Noreddine Gherabi

Noreddine Gherabi, Abdelhadi Daoui, Abderrahim Marzouk

Today, with the emergence of semantic web technologies and increasing of information quantity, searching for information based on the semantic web has become a fertile area of research. For this reason, a large number of studies are performed based on the measure of semantic similarity. Therefore, in this paper, we propose a new method of semantic similarity measuring which uses the dynamic programming to compute the semantic distance between any two concepts defined in the same hierarchy of ontology. Then, we base on this result to compute the semantic similarity. Finally, we present an experimental comparison between our method and other methods of similarity measuring. Where we will show the limits of these methods and how we avoid them with our method. This one bases on a function of weight allocation, which allows finding different rate of semantic similarity between a given concept and two other sibling concepts which is impossible using the other methods.

Noreddine Gherabi, Bahaj Mohamed

We propose a new method for shape recognition and retrieval based on dynamic programming. Our approach uses the dynamic programming algorithm to compute the optimal score and to find the optimal alignment between two strings. First, each contour of shape is represented by a set of points. After alignment and matching between two shapes, the contours are transformed into a string of symbols and numbers. Finally we find the best alignment of two complete strings and compute the optimal cost of similarity. In general, dynamic programming has two phases: the forward phase and the backward phase. In the forward phase, we compute the optimal cost for each subproblem. In the backward phase, we reconstruct the solution that gives the optimal cost. Our algorithm is tested in a database that contains various shapes such as MPEG-7.

Noreddine Gherabi, Abdelhadi Daoui, Abderrahim Marzouk

With the use of ontologies in several domains such as semantic web, information retrieval, artificial intelligence, the concept of similarity measuring has become a very important domain of research. Therefore, in the current paper, we propose our method of similarity measuring which uses the Dijkstra algorithm to define and compute the shortest path. Then, we use this one to compute the semantic distance between two concepts defined in the same hierarchy of ontology. Afterward, we base on this result to compute the semantic similarity. Finally, we present an experimental comparison between our method and other methods of similarity measuring.

Noreddine Gherabi, Redouane Nejjahi, Abderrahim Marzouk

The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big files in memory, the time it needs to download such files and especially the time it needs to make reasoning on them. We discuss in this paper approaches for segmenting such big Web ontologies as well as its usefulness. The segmentation method extracts from an existing ontology a segment that represents a layer or a generation in the existing ontology; i.e. a horizontally extraction. The extracted segment should be itself an ontology.

Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi et al.

Coronavirus (covid 19) is one of the most dangerous viruses that have spread all over the world. With the increasing number of cases infected with the coronavirus, it has become necessary to address this epidemic by all available means. Detection of the covid-19 is currently one of the world's most difficult challenges. Data science and machine learning (ML), for example, can aid in the battle against this pandemic. Furthermore, various research published in this direction proves that ML techniques can identify illness and viral infections more precisely, allowing patients' diseases to be detected at an earlier stage. In this paper, we will present how ontologies can aid in predicting the presence of covid-19 based on symptoms. The integration of ontology and ML is achieved by implementing rules of the decision tree algorithm into ontology reasoner. In addition, we compared the outcomes with various ML classifications used to make predictions. The findings are assessed using performance measures generated from the confusion matrix, such as F-measure, accuracy, precision, and recall. The ontology surpassed all ML algorithms with high accuracy value of 97.4%, according to the results.