In this paper, we discussed the application of a compositional adaptation approach to recommend learning resources to users in the area of software development. This approach makes use of a domainspecific ontology in this area to find those words, which are used in the technical description of the stored cases. A point peculiar with representing cases in the proposed approach is to take into account the characteristics of included learning resources, which justify the way they support the essential operations in the case of solution. In this way, only those components that comply with user's request would be considered in the final solution. In the paper, the performance of the proposed approach for recommending learning resources together with the status of user experience in his/ her interaction with the resulted recommending system, have been evaluated. Results demonstrate the fact that the learning resources through this approach are sufficiently beneficial for the users. Although the proposed approach has been applied for recommending learning resources in the area of software development, it can be equally applied to any technological area through developing domain-specific ontology for that area. This is mainly because any technological area has its own specific objects/ entities holding their own semantic similarities that finally lead to forming a domain-specific ontology for that area.

Fusing textual information, as type of information fusion, has been of great significance to those interested in making informative texts out of the existing ones. The main idea behind text fusion, like any other type of information fusion, is to merge the partial texts from different sources in such a way that the outcome can hold a reasonably high relevance with regard to certain objectives. In this paper, a fuzzy framework is proposed for text generation, according to which a range of relevant texts are merged to yield producing a new text that can help the users fulfill a certain functionality in plausible manner. The focal point in our approach with regard to fusion is the distance between the class prototype of a text on the one side and the feature vectors belonging to different subsets of the existing texts on the other side. Results of experiments, show that the suggested framework can be a suitable alternatives for performing fusion in the cases that the identity of the existing texts from the viewpoint of the texts considered is unclear. This would turn into an effective utilization of the existing texts for the purpose of generating new texts.