Lylia Abrouk

Discovering implicit communities in Web forums through ontologies

Being a Community manager is an emerging employment in social Web companies. His or her role is to monitor communities on a devoted social website, in order to understand new trends or behaviours. He or she also has to discover and attract new potential users of the website in external resources like web forums, that are not necessarily on the same topics nor explicitly defined. In this paper we propose a scalable protocol to monitor on-line communications, like web forums, walls or twits. We provide an analysis method to extract implicit communities and user interests based on the semantics of data exchange and the structure of communications. The method is parameterized by a target vocabulary expressed as an ontology, in order to focus on relevant communities.

Temporal semantic centrality for the analysis of communication networks

Understanding communication structures in huge and versatile online communities becomes a major issue. In this paper we propose a new metric, the Semantic Propagation Probability, that characterizes the users ability to propagate a concept to other users, in a rapid and focused way. The message semantics is analyzed according to a given ontology. We use this metric to obtain the Temporal Semantic Centrality of a user in the community. We propose and evaluate an efficient implementation of this metric, using real-life ontologies and data sets.

Automatic annotation using citation links and co-citation measure: application to the water information system

This paper describes an approach to automatically annotate documents for the Euro-Mediterranean Water Information System This approach uses the citation links and co-citation measure in order to refine annotations extracted from an indexation method An experiment of this approach with the CiteSeer database is presented and discussed.

Using social networks to enhance customer relationship management

In recent years, the Web has evolved into an exchange platform. Customer Relationship Management (CRM) must follow this evolution and connect CRM tools to social networks in order to place companies in the center of all the exchanges. We propose, in this article, a community detection approach that identifies clusters of customers of a company using their explicit and implicit behaviour. Our contribution is the definition of a composite profile that integrates various informations gathered from different applications, such as the information system of the company, the existing CRM, or Twitter. We define a similarity measure, between a user and a tag, that takes into account the rating and consultation of resources, as well as actions on social networks and user contacts. We validate this approach against a test database and we discuss results and future works.

A players clustering method to enhance the players' experience in multi-player games

Online multi player games are a kind of video game where players have to interact together in a same game environment through an Internet connection. In these games, the players are grouped in different sessions where they can only interact with the member of the session. In order to maximize the players experience, game designers have to solve different issues. The first one is to maximize the number of players in the different game sessions. As these game are designed to provide the better game experience when the maximum number of players are in the session. It is important to avoid session with few players. The second one is to create session where the players have the same skill level. A too large difference between the level skills of the players can create frustration. In this short paper we focus on players skill and present an approach in development to automatically detect communities of players in order to create game session. Our approach is based on game play component, user profile and player interaction with the different game play component. We describe the experiment scheme that has been designed in order to evaluate the impact of the proposition on player satisfaction.

Tagging Resources, Tagging Communities

Most of the existing social network systems require from their users an explicit statement of their friendship relations. In this paper we focus on implicit communities of Web users and present an approach to automatically detect such communities based on user’s resource manipulations. This approach is dynamic as user groups appear and evolve along with users interests over time. Moreover, new resources are dynamically labeled according to who is manipulating them. Our proposal relies on the fuzzy K-mean clustering method and is assessed on large movie data sets.

QUEXME: A Query Expansion Method Applied to Water Information System

The aim of the paper is to present and apply a QUery EXpansion MEthod called QUEXME while querying the Euro-Mediterranean Information System (EMWIS) on know-how in the Water sector. EMWIS provides a strategic tool for exchanging information and knowledge in the water sector between and within the Euro Mediterranean partnership countries (www.emwis.net). Information retrieval on the web or through some cooperation of information sources or some general knowledge bases is a complex process and a great challenge with the emergence of the semantic web. The aim of the query expansion method is to help and guide users to build their requests giving them some usually related terms close to their queries. Information retrieval in EMWIS is based on the use of a thesaurus to query the information system and to find relevant documents on some specific topics in the water sector. This thesaurus can be viewed as a light-weight web ontology. It is multilingual. This paper proposes an experimentation of our query expansion method within the framework of the EMWIS information system.

The design and implementation of Neuma , a collaborative Digital Scores Library: Requirements, architecture, and models

This paper presents the design and implementation of the Neuma platform, a digital library devoted to the preservation and dissemination of symbolic music content (scores). Neuma is open to musicologists, musicians, and music publishers. It consists of a repository dedicated to the storage of large collections of digital scores, where users/applications can upload their documents. It also proposes services to publish, annotate, query, transform, and analyze scores. The long-term goal of the project is to enable an open and collaborative space where musician communities will be able to share music in symbolic notation. The project is organized around the French IRPMF institute (BnF–CNRS) which chooses and produces collections (or corpora), collects and organizes user requirements, and validates new publications. We describe the architecture of Neuma and develop some of its salient features: score modeling annotations, search and transformation language, collaborative and community tools, and digital rights preservation. We illustrate these features with two collections published by Neuma, and discuss the impact of such on-line score collections from a musicological perspective.