• [Journal] Mohamed Ben Aouicha, Mohamed Ali Hadj Taieb et Abdelmajid Ben Hamadou. System for Integrating Semantic Relatedness and similarity. , mars , 2017, Soft Computing
  • [Conférence intertnationale] Leila Ghorbel, Corinne Amel Zayani, Ikram Amous Ben Amor et Florence Sèdes. Learner's Pro le Hierarchization in an Interoperable Education System. International Conference on Intelligent Systems Design and Applications (ISDA), decembre, 2016, Portugal
    In recent years, several education systems have been developed. Consequently, each learner can have di fferent pro files which each one is related to a system. Each profi le can be completed and enriched by the data coming from the other profi les in order to return results reflecting the learner's need. The profi le enrichment requires the establishment of an interoperable system which i) resolves the problem of learner's profi le heterogeneity based on a matching process and ii) integrates the data in the diff erent profi les based on a data fusion process. The data fusion approaches mainly aim at resolving the conficts occurring in the data values. They are based on non organized pro les which may produce inconsistent results. The profi le organization is done either by using the machine learning techniques or the notion of temperature. In this paper, we propose a new data fusion approach to improve the conflict resolution by organized profi les. Each profi le is organized by respectively merging a clustering algorithm and the temperature and by taking into account the data semantic relationship.
  • [Conférence intertnationale] Ahlem Kalaï, Abdelghani Wafa, Corinne Amel Zayani et Ikram Amous Ben Amor. LoTrust: A Social Trust Level Model based on Time-Aware Social Interactions and Interests Similarity. Fourteenth Annual Conference on Privacy, Security and Trust (PST 2016), decembre, 2016, Nouvelle-Zélande

    With the immense growth of online social applications, trust plays a more and more important role in connecting users to each other, sharing their personal information and attracting him to receive recommendations. Therefore, how to obtain trust relationships through mining online social networks became a critical issue. To calculate the level of trust between two users, many computational trust models are proposed which mainly rely on the social network structure, the explicit trust from user to another, the users’ behaviors, or the users’ similarity, etc. However, the majority of these models ignored the temporal factor. In this paper, we propose a trust relationship detection mechanism from an egocentric social network in order to compute the trust level between an active user and his directed friends. We propose a Level of social Trust model, that we called LoTrust, which is suitable for personalized recommendation purpose. This computational model founded on novel trust metric which is based not only on the users’ interests similarity according to their semantic social profiles (RDF/FOAF), but also takes into account the time factor of the users’ active interactions (e.g comments, share photo, wall posts, messages). We perform experiments on real life dataset extracted from Facebook. The experimental results demonstrated how our LoTrust model produces satisfactory results than other computational models.

  • [Conférence intertnationale] maha azabou, Kais Khrouf et Jamel Feki. Analyzing Textual Documents with New OLAP Operators. AICCSA , decembre, 2016
  • [Conférence intertnationale] Omar Khrouf, Kais Khrouf et Jamel Feki. CobWeb Multidimensional Model: Fom OLAP To Tag Cloud. AICCSA , decembre, 2016
  • [Conférence intertnationale] Abir Boujelbène, Tarak Chaari et Ikram Amous Ben Amor. Towards a better SWRL rules dependency extraction. ISDA 2016, decembre, 2016, Portugal
    Information systems knowledge bases often include inference rules. The continuous growth of the facts in the recent information systems environments has caused the exponential increase of rule bases sizes. Therefore, rule bases management becomes more and more dicult. Such a task should be automated and based on the extraction of dependencies between rules in order to have a better insight on their correct execution order and to detect conflicts between them. In this paper, we describe a rules dependency extraction approach for Semantic Web Rule Language (SWRL) rules. Our approach insures the automatic extraction of a rule dependency graph based on the semantics of their components. We evaluated our work by applying it to two diff erent ontologies from medical and network security domains. We have implemented a prototype of our approach and we integrated it in a plug-in for Potege-Owl editor.
  • [Conférence intertnationale] Maryam Jallouli, Sonia Lajmi et Ikram Amous Ben Amor. Similarity and trust metrcis used in Recommender System: A survey. Intelligent Systems Design and Applications (ISDA), decembre, 2016, International Conference, Porto, Portugal

    Recommender systems suggest the most appropriate items to users in
    order to help customers to find the most relevant items and facilitate sales.
    Collaborative filtering recommendation algorithm is the most successful
    technique for recommendation. In view of the fact that collaborative filtering
    systems depend on neighbors as the source of information, the recommendation
    quality of this approach depends on the neighbor’s selection. However,
    selecting neighbors can either stem from similarity or trust metrics. In this
    paper, we analyze these two types of neighbor’s selection metrics used in the
    field of recommendation in the literature. For each type, we first define it and
    then review different proposed metrics.

