The annotation of articles from a given domain and the generation of semantic metadata can be considered a reliable foundation for creating a paper recommender system. Within this paper, the models from other previous researches are extended with the capability of visualizing articles and the most important concepts from a domain within imposed timeframes. This can be very useful for researchers to check out the most important publications from a given period, to view which are the trends and how a domain has evolved. Our previous analyses used the articles to build a paper graph and to suggest the most relevant articles, given a user defined query in natural language. This research contains a use case and creates visual graph representations to enhance the overall perception of the evolution of a domain.