Nesrine Ben Mustapha

Enhancing semantic search using case-based modular ontology

In this paper, we present a semantic search approach based on Case-based modular Ontology. Our work aims to improve ontology-based information retrieval by the integration of the traditional information retrieval, the use of ontology and the case based reasoning (CBR). In fact, our recommender approach uses the CBR with ontology for case representation and indexing. Ontology-based similarity is used to retrieve similar cases and to provide end users with alternative recommendations. The main contribution of this work is the use of a CBR mechanism and an ontological representation for two purposes: Resource Retrieval from Web and ontology enrichment from cases.

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.

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”.

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.

Context-Based Grouping and Recommendation in MANETs

We propose in this chapter a context grouping mechanism for context distribution over MANETs. Context distribution is becoming a key aspect for successful context-aware applications in mobile and ubiquitous computing environments. Such applications need, for adaptation purposes, context information that is acquired by multiple context sensors distributed over the environment. Nevertheless, applications are not interested in all available context information. Context distribution mechanisms have to cope with the dynamicity that characterizes MANETs and also prevent context information to be delivered to nodes (and applications) that are not interested in it. Our grouping mechanism organizes the distribution of context information in groups whose definition is context based: each context group is defined based on a criteria set (e.g. the shared location and interest) and has a dissemination set, which controls the information that can be shared in the group. We propose a personalized and dynamic way of defining and joining groups by providing a lattice-based classification and recommendation mechanism that analyzes the interrelations between groups and users, and recommend new groups to users, based on the interests and preferences of the user.

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.

Ontology Learning from Open Linked Data and Web Snippets

The Web of Open Linked Data (OLD) is a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF. Such data can be used as a training source for ontology learning from web textual contents in order to bridge the gap between structured data and the Web. In this paper, we propose a new method of ontology learning that consists in learning linguistic patterns related to OLD entities attributes from web snippets. Our insight is to use the Linked Data as a skeleton for ontology construction and for pattern learning from texts. The contribution resides on learning patterns for relations existing in the Web of Linked Data from Web content. These patterns are used to populate the ontology core schema with new entities and attributes values. The experiments of the proposal have shown promising results in precision.

The PARLANCE mobile application for interactive search in English and Mandarin

We demonstrate a mobile application in English and Mandarin to test and evaluate components of the Parlance dialogue system for interactive search under real-world conditions.

Ontology-Based User Preferences and Social Search for Spoken Dialogue Systems

Many current spoken dialogue systems for search are domain-specific and do not take into account the preferences and interests of the user. In order to provide a more personalized answer tailored to the user needs, we propose a spoken dialogue system where user interests are expressed as scores in modular ontologies. This also allows us to cover multiple domains (e.g. searching for restaurant, housing,...) because each ontology module corresponds to a search domain. This approach allows for a dynamic and evolving representation of user interests. Moreover, a collaborative search of users with similar interests allows to build ad-hoc communities where information can be shared amongst and recommended to users. We propose to use techniques borrowed from formal concept analysis to flexibly and efficiently build these communities.

Semantic Web Search System Founded on Case-Based Reasoning and Ontology Learning

With the continuous growth of data volume on the Web, the search for information has become a challenging task. Ontologies are used to improve the accuracy of information retrieval from the web by incorporating a degree of semantic analysis during the search. However, manual ontology building is time consuming. An automatic approach may aid to solve this problem by analyzing implicitly available knowledge such as the users’ search feedback. In this context, we propose a semantic web search system founded on Case-Based-Reasoning (CBR) and ontology learning that aims to enrich automatically the ontologies by using previous search queries performed by the user. Some experiments and results obtained with the proposed system are also presented, which show an improvement on the precision of the Web search and ontology enrichment.