Please use this identifier to cite or link to this item: http://hdl.handle.net/11366/715
Title: Text data mining and data quality management for research information systems in the context of open data and open science
Other Titles: Exploration de données textuelles et gestion de la qualité des données pour les systèmes d'information de recherche dans le contexte des données ouvertes et de la science ouverte
Authors: Azeroual, Otmane 
Saake, Gunter 
Abuosba, Mohammad 
Schöpfel, Joachim 
Keywords: current research information systems
research information systems
Core Data Set (CDS)
CERIF
research information
standardisation
text mining
data quality
open data
open science
Issue Date: 28-Nov-2018
Publisher: École des Sciences de l’Information (ESI, Rabat, Maroc)
Source: Otmane Azeroual, Gunter Saake, Mohammad Abuosba, Joachim Schöpfel. Text data mining and data quality management for research information systems in the context of open data and open science. ICOA 2018 3e colloque international sur le libre accès, Nov 2018, Rabat, Morocco. 2018, Actes du 3e colloque international sur le libre accès. Le libre accès à la science : fondements, enjeux et dynamiques.
Conference: 3rd International Colloquium on Open Access = 3ème Colloque International sur le Libre Accès 
Abstract: In the implementation and use of research information systems (RIS) in scientific institutions, text data mining and semantic technologies are a key technology for the meaningful use of large amounts of data. It is not the collection of data that is difficult, but the further processing and integration of the data in RIS. Data is usually not uniformly formatted and structured, such as texts and tables that cannot be linked. These include various source systems with their different data formats such as project and publication databases, CERIF and RCD data model, etc.
Internal and external data sources continue to develop. On the one hand, they must be constantly synchronized and the results of the data links checked. On the other hand, the texts must be processed in natural language and certain information extracted. Using text data mining, the quality of the metadata is analyzed and this identifies the entities and general keywords. So that the user is supported in the search for interesting research information. The information age makes it easier to store huge amounts of data and increase the number of documents on the internet, in institutions' intranets, in newswires and blogs is overwhelming. Search engines should help to specifically open up these sources of information and make them usable for administrative and research purposes.
Against this backdrop, the aim of this paper is to provide an overview of text data mining techniques and the management of successful data quality for RIS in the context of open data and open science in scientific institutions and libraries, as well as to provide ideas for their application. In particular, solutions for the RIS will be presented.
Description: 18 pages.-- Presentation delivered within ICOA 2018 Session II: "Aspects technologiques de l’Open Access / Technological Aspects of Open Access"
URI: http://hdl.handle.net/11366/715
Appears in Collections:Outreach: Papers

Files in This Item:
File Description SizeFormat 
Final_Manuscript_ICOA'18_Azeroual.pdfConference paper (PDF)1.38 MBAdobe PDFView/Open
Show full item record

Page view(s)

42
Last Week
8
Last month
checked on Jan 22, 2019

Download(s)

7
checked on Jan 22, 2019

Google ScholarTM

Check


Items in the euroCRIS DSpace-CRIS are offered under a CC-BY licence unless otherwise indicated.