Personalized information retrieval through alignment of ontologies State of art

Current information systems provide transparent access to multiple, distributed, autonomous and potentially redundant data sources based on a mediation architecture. Their users may not know the sources they questioned, nor their description and content. Consequently, their queries reflect no more a need that must be satisfied but an intention that must be refined based on data sources available at the time of interrogation. The purpose of the personalization is to facilitate the expression of users' needs. It allows them to obtain relevant information by maximizing the exploitation of their preferences grouped in their respective profiles. Our work aims to extend the users queries by extending the research field using ontologies. In a mediation architecture context, founded on the couple mediator-adapter, our process must consider not only the users' profiles but also the semantic description of data sources defined by mediation requests. The mediator solves the problems associated with heterogeneity while adapters describe the available data sources. The users express their requests in terms of a global schema when the system evaluates them over multiple data sources with different structure and content. Each data source is modeled using a local ontology when the global schema is obtained via a global one. The use of an adequate process of ontology alignment will allow us to increase the recall (retrieved information) and the precision or accuracy (relevant information) of our integration system. This article is a comparative study of the existing works that attempt to establish matching and alignment between ontologies. It presents their capabilities in terms of information retrieval metrics namely: precision, recall and F-measure. It highlights then their strength and week points. In addition, it presents the massive role of machine learning techniques to insure the interoperability over large-scale ontologies.