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

Knowledge Extraction from Description Logic Terminologies

Starts on 01/10/2015
Advisor : DAGUE, Philippe

Funding : Bourse pour étudiant étranger
Affiliation : Université Paris-Saclay
Laboratory : LRI - LaHDAK

Defended on 30/11/2018, committee :

Research activities :

Abstract :
An increasing number of large ontologies are being developed and made available, e.g., in repositories such as the NCBO Bioportal. Ensuring access to the knowledge contained in ontologies that is most relevant to users has been identified as an important challenge. In this work, we tackle this challenge by proposing three different approaches to extracting knowledge from Description Logic ontologies: extracting minimal ontology modules (i.e., sub-ontologies that are minimal w.r.t. set inclusion while still preserving all entailments over a given vocabulary); computing best ontology excerpts (a certain, small number of axioms that best capture the knowledge about the vocabulary while allowing for a degree of semantic loss); and determining projection modules (sub-ontologies of a target ontology that entails subsumption, instance or conjunctive queries that follow from a reference ontology). For each of these approaches, we are interested in extracting not only one but all instances of the module notion. For computing minimal modules and best excerpts, we introduce the notion of subsumption justification as a generalisation of the notion of a justification (a minimal set of axioms needed to preserve a given logical consequence) to capture the subsumption knowledge over the vocabulary. Similarly, for computing projection modules, we introduce the notion of projection justifications that preserve the answers to one of three query types as given by a reference ontology. Finally, we evaluate our approaches using a prototype implementation of the algorithms on large ontologies.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


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.