  • [Conférence intertnationale] Imen Moalla, Ahlem Nabli, Lotfi Bouzguenda et Mohamed Hammami. Data warehouse design from social media for opinion analysis: the case of Facebook and Twitter. AICCSA, decembre, 2016, Agadir, Maroc

    In the recent years, the number of social media, such
    as Facebook, LinkedIn, Tumblr have been increasing regularly,
    have become ideal platforms for a large number of users
    at lower costs. However, social media helped companies get
    known by a wider public and collect the relevant information’s
    about customers, their needs and their expectations. Therefore,
    several decision makers have worked on these media to get
    extra information about companies and customers. Moreover,
    the explosive growth of the amount of data makes their analysis
    and usage difficult. Therefore, the collection, analysis and the
    storage of these data in a data warehouse are extremely necessary.
    Thus, data warehouse modeling from social media should take
    into account aspects of social media. This paper proposes an
    approach for data warehouse construction from social media.
    Precisely, it deals with the conceptual modeling from social media,
    especially Facebook and Twitter. The proposed model can predict
    the success of the products prospects and improve the existing

  • [Journal] Mohamed Ben Aouicha, Mohamed Ali Hadj Taieb et Abdelmajid Ben Hamadou. LWCR: multi-Layered Wikipedia representation for Computing word Relatedness. , decembre, 2016, Neurocomputing
  • [Conférence intertnationale] Bouchekwa Mariam. Modeling the semantic content of the socio-tagged images based on the extended conceptual graphs formalism. MoMM2016, novembre, 2016, Singapour, Singapour

    With the emergence of Web 2.0, the volume of multimedia documents, particularly the socio-tagged images, has become very considerable. This has made it the annotation and interrogation process of this type of documents a consuming and elusive task. A promising solution is to design methods allowing to model the semantic content of socio-tagged images in order to facilitate their annotation and the research process. Thus, we present in this paper a conceptual modeling approach of the semantic content of these images. First, our idea consists in expanding the conceptual graphs formalism in order to represent the relationships between the concepts and those that are between the concepts and their properties. Second, we use the extended graph to model the semantic content of the socio-tagged images. Experimental studies are conducted on a collection of 25.000 socio-tagged images shared in Flickr. The results demonstrate the effectiveness of our proposed approach.

  • Date de création : 19 Décembre 2012
    MIRADOC 2011 : Journées doctorales du laboratoire Miracl. Décembre 2011. Touzeur , Tunisie
  • Date de création : 19 Décembre 2012
    MIRADOC’2010, Rencontre Doctorale de Miracl Date : 19-22 décembre 2010. Lieu: Douz , Tunisie
  • Date de création : 19 Décembre 2012
    Les 2éme Journées sur les Réseaux Bayésiens et leurs Applications Date et lieu : octobre 2011, Sfax, Tunisie. Abstract The Bayesian Networks are graphical models that are easy to interpret and update. These models are useful if the knowledge is uncertain, but they lack some means to express ambiguity. To face this problem, we propose Fuzzy Evidence in Bayesian Networks and combine the Fuzzy Logic and Bayesian Network. This has allowed to benefit from mutual advantages of these two approaches, and to overcome the problem of data and observation ambiguity. This paper proposes an inference algorithm which uses the Bayesian Network and Fuzzy Logic reliability. This solution has been implemented, tested and evaluated in comparison with the existing methods.
  • Faiez Gargouri, Wassim Jaziri  Ontology Theory, Management and Design: Advanced Tools and Models ». IGI Global. ISBN-978-1-61520-   859-3. Mars 2010. Description Ontologies and formal representations of knowledge are extremely powerful tools for modeling and managing large applications in several domains ranging from knowledge engineering, to data mining, to the semantic web. Ontology Theory, Management and Design: Advanced Tools and Models, explores the wide range of applications for ontologies, while providing a complete view of the both the theory behind the design and the problems posed by the practical development and use of ontologies. This reference presents an in-depth and forward looking analysis of current research, illustrating the importance of this field and pointing toward to the future of knowledge engineering, management and information technology.  
  • Titre : Finite State Language Engineering Autheurs : BEN HAMADOU A., MESFAR S., SILBERZTEIN M.  ISBN : 978-9973-37602-2 Livre, Edition CPU, Tunis, 2010
  • Titre :  Les Systèmes décisionnels : Théorie et pratique Autheurs : BEN-ABDALLAH H., BOULMARKOUL A., BOUSSAID O., FEKI J., GARGOURI F. ISBN : 789973990020. Nombre des pages: 130 pages Livre, Nouha Editions, 2010.