Please use this identifier to cite or link to this item:
http://hdl.handle.net/11366/527
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Guillaumet, Anna | en_US |
dc.date.accessioned | 2016-05-31T11:54:10Z | - |
dc.date.available | 2016-05-31T11:54:10Z | - |
dc.date.issued | 2016-06-10 | - |
dc.identifier.uri | http://hdl.handle.net/11366/527 | - |
dc.description | Delivered at the CRIS2016 Conference in St Andrews.-- Contains conference paper (9 pages) and presentation (25 slides). | en_US |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | euroCRIS | en_US |
dc.relation.ispartofseries | CRIS2016: 13th International Conference on Current Research Information Systems (St Andrews, June 9-11, 2016) | - |
dc.subject | semantics | en_US |
dc.subject | research information | en_US |
dc.subject | research data | en_US |
dc.subject | machine learning | en_US |
dc.subject | data mining | en_US |
dc.subject | graphs | en_US |
dc.subject | discoverability | en_US |
dc.title | Can machines understand what researchers look for? Conceptualizing the research world | en_US |
dc.type | Conference Paper | en_US |
dc.relation.conference | CRIS2016 – St Andrews | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairetype | Conference Paper | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
Appears in Collections: | Conference |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
CRIS2016_paper_17_Guillaumet.pdf | post-print version | 615.47 kB | Adobe PDF | View/Open |
Guillaumet_euroCRIS 2016_v.2.pptx | PPT presentation | 4.38 MB | Microsoft Powerpoint XML | View/Open |
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