In the Web publishing process of data it is recommended to link the data from different sources using similar resources that describe a domain in common. However, the growing number of published data sets on the Web have made the data discovery and data selection tasks become increasingly complex. Moreover, the distributed and interconnected nature of the data causes the understanding and analysis to become too prolonged. In this context, this work aims to provide a Web architecture for identifying RDF data sources with the goal of improving the publishing, interconnection, and data exploration processes within the Linked Open Data. Our approach utilizes the MapReduce computing model on top of the cloud computing paradigm. In this manner, we are able to make parallel keyword searches over existing semantic data indexes available on the web. This will allow to identify candidate sources to link the data. Through this approach, it was possible to integrate different semantic web tools and relevant data sources in a search process, and also to relate topics of interest denied by the user. In order to achieve our objectives it was necessary to index and analyze text to improve the search of resources in the Linked Open Data. To show the effectiveness of our approach we developed a case study using a subset of data from a source in the Linked Open Data through its SPARQL endpoint service. The results of our work reveal that the generation and usage of data source s statistics do make a great difference within the search process. These statistics help the user within the choosing individuals process. Furthermore, a specialized keyword extraction process is run for each individual in order to create different search processes using the semantic index. We show the scalability of our RDF recommendation process by sampling several individuals.