Effective Service Discovery based on Pertinence Probabilities Learning

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.

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