In recent years, several education systems have been developed. Consequently, each learner can have di fferent pro files which each one is related to a system. Each profi le can be completed and enriched by the data coming from the other profi les in order to return results reflecting the learner's need. The profi le enrichment requires the establishment of an interoperable system which i) resolves the problem of learner's profi le heterogeneity based on a matching process and ii) integrates the data in the diff erent profi les based on a data fusion process. The data fusion approaches mainly aim at resolving the conficts occurring in the data values. They are based on non organized pro les which may produce inconsistent results. The profi le organization is done either by using the machine learning techniques or the notion of temperature. In this paper, we propose a new data fusion approach to improve the conflict resolution by organized profi les. Each profi le is organized by respectively merging a clustering algorithm and the temperature and by taking into account the data semantic relationship.