Ph.D
Group : Artificial Intelligence and Inference Systems

Understanding the hidden web

Starts on
Advisor : ABITEBOUL, Serge

Funding :
Affiliation : INRIA
Laboratory :

Defended on 12/01/2007, committee :

Research activities :
   - Semantic Web

Abstract :
The hidden Web (also known as deep or invisible Web), that is, the part of the Web not directly accessible through hyperlinks, but through HTML forms or Web services, is of great value, but difficult to exploit.
We discuss a process for the fully automatic discovery, syntactic and semantic analysis, and querying of hidden-Web services. We propose first a general architecture that relies on a semi-structured warehouse of imprecise (probabilistic) content. We provide a detailed complexity analysis of the underlying probabilistic tree model. We describe how we can use a combination of heuristics and probing to understand the structure of an HTML form. We present an original use of a supervised machine-learning method, namely conditional random fields, in an unsupervised manner, on an automatic, imperfect, and imprecise, annotation based on domain knowledge, in order to extract relevant information from HTML result pages. So as to obtain semantic relations between inputs and outputs of a hidden-Web service, we investigate the complexity of deriving a schema mapping between database instances, solely relying on the presence of constants in the
two instances. We finally describe a model for the semantic representation and intensional indexing of hidden-Web sources, and discuss how to process a user’s high-level query using such descriptions.

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.