Abstract: Web service discovery is one of the most motivating issues of service-oriented computing field. Several approaches have been proposed to tackle this problem. In general, they leverage similarity measures or logic-based reasoning to perform this task, but they still present some limitations in terms of effectiveness. In this paper, we propose a probabilistic-based approach to merge a set of matching algorithms and boost the global performance. The key idea consists of learning a set of relevance probabilities; thereafter, we use them to produce a combined ranking. The conducted experiments on the real world dataset “OWL-S TC 2” demonstrate the effectiveness of our model in terms of mean averaged precision (MAP); more specifically, our solution, termed “probabilistic fusion”, outperforms all the state of the art matchmakers as well as the most prominent similarity measures.

Mohammed Merzoug, Abdelhak Etchiali, Fethallah Hadjila and Amina Bekkouche, “Effective Service Discovery based on Pertinence Probabilities Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(9), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120989

@article{Merzoug2021,
title = {Effective Service Discovery based on Pertinence Probabilities Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120989},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120989},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {9},
author = {Mohammed Merzoug and Abdelhak Etchiali and Fethallah Hadjila and Amina Bekkouche}
}