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Ph.D de

Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

Identity Management in Knowledge Graphs

Starts on 01/10/2015
Advisor : PERNELLE-MANSCOUR, Nathalie

Funding :
Affiliation : vide
Laboratory : INRA et LRI LaHDAK

Defended on 30/11/2018, committee :
Co-Directrice de thèse :
- Mme Juliette Dibie - Professeure, AgroParisTech
- Mme Nathalie Pernelle - Maître de Conférences HDR, Université Paris-Sud

Co-Encadrante de thèse :
- Mme Fatiha Saïs - Maître de Conférences, Université Paris-Sud
- Mme Liliana Ibanescu (Co-Encadrante de thèse) - Maître de Conférences, AgroParisTech

Rapporteurs :
- Mme Catherine Faron Zucker - Maître de Conférences HDR, Université Nice Sophia Antipolis
- M. Mathieu d’Aquin - Professeur, National University of Ireland Galway

Examinateurs :
- M. Harry Halpin - Chercheur, Massachusetts Institute of Technology
- M. Pascal Molli - Professeur, Université de Nantes
- Mme Sarah Cohen Boulakia - Professeure, Université Paris-Sud

Research activities :

Abstract :
In the absence of a central naming authority on the Semantic Web, it is common for different knowledge graphs to refer to the same thing by different names (IRIs). Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Such identity statements have strict logical semantics, indicating that every property asserted to one name, will also be inferred to the other, and vice versa. While such inferences can be extremely useful in enabling and enhancing knowledge-based systems such as search engines and recommendation systems, incorrect use of identity can have wide-ranging effects in a global knowledge space like the Semantic Web. With several studies showing that owl:sameAs is indeed misused for different reasons, a proper approach towards the handling of identity links is required in order to make the Semantic Web succeed as an integrated knowledge space. By relying on a collection of 558 million identity statements, this thesis shows how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect possibly erroneous identity assertions. In addition, as a way to limit the excessive and incorrect use of owl:sameAs, we define a new relation for asserting the identity of two class instances in a specific context. This identity relation is accompanied by an approach for automatically detecting these links, with the ability of using certain expert constraints for filtering irrelevant contexts. As a first experiment, the detection and exploitation of the detected contextual identity links are conducted on a knowledge graph for life sciences, constructed in the context of this thesis in a collaboration with experts from the French National Institute of Agricultural Research (INRA).

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MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.