The process of detecting similar research areas in academic is paramount, due to the large number of data sources, processes and high volumes of data to be analyzed. Academic researchers are used to producing and gathering a vast amount of data about their publications. Typical publications are indexed in different search applications. Unfortunately, these heterogeneous data are only partially used for detect similar research areas due to the wide variety of formats, standards and technologies, as well as the lack of efficient methods of integration. This article presents the design of a mashup system, Research Circles, for academic publications. Firstly, an integration system is used for data collection. This approach incorporates key elements including Web service access, named entity recognition, information relevance and semantic tagging. Based on, this study defines a set of queries of interest to be issued against the proposed integrated system. Next, a machine learning algorithms, such as clustering and Social Network Analysis, is proposed to detect similar research areas. Eventually, the data are published through triple store. In our mashup we provide hence an integrated solution allow detecting and publishing data about similar research areas.