Abstract: Through this paper we present a method for automatic
generation of ontological model from any data source using Model
Driven Architecture (MDA), this generation is dedicated to the
cooperation of the knowledge engineering and software engineering.
Indeed, reverse engineering of a data source generates a software
model (schema of data) that will undergo transformations to generate
the ontological model. This method uses the meta-models to validate
software and ontological models.
Abstract: The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower
Abstract: Location-aware computing is a type of pervasive
computing that utilizes user-s location as a dominant factor for
providing urban services and application-related usages. One of the
important urban services is navigation instruction for wayfinders in a
city especially when the user is a tourist. The services which are
presented to the tourists should provide adapted location aware
instructions. In order to achieve this goal, the main challenge is to
find spatial relevant objects and location-dependent information. The
aim of this paper is the development of a reusable location-aware
model to handle spatial relevancy parameters in urban location-aware
systems. In this way we utilized ontology as an approach which could
manage spatial relevancy by defining a generic model. Our
contribution is the introduction of an ontological model based on the
directed interval algebra principles. Indeed, it is assumed that the
basic elements of our ontology are the spatial intervals for the user
and his/her related contexts. The relationships between them would
model the spatial relevancy parameters. The implementation language
for the model is OWLs, a web ontology language. The achieved
results show that our proposed location-aware model and the
application adaptation strategies provide appropriate services for the
user.