Please use this identifier to cite or link to this item: http://hdl.handle.net/11366/527
Title: Can machines understand what researchers look for? Conceptualizing the research world
Authors: Guillaumet, Anna 
Keywords: semantics;research information;research data;machine learning;data mining;graphs;discoverability
Issue Date: 10-Jun-2016
Publisher: euroCRIS
Series/Report no.: CRIS2016: 13th International Conference on Current Research Information Systems (St Andrews, June 9-11, 2016)
Conference: CRIS2016 – St Andrews 
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.
Description: 
Delivered at the CRIS2016 Conference in St Andrews.-- Contains conference paper (9 pages) and presentation (25 slides).
URI: http://hdl.handle.net/11366/527
Appears in Collections:Conference

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CRIS2016_paper_17_Guillaumet.pdfpost-print version615.47 kBAdobe PDFView/Open
Guillaumet_euroCRIS 2016_v.2.pptxPPT presentation4.38 MBMicrosoft Powerpoint XMLView/Open
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