Ph.D
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.
