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.
Highlights
Nowadays, the growth of Scientometrics in different contexts has an impact on the way of analyzing science information
At the end of this section, we compare the number of nodes and relationships in our Neo4j graph database against the number of triplets uploaded to a graph database using Resource Description Framework (RDF) files in an Apache Jena Fuