Abstract Scalability of ontology reasoning is a key factor in the practical adoption of ontology technologies. To improve the quality of reasoning on a simple ontology, we propose to enrich the comportment of the ontology concepts by rules. This enrichment is done by extending a version of SWRL and its execution in Jess. The extending of SWRL rules consists of adding... Scalability of ontology reasoning is a key factor in the practical adoption of ontology technologies. To improve the quality of reasoning on a simple ontology, we propose to enrich the comportment of the ontology concepts by rules. This enrichment is done by extending a version of SWRL and its execution in Jess. The extending of SWRL rules consists of adding new keywords as OWL ontology classes and properties, and post-translating them by using rewriting meta-rules. These rules adapt the technique of Object-Oriented programming and create a new hybrid ontology. The reasoning on this hybrid ontology makes it possible to improve in terms of results the quality of the previous reasoning. To mention and clarify the importance of this kind of reasoning, we use a case study inherent to interpret a check up in preventive medicine. Read More Read Less

Abstract Collaborative filtering algorithm (CF) is a personalized recommendation algorithm that is the most widely used in e-commerce. CF still needs to be improved so that it can make adequate recommendations and solve the problems such as scalability, smoothing the rating estimation. In this paper, we provide an approach of an item based collaborative filtering using item clustering prediction and... Collaborative filtering algorithm (CF) is a personalized recommendation algorithm that is the most widely used in e-commerce. CF still needs to be improved so that it can make adequate recommendations and solve the problems such as scalability, smoothing the rating estimation. In this paper, we provide an approach of an item based collaborative filtering using item clustering prediction and including a new enhanced correlation similarity. Firstly, we cluster the items i n some groups. Then, in the process of collaborative filtering recommendation, we need to calculate the similarity between the targeted item and items in the selected center. For this aim, an enhanced similarity measure based on multi criteria is proposed instead of the similarity based just on ratings’ items. The objective is to consider when we calculate similarity, the integration of item rating information, the background of the item and the time-weight as criteria of the item evaluation into a convex model. In so doing, the amelioration of the similarity between items performs the recommendation. This proposed CF algorithm is showing to reduce also the influence of the former evaluation of the item. Read More Read Less

Abstract Recommender system is a system that helps users to find interesting items. Actually, collaborative filtering technology is one of the most successful techniques in recommender system. In this article we propose two new approaches based on the co-clustering and co-dissimilarity between users. In the literature, we find a lot of approaches able to recommend items to the user. Aiming... Recommender system is a system that helps users to find interesting items. Actually, collaborative filtering technology is one of the most successful techniques in recommender system. In this article we propose two new approaches based on the co-clustering and co-dissimilarity between users. In the literature, we find a lot of approaches able to recommend items to the user. Aiming to offer a list of interesting items, we use a hybrid approach of collaborative filtering that perfo rms better than others. Our collaborative filtering approach is partitioned in two steps, the first based a bond energy algorithm (BEA), it’s one of group technology algorithm, its objective is realized the co-clustering or simultaneous clustering. The second is recommendation based on the graph theory, when we propose the use of kruskal algorithm; this one gives us a spanning tree with minimum weight from a connected graph. We define a group of criteria that help to determine the best items to recommend without computing the rating predictio Read More Read Less

Abstract In ubiquitous computing the discovery process may retrieve many services when in fact only one of them fit exactly user satisfaction, besides, after discovering is achieved, there is a set of candidate services between theme a selection must be made. In fact, discovery is a prerequisite for selection, but selection is the main purpose. Actually, uncertainty of context information may... In ubiquitous computing the discovery process may retrieve many services when in fact only one of them fit exactly user satisfaction, besides, after discovering is achieved, there is a set of candidate services between theme a selection must be made. In fact, discovery is a prerequisite for selection, but selection is the main purpose. Actually, uncertainty of context information may lead to inexact matching between already discovered and required service capabilities, and conse-quently to the non selection of fitting services. In order to handle incomplete context information, we propose in this paper a workflow-based algorithm allowing inexact matches for matching contextual service descriptions using similarity measures. Service description and request are compared using four kinds of similarity measures: syntactic, linguistic, structural and QoS semantic measures, which compare individually requested and provided properties represented as workflow nodes, and thereafter the global measures which take into account context and service as a whole are aggregated by means of the linguistic quantifier “almost all”. In our approach we consider that functional aspects of a service are already met and we focus on non-functional and QoS-related aspects of service description to rank-order the discovered services Read More Read Less