Information retrieval methodology for aiding scientific database search

During literature reviews, and specially when conducting systematic literature reviews, finding and screening relevant papers during scientific document search may involve managing and processing large amounts of unstructured text data. In those cases where the search topic is difficult to establish...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 24; no. 8; pp. 5551 - 5560
Main Authors Marcos-Pablos, Samuel, García-Peñalvo, Francisco J.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2020
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Automation
Big Data
Computational Intelligence
Computer science
Control
Data analysis
Data mining
Decision making
Documents
Engineering
Focus
Information retrieval
Literature reviews
Machine learning
Mathematical Logic and Foundations
Mechatronics
Methodology
Robotics
Search strategies
Searching
Unstructured data
Text mining
Information retrieval
Vector space model
Support vector machine
Systematic literature review
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Summary:During literature reviews, and specially when conducting systematic literature reviews, finding and screening relevant papers during scientific document search may involve managing and processing large amounts of unstructured text data. In those cases where the search topic is difficult to establish or has fuzzy limits, researchers require to broaden the scope of the search and, in consequence, data from retrieved scientific publications may become huge and uncorrelated. However, through a convenient analysis of these data the researcher may be able to discover new knowledge which may be hidden within the search output, thus exploring the limits of the search and enhancing the review scope. With that aim, this paper presents an iterative methodology that applies text mining and machine learning techniques to a downloaded corpus of abstracts from scientific databases, combining automatic processing algorithms with tools for supervised decision-making in an iterative process sustained on the researchers’ judgement, so as to adapt, screen and tune the search output. The paper ends showing a working example that employs a set of developed scripts that implement the different stages of the proposed methodology.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3568-0