Context-based Grouping and Recommendation in MANETs

International audienceWe 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

Knowledge Harvesting for Business Intelligence

International audienceWith the growth rate of information volume, information access and knowledge management in enterprises has become challenging. This paper aims at describing the importance of semantic technologies (ontologies) and knowledge extraction techniques for knowledge management, search and capture in e-business processes. We will present the state of the art of ontology learning approaches from textual data and web environment and their integration in enterprise systems to perform personalized and incremental knowledge harvesting

A prototype for Knowledge Extraction from Semantic Web based on Ontological Components Construction

International audienceAdding a semantic dimension to web pages is a response to some problems of the present web and is known as the semantic web. Many methods and methodologies can be found in the literature. Generally, they are dedicated to particular data types like text, semi-structured data, relational data, etc. This paper presents a prototype for knowledge extraction from web pages based on ontological components construction. Our work deals with web pages. We will first study the state of the art of methodologies defined to learn ontologies from texts. Then, we will define architecture of ontological components for the Semantic web. An implementation and experimentation of the proposed architecture are presented

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

International audienceMany 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

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

International audienceThe 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 these approaches consider only the input of predefined ontologies, instead of the 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 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"

Enhancing semantic search using case-based modular ontology

International audienceIn 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

Survey on ontology learning from Web and open issues

International audienceWith the continual increase of the volume of available information on the Web, information access and knowledge management become challenging. Thus, adding a semantic dimension to the Web, by the deployment of ontologies, contributes to solve many problems. In the context of the semantic Web, ontologies improve the exploitation of Web resources by adding a consensual field of knowledge. The need for using domain ontology for information retrieval (IR) has been explored by some approaches to better answer users' queries. However, ontology in IR system requires a regular updating, especially the addition of new concepts and relationships. In fact, IR systems are generally based on few number of domain ontology that cannot be extended. This paper proposes a survey of main several approaches of ontology learning from Web. In a previous work, we have proposed an incremental approach for ontology learning using an ontological representation called "Metaontology". In this paper, we describe a how the processes of semantic search and ontology learning from texts can collaborate for learning of multilayer ontology warehouse