Paper Info
Title
Modeling scientometric indicators using a statistical data ontology
Abstract
Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propose a semantic modeling of scientometric indicators using the ontology Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data as a mean for providing flexible and quick access knowledge representation that supports indicator discovery, enquiring and composition. The semi-automatic generation and further storage of this linked data in the Neo4j graph database enabled an updatable and quick access model.
Year
DOI
Venue
2022
10.1186/s40537-022-00562-x
Journal of Big Data
Keywords
DocType
Volume
Graph database, Ontology generation, CRISP-DM, Neo4j, Query evaluation
Journal
9
Issue
ISSN
Citations 
1
2196-1115
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Lopez-Rodriguez Victor100.34
Ceballos Hector G.210.71