**7. Conclusion and future works**

In this chapter, we investigated how the semantic technologies can be used to provide additional semantics from existing resources in industrial repositories. For this purpose, we presented a system based on ontology and AI architecture for knowledge management in industrial repositories. We describe an effort to design and develop a prototype to manage resources in a repository such as the OntoEnter project and exploit them to help users as they select resources. Our study addresses the main aspects of a Semantic Web Information Retrieval System architecture attempting to respond to the requirements of the next generation of Semantic Web users. This scheme is based on the following principle: knowledge elements are abstracted from a characterization by a metadata description that is used for further processing.

In this chapter, we offer different possibilities, which the semantic Web opens for the industry. An important goal is to study appropriate industrial cases, compile arguments, launch industrial projects, and develop prototypes for industrial companies that not only create with us but also benefit from the semantic Web.

As described here, semantic models play a key role in the evolving solution architectures that support the business goal of obtaining the complete view of "what is happening" within operations and then deriving business insights from that view. Semantic models based on industry standards take that one step further, especially as application vendors adopt those standards (which, as always, will happen more rapidly through pressure from the user community). This study addresses the main aspects of a semantic and intelligent information retrieval system architecture trying to answer the requirements of the next-generation semantic search engine. We have investigated how the semantic technologies can be used to provide additional semantics from existing resources in institutional repositories.

This scheme is based on the principle of knowledge elements that are abstracted from a metadata description characterization that is used for further processing. We have proposed to use ontology together with CBR in acquiring expert knowledge in the industry specific domain. We have developed the domain ontology, and we have studied how the content-based similarity between the concepts typed attributes could be assessed in CBR system. The study analyses the implementation results and evaluates the viability of our approaches in enabling search in intelligent-based digital repositories. It introduced a prototype Web-based CBR retrieval system, which operates on an RDF file store. With this, characteristic of the model ability of an individual will be increased to learn through collective searches experience. Furthermore, an IA was illustrated for assisting the user by suggesting improved ways to query the system on the ground of the resources in industry repositories according to his own preferences, which come to represent his interests. We have used all the profile agents effectively to generate relevant and recommended personalized profile for the different users.

OntoEnter can be part of a bigger framework of interacting global information networks including other DIRs, scientific repositories, commercial providers, and relies as much as possible on standards and existing building blocks as well as be based on Web standards. The combination of effective information retrieval techniques and IAs continues to show promising results in improving the performance of the information that is being extracted from the online repositories for users. Our findings suggest that IA is the central manager in the knowledge transfer process. Their mediation is essential to help adapt the knowledge produced by academics and makes it easier to adopt and use by the educational community. We conclude pointing out an important aspect of the obtained integration: improving representation by incorporating more metadata from within the information and intelligent techniques into the retrieval process, the effectiveness of the knowledge retrieval is enhanced. The model has good characteristics in providing preference to the users with a novel approach of finding nearby meaning of query and user can also recommend result pages by their opinion.

Future work will address the exploitation of information from other institutional repositories and digital services and refine the suggested queries, expand the system to provide other support, and refine and evaluate the system through user testing.

Future work will focus on the design of distributed and self-managed services based on the Web and services, which are:

