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.
Highlights
The web service technology is involved in many applications, such as business processes management and recommendation systems [1]Thanks to its modularity, composability and loose coupling, this technology is largely utilized in data integration and applications’ composition
Several approaches have been proposed in the literature for tackling the web service discovery problem [3]
In [38], the authors propose a multi-criteria decision method (MCDM) for searching web services based on contextual attributes
Summary
The web service technology is involved in many applications, such as business processes management and recommendation systems [1]. According to [2], the service discovery can defined as follows: Given a web service repository, and a query requesting a service (hereafter service query), finding automatically a service from the repository that matches these requirements is the web service discovery problem. We must utilize both types of matching algorithms to enhance the discovery performance In this line of thought, the creation of a hybrid matching algorithm must address the following concerns: 1) How to solve the ordering conflicts entailed by the individual matching algorithms (for instance an algorithm may conclude that service S1 is better than service S2, while another may decide that S2 is better than S1)?. The most suitable matching algorithms are those that have a higher weight and a higher value of scoreij (see equation 22) These heuristic will ensure a good performance in terms of recall and precision.
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