usability

Towards Big Data in Medical Imaging

National audienceWe present our vision to implement a big medical imaging platform to improve medical diagnosis. We aim to link multi-scale and multimodal images through open data and ontologies to discover new correlations and scientific knowledges. The platform is based on CIRRUS, a Sorbonne-Paris-Cité private cloud for research

Utilisation des Topic Maps pour l\u27interrogation et la génération de documents virtuels : Application au domaine médical

Le travail mené dans le cadre de cette thèse repose sur les deux disciplines suivantes : le Web sémantique et les systèmes d\u27information intelligents. Le système que nous proposons permet à l\u27utilisateur d\u27explorer des données multisources pour rechercher l\u27information avec un moindre effort. Nous avons utilisé les Topic Maps comme formalisme central dans notre conception, notamment pour la représentation et l\u27organisation des données et ensuite pour construire l\u27interface d\u27interrogation. Les données, une fois fusionnées, sont présentées à l\u27utilisateur par une interface intelligente pour être explorées par navigation. Cette interface est adaptée au profil de l\u27utilisateur et est adaptative en temps réel lorsque l\u27utilisateur l\u27interroge. Elle fonctionne en deux modes : l\u27interrogation centrée sujet et l\u27interrogation centrée population. Dans le premier mode d\u27interrogation, les données sont explorées selon un principe de navigation sémantique assez original. La session d\u27interrogation est entièrement centrée sur un sujet choisi par l\u27utilisateur. Le résultat d\u27une session est un document virtuel personnalisé créé pour l\u27utilisateur et qui contient l\u27ensemble des informations requises par l\u27utilisateur, structurées et présentées selon ses préférences. Le deuxième mode d\u27interrogation permet à l\u27utilisateur de réaliser, d\u27une façon interactive, des classifications sémantiques de données qui servent de base pour d\u27autres analyses statistiques et des prises de décisions. Nous avons appliqué notre approche au domaine médical et plus particulièrement pour l\u27accès au dossier médical distribué et l\u27aide à l\u27analyse de données pour la réalisation d\u27études épidémiologiques

Data Integration and user modelling : A topic Maps and Description Logics based approach

International audienc

Multi-side interface for the query and the analysis of medical data

International audienc

Semantic indexing for intelligent browsing of distributed data

International audienceWe present in this paper a semantic indexing technics based on description logics. Data to be indexed are semantically organized as a Topic Map. The index is constructed according to data organization, user profile, and data distribution (association rules). This way leads to obtain a more efficient index and represents more semantics. The index is well adapted to jointly query and navigate in the topic map. DL allows to represent semantics and performs powerful reasoning. The index structure is based on subsumption relationships (for intra-concept indexing) and roles (for inter-concepts indexing)