Can machines understand what researchers look for? Conceptualizing the research world

Abstract

Research information is a key topic for the researchers. Throughout history, researchers need to find what they want mainly through paper publications or books of previous researchers. Due the advance of the Internet, a large number of possibilities of data interaction appeared, making impossible to process and track all the research information and this could be a disadvantage. For this reason, semantics searches could help researchers to find and discover information in a reliable way. We must conceptualize the research word, through ontologies and the semantic data modeling techniques such as Resource Description Framework (RDF) and Web Ontology Language (OWL), to create a virtual scenario that a machine can “understand”, in this way, when a researcher search or seek something, the machine provides results ordered by categories and discards the results that are not relevant. Also it can make recommendations: helping researchers find colleagues, affinities with groups, best projects for them, and so on. To make this possible, we must define a good interface (using user experience techniques) and use a powerful semantic search engine (using i.e. machine learning, data mining techniques). The results must show as clear as possible, maybe with data visualization techniques.