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

Author(s)
Guillaumet, Anna
Keywords

semantics

research information

research data

machine learning

data mining

graphs

discoverability

Issue Date
June 10, 2016
Publisher
euroCRIS
Type
Conference Paper
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
https://dspacecris.eurocris.org/handle/11366/527
File(s)
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CRIS2016_paper_17_Guillaumet.pdf

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post-print version
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615.47 KB

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Guillaumet_euroCRIS 2016_v.2.pptx

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PPT presentation
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4.28 MB

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Microsoft Powerpoint XML

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