Text data mining and data quality management for research information systems in the context of open data and open science
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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.
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