Journal of Computer Science 8 (10): 1667-1673, 2012 ISSN 1549-3636 © 2012 Science Publications Corresponding Author: Ouail Ouchetto, Department Mathemtics and Computer Science, Faculty of SJES, Hassan II Ain Chok University, Casablanca, Morocco 1667 Relevance Ranking for Services Retrieval 1 Ouail Ouchetto, 2 Hassania Ouchetto and 2 Ounsa Roudies 1 Department Mathemtics and Computer Science, Faculty of SJES, Hassan II Ain Chok University, Casablanca, Morocco 2 Department Computer Science, Mohammadia School of Engineers, Mohammed Vth University-Agdal, Rabat, Morocco Absract: Problem statement: One of the challenges of e-gov systems is to provide, during a search process, relevant services that meet user expectations. Indeed, obtaining relevant information responding to user queries is a difficult process. It becomes even complex when the query terms have many meanings and do not fit with the vocabulary used by the services. Approach: We propose an appropriate method to assess the adequacy of rendered services. This new method is based on a mathematical representation. It is based on calculating the relevance weight of each service by using the semantic equivalence. Results: Validation of this method was done in two times. Initially, it was implemented and integrated in a retrieval system. In a second step, it was made available to a number of users to give their judgment. Conclusion: The experiments show a high level of satisfaction of this method by improving the quality of the relevance ranking. The relevant services are presented in the first page and the order of relevance decreases with the pages. Key words: Relevance ranking, services retrieval, semantic layer INTRODUCTION For the computer industry, the personalization of information is a major issue in the context of enterprise information systems, electronic commerce, electronic government and the knowledge access. The relevance of the provided information, its intelligibility and its adaptation to use and user preferences constitute the factors that determine the success of implementing such systems. In the e-government systems, access to information and to the relevant services which fit to both the user context and user requirements represents a huge challenge for governments. This is due to many factors: the complexity of these systems, the diversity of the actors involved in the search process and the proliferation of heterogeneous resources constituting these systems (structured data, text documents, components). Therefore, information's diversity and user’s disorientation are the main reasons of non-user's satisfaction of e-government services during a search process (Ouchetto et al., 2012). In a process of searching an e-gov service or information, the user information needs are often expressed by using some keywords and short phrases. Different query terms can be used to retrieve services. However, the user often does not build a query which accurately reflects his needs because of: (i) the user perspectives and terminological habits (ii) the difficulty of formulating a query, (iii) non-mastery of the vocabulary used by e-gov services and (iv) control's lack of the user's real needs who prefers to look in the long results' lists which do not meet his/her expectations than to look for the appropriate keywords. To resolve this problem, we believe that the integration of a method for evaluating the services' appropriateness, as an important element in the search process of e-gov services, becomes an absolute necessity. The assistance to be brought is related to the final presentation of services. The principle is as follow: the user starts the search with a fixed need and a specific context, the system takes the keywords of the query. It enriches the query by including the semantics of these keywords. Afterwards, it calculates the weight of the retrieved services. Finally, it orders them in descending order before presenting them to the user. Related work: Research communities in the field of information retrieval believe that relevance is a strategic point in all personalization systems. Its purpose is to make information relevant to the user. To achieve this goal, they developed several methods to improve the user’s query, based on additional knowledge of the user. These methods are complemented by query expansion algorithms to remove the ambiguity of the meaning of terms used in the user’s query (Bhogal et al., 2007).