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Ph.D de

Group : Artificial Intelligence and Inference Systems

Optimal adaptive information management over the web.

Starts on 24/04/2006
Advisor : REYNAUD, Chantal

Funding : Autre financement à préciser
Affiliation : Université Paris-Saclay
Laboratory :

Defended on 29/04/2009, committee :

Research activities :
   - Semantic Web

Abstract :
The advent of the Web in the early 90s has deeply upset our society. This new media has rapidly become the greatest database in the world. Moreover, the ever increasing popularity of the Web engendered a huge dynamics with respect to Web data. Actually, by virtue of knowledge
evolution, data is permanently added, deleted or updated from the Web which raises important issues regarding Web information retrieval. Existing Web search engines are neither able to take knowledge evolution into account when users submit their queries nor able to understand users'
needs in order to return the most relevant information to users. The Semantic Web, proposed in 2001 and which aims at giving a sense to Web data in order to make it machine understandable, helps to improve Web search but knowledge evolution is still problematic.
In this work, we address the problem of taking knowledge evolution for improving Web search in the sense of relevance of the returned results. The advocated solution is based on the use of ontologies, cornerstone of the Semantic Web, for representing both the domain targeted by the query and the profil of the user who submit the query. Ontologies are considered as knowledge that is evolving over time. In consequence, the ontology evolution problem has to be tackled as regards the evolution of the targeted domain but also with respect to the evolution of users' profile.
First of all, we introduce un new paradigm : adaptive ontology as well as a process for making adaptive ontologies smoothly follow evolution of a domain. The so-defined model rely
on the adaptation of ideas developed in the field of psychology and biology to the knowledge engineering field.
Then, we propose an approach exploiting adaptive ontologies for improving Web information retrieval. To this end, we first introduce data structures, WPGraphs and W3 Graphs, for
representing Web data. We then introduce the ASK query language tailored for the extraction
of relevant information from these structures. We also propose a set of query enrichment rules
based on the exploitation of ontological elements as well as adaptive ontologies characteristics of the ontology representing the domain targeted by the query and the one representing the view of the user on the domain.

Ph.D. dissertations & Faculty habilitations


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.