Faiez Gargouri

Improving algorithms for structure learning in Bayesian Networks using a new implicit score

Learning Bayesian Network structure from database is an NP-hard problem and still one of the most exciting challenges in machine learning. Most of the widely used heuristics search for the (locally) optimal graphs by defining a score metric and employs a search strategy to identify the network structure having the maximum score. In this work, we propose a new score (named implicit score) based on the Implicit inference framework that we proposed earlier. We then implemented this score within the K2 and MWST algorithms for network structure learning. Performance of the new score metric was evaluated on a benchmark database (ASIA Network) and a biomedical database of breast cancer in comparison with traditional score metrics BIC and BD Mutual Information. We show that implicit score yields improved performance over other scores when used with the MWST algorithm and have similar performance when implemented within K2 algorithm.

A user profile modelling using social annotations: a survey

As social networks are growing in terms of the number of users, resources and interactions; the user may be lost or unable to find useful information. Social elements could avoid this disorientation like the social annotations (tags) which become more and more popular and contribute to avoid the disorientation of the user. Representing a user based on these social annotations has showed their utility in reflecting an accurate user profile which could be used for a recommendation purpose. In this paper, we give a state of the art of characteristics of social user and techniques which model and update a tag-based profile. We show how to treat social annotations and the utility of modelling tag-based profiles for recommendation purposes.

Contextual ontologies: motivations, challenges, and solutions

Contextual ontologies are ontologies that characterize a concept by a set of properties that vary according to context. Contextual ontologies are now crucial for users who intend to exchange information in a domain. Existing ontology languages are not capable of defining such type of ontologies. The objective of this paper is to formally define a contextual ontology language to support the development of contextual ontologies. In this paper, we use description logics as an ontology language and then we extend it by introducing a new contextual constructor.

Group extraction from professional social network using a new semi-supervised hierarchical clustering

Recently, social network has been given much attention. This paper addresses the issue of extraction groups from professional social network and enriches the representation of the user profile and its related groups through building a social network warehousing. Several criteria may be applied to detect groups within professional communities, such as the area of expertise, the job openings proposed by the group, the security of the group, and the time of the group creation. In this paper, we aim to find, extract, and fuse the LinkedIn users. Indeed, we deal with the group extraction of LinkedIn users based on their profiles using our innovative semi-supervised clustering method based on quantitative constraints ranking. The encouraging experimental results carried out on our real professional warehouse show the usefulness of our approach.

How to Model a Real-Time Database?

This paper presents a framework for real-time database design that is able to support real-time database requirements such as time-constrained data and time-constrained transactions. It is based upon a real-time object-oriented data model in which each object encapsulates time-constrained data, time-constrained methods and concurrency control mechanisms. Our framework is composed of an UML profile for real-time database, a translator to object-relational model, and an UML CASE Tool.

Dealing with Similarity Relations in Fuzzy Ontologies

Crisp ontologies become less suitable in all domains in which the concepts to be represented have imprecise definitions. Fuzzy ontologies are developed to overcome this problem. Determining similarity relations among fuzzy ontology components is essential for many reuse subprocesses, for query rewriting as well as for fuzzy ontology modeling. Denning similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontology components or may be deduced from their intensional definitions. The later approach needs to be dealt with differently in crisp and fuzzy ontologies. In the first case, two ontology components are related by a given similarity relation or not. In the second one, two ontology components have a degree to be related by a given similarity relation. This is the scope of this paper.

Approach and tool to evolve ontology and maintain its coherence

The concept of ontology is more and more used to provide a shared understanding of a domain of interest and to enhance communication among humans, computers and software. However, ontologies are often used in changing environments and, therefore, must be adapted to evolution requirements. This paper proposes an approach to manage the ontology evolution and to maintain its coherence after changing. This approach anticipates incoherencies that can be generated and proposes additional operations to correct them. To assist users in expressing and applying evolution requirements, an ontology evolution tool has been developed and applied to develop the Tunisian Education system.

Structural Model of Real-Time Databases: An Illustration

A real-time database is a database in which both the data and the operations upon the data may have timing constraints. The design of this kind of database requires the introduction of new concepts to modelize both data structures and the dynamic behavior of the database. In this paper, we propose an UML2.0 profile, entitled UML-RTDB, allowing the design of structural model for a real-time database. One of the main advantages of UML-RTDB is its capacity to take into account real-time database properties through specialized concepts in rigourous, easy and expressive manner.

Formalizing the framework design language F-UML

Frameworks offer reuse through the generality they have to encompass. This same property, however, often makes a framework design fairly complex, hard to understand and, hence, to reuse. This paper briefly presents the F-UML design. It then focuses on the definition of the formal semantics of F-UML. This latter is defined through a translation of the meta-model of F-UML to Object-Z. The Object-Z semantics allows a designer to prove the syntactic well-formedness of an F-UML design. In addition, it allows the verification of several design properties through a theorem prover.

A Core Ontology of Business Processes Based on DOLCE

New performance requirements to adapt to the changing environment and to maintain a competitive advantage have contributed since the 1980s to the emergence of new types of organizations focused on projects or processes. To facilitate the implementation of this process view of organizations, many theoreticians and practitioners have proposed analysis and modeling frameworks, ontologies being considered as a relevant tool to conduct a “semantic analysis” of business processes. Approaches in this area are, however, based on ad hoc, often implicit modeling principles and the proposed ontologies remain poor in terms of expressiveness. The objective of this paper is to analyze the ontological foundations of the business processes following a formal approach. We propose a core ontology of business processes specializing the DOLCE foundational ontology and supplementing Bottazzi and Ferrario’s DOLCE-based formal ontology of organizations. This ontology comprises several modules to reflect both the “static” aspects of organizations and their behaviors, including intentional ones. In the article, we present the contents of the ontology, the formal ontological tools reused for its design, and the various theories the ontology is committed to.