Semantic Web Service Discovery Framework using Multi-Agents System And NLP Techniques

  • A. Essayah Laboratory: Signals, Distributed Systems and Arti¬cial Intelligence (SSDIA) ENSET Mohammedia, Hassan II University, Casablanca. Bd Hassan II, Mohammedia, Maroc.
  • M. Youssfi Laboratory: Signals, Distributed Systems and Arti¬cial Intelligence (SSDIA) ENSET Mohammedia, Hassan II University, Casablanca. Bd Hassan II, Mohammedia, Maroc.
  • E. Illoussamen Laboratory: Signals, Distributed Systems and Arti¬cial Intelligence (SSDIA) ENSET Mohammedia, Hassan II University, Casablanca. Bd Hassan II, Mohammedia, Maroc.
  • K Mansouri Laboratory: Signals, Distributed Systems and Arti¬cial Intelligence (SSDIA) ENSET Mohammedia, Hassan II University, Casablanca. Bd Hassan II, Mohammedia, Maroc.
  • M. Qbadou Laboratory: Signals, Distributed Systems and Arti¬cial Intelligence (SSDIA) ENSET Mohammedia, Hassan II University, Casablanca. Bd Hassan II, Mohammedia, Maroc.
Keywords: Semantic web Service, Ontology, web service composition, functional and non-functional properties, Distributed information systems, natural language processing.

Abstract

As a consequently increase amount of web services available on the web, automatic discovery presents a great challenge, in order to satisfy this requirement, semantic discovery approaches based on ontologies have been developed. However, end-users do not have knowledge about semantic web languages to express their requirements.

In this paper, we propose an automatic discovery framework based on multi-agent systems and natural language processing techniques to match a user request. The framework allows semantic matching through a set of semantic web services in order to enhance the accuracy of the discovery pattern, and to find a relevant match to the user request composed of keywords written in natural language. The use of multi-agent systems provide us the possibility to decrease the run-time by parallelizing simultaneous tasks, also, to implement various natural language processing techniques and matchmaking algorithms, and finally, they allow us to measure the users satisfaction through their behaviors dashboard analyze. 

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Published
2017-09-01
Section
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems