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Research results
Ph.D de

Group : Large-scale Heterogeneous DAta and Knowledge

Maintenance of semantic annotations

Starts on 18/11/2015
Advisor : REYNAUD, Chantal

Funding : Bourse pour étudiant étranger
Affiliation : Université Paris-Sud
Laboratory : LIST et LRI

Defended on 07/12/2018, committee :
Directrice de thèse :
- Mme Chantal REYNAUD - Université Paris-Sud -

Co-Encadrant de thèse :
- M. Cédric PRUSKI - Luxembourg Institute of Science and Technology
- M. Marcos DA SILVEIRA - Luxembourg Institute of Science and Technology

- M. Jean CHARLET - LIMICS Paris 6
- M. Fabien GANDON - Université Côte d’Azur, Inria

Examinateurs :
- M. Pierre ZWEIGENBAUM - Université Paris-Saclay
- M. Patrick RUCH - HES-SO / HEG Geneva, Battelle campus
- Mme Lina SOUALMIA - Université de Rouen

Research activities :

Abstract :
Semantic annotations are often used in a wide range of applications ranging from information retrieval to decision support. Annotations are produced through the association of concept labels from Knowledge Organization System (KOS), i.e. ontology, thesaurus, dictionaries, with pieces of digital information, e.g. images or texts. Annotations enable machines to interpret, link, and use a vast amount of data. However, the dynamic nature of KOS may affect annotations each time a new version of a KOS is released. New concepts can be added, obsolete ones removed and the definition of existing concepts may be refined through the modification of their labels/properties. As a result, many annotations can lose their relevance, thus hindering the intended use and exploitation of annotated data. To solve this problem, methods to maintain the annotations up-to-date are required. In this thesis we propose a framework called MAISA to tackle the problem of adapting outdated annotations when the KOS utilized to create them change. We distinguish two different cases. In the first one we consider that annotations are directly modifiable. In this case, we proposed a rule-based approach implementing information derived from the evolution of KOS as well as external knowledge from the Web. In the second case, we consider that the annotations are not modifiable. The goal is then to keep the annotated documents searchable even if the annotation are produced with a given KOS version but the user used another version to query them. In this case, we designed a knowledge graph that represents a KOS and its successive evolution and propose a method to extract the history of a concept and add the gained label to the initial query allowing to deal with annotation evolution. We experimentally evaluated MAISA on realistic cases-studies built from four well-known biomedical KOS: ICD-9-CM, MeSH, NCIt and SNOMED CT. We show that the proposed maintenance method allow to maintain semantic annotations using standard metrics.