Henda Ben Ghezala

Modular ontological warehouse for adaptative information search

With the growth rate of information repositories, most of the current research effort are focusing on improving the accuracy in searching and managing information (especially text data), because of lacking of adaptive knowledge representation to the information content of these systems. Besides, domain knowledge is evolving and consequently, ontologies should be automatically built and extended. Thus, introducing modularity paradigm in ontology engineering is now important to tackle scalability problems. In this paper, we address the problem of representing modular ontologies at an abstract level that can improve the traditional information system with higher efficiency, in the context of previous work aiming at integrating ontology learning in traditional Information Retrieval systems on the web. The contribution consists in organizing ontology elements into semantic three-layered ontology warehouse (topic classification, domain knowledge representation, and module representation). The proposed model has been applied for textual content semantic search and relevance improvement has been observed.

A reuse approach based on object orientation: its contributions in the development of CASE tools

The aim of this paper is to present an approach to facilitate reuse. This approach, which is based on an object oriented design method, describes a way of structuring components and reuse library. Two concepts, domain and theme, are introduced to allow a classification of components by the services that they offer and by application domain. The library itself is organized in three hierarchical levels -general, dedicated and personal-, where the reusable components are stored according to their degree of “interest” (general interest, by application type or particular). So, the library is generic and could cluster various reusable component types (specification components, design components, packages,…). The contributions of this approach in the development of CASE tools are also emphasized.

Query-driven approach of contextual ontology module learning using web snippets

The main objective of this work is to automatically build ontology modules that cover search terms of users in ontology-based question answering on the Web. Indeed, some arising approaches of ontology module extraction aim at solving the problem of identifying ontology fragment candidates that are relevant for the application. The main problem is that these approaches consider only the input of predefined ontologies, instead of the underlying semantics represented in texts. This work proposes an approach of contextual ontology module learning covering particular search terms by analyzing past user queries and by searching for web snippets provided by the traditional search engines. The obtained contextual modules will be used for query reformulation. The proposal has been evaluated on the ground of two criteria: the semantic cotopy measure of discovered ontology modules and the precision measure of the search results obtained by using the resulted ontology modules for query reformulation. The experiments have been carried out according to two case studies: an open domain web search and the medical digital library “PubMed”.

Towards a new model for context-aware recommendation

With current growth of internet sales and content consumption, several research efforts focus on recommendation and personalization approaches as a solution to information overload. In this paper, we first propose a new context-aware recommendation model that inspires from consumption psychology researches. Then, two different techniques for generating recommendations from the proposed model are detailed and evaluated. The first is based on logistic regression and the second uses enumeration in order to calculate the probability a customer purchases a given item. We Also study and evaluate three strategies of recommender systems hybridization based on weighting and selection to eliminate the problems the underlying techniques have when applied solely. At the end, we conclude with some ideas for further development and research.

Contextual ontology module learning from web snippets and past user queries

In this paper, we focus on modularization aspects for query reformulation in ontology-based question answering on the Web. The main objective is to automatically learn ontology modules that cover search terms of the user. Indeed, the main problem is that current approaches of ontology modularization consider only the input existant ontologies, instead of underlying semantics found in texts. This work proposes an approach of contextual ontology module learning covering particular search terms by analyzing past user queries and snippets provided by search engines. The obtained contextual modules will be used for query reformulation. The proposal has been evaluated on the ground of semantic cotopy measure of discovered ontology modules, relevance of search results.

A Multi-Agent Framework for Anomalies Detection on Distributed Firewalls Using Data Mining Techniques

The Agents and Data Mining integration has emerged as a promising area for disributed problems solving. Applying this integration on distributed firewalls will facilitate the anomalies detection process. In this chapter, we present a set of algorithms and mining techniques to analyse, manage and detect anomalies on distributed firewalls’ policy rules using the multi-agent approach; first, for each firewall, a static agent will execute a set of data mining techniques to generate a new set of efficient firewall policy rules. Then, a mobile agent will exploit these sets of optimized rules to detect eventual anomalies on a specific firewall (intra-firewalls anomalies) or between firewalls (inter-firewalls anomalies). An experimental case study will be presented to demonstrate the usefulness of our approach.

A personalised semantic and spatial information retrieval system based on user's modelling and accessibility measure

Search personalisation is a multi-criteria decision problem whose objective is to filter relevant information based on a set of criteria such as needs, interests and content semantics. Hereby, different users could enter the same query into a search system, but their information needs can be very different. Web personalisation can be seen as an interdisciplinary field whose objective is to facilitate the interaction between web content and users needs. It includes per definition several research domains from social to information sciences. The personalised search focuses on integrating users contexts, needs and relevancy criteria in the information retrieval process in order to help them finding the right content. This paper presents users network modelling for personalised spatial and semantic information retrieval. The idea is to provide a user with personalised results based on his model and on the neighbour users models. The spatial personalisation search is based on a measure of spatial accessibility, whose objective is to predict and evaluate location relevancy, accessibility and associations at the user level. This measure favours delivery of location-based and personalised recommendations. Our experiments confirm the effectiveness of our proposal by pointing out the improvement of the personalised search results when compared to a baseline web search.

A dynamic composition of ontology modules approach: application to web query reformulation

As the semantic web emerges, the problems related to the management of ontologies are gaining relevance. Ontology management is considered as one of the main issues in artificial intelligence and knowledge engineering. Ontology modularisation aims at structuring ontologies to facilitate its reuse and maintenance. Compositional methodologies intend to integrate a set of modules into a larger ontology. This paper presents a dynamic approach for ontology modules composition based on semantic similarity measures between concepts on the web, and aiming to improve the resulting modular ontology’s structure and content. The proposed approach takes place in three main phases, namely: cooccurrence graph construction of ontology modules, cooccurrence graph clustering of modular ontologies and hierarchical modules clusters construction. To evaluate our approach, we compare the obtained results with those of other compositional approaches in the literature, and we also show how these modular ontologies can be used to improve web query reformulation.

Operationalization of an ontology based sociocultural adaptation approach and its application to CSCL

Collaborative learning environments bring together learners from different cultures and social contexts, around a common task. These learners interact both with each other and with computers. Hence, a dual problem arises: how to model and integrate socio-cultural factors that characterize these learners? How to design and develop culture-aware collaborative learning environments? This paper addresses both issues by describing an ontology based socio-cultural adaptation approach and its operationalization leading to the implementation of a culture-aware-web-based collaborative system. The adaptation of the collaborative learning environment is performed according to the socio-cultural profile of each learner.

Evaluating community detection using a bi-objective optimization

Community detection consists on a partitioning networks technique into clusters (communities) with weak coupling (external connectivity) and high cohesion (internal connectivity). In order to measure the performance of the clustering, the network modularity is largely used, a metric that presents the cohesion and the coupling of communities. In this paper, a global and bi-objective function is proposed to evaluate community detection. This function combines modularity (based on structure and edges weights) and the inter-classes inertia (based on nodes weights). Then, we rely on a computational optimization technique i.e. Particle Swarm Optimization to maximize this bi-objective quality. Finally, a case study evaluates the proposed solution and illustrates practical uses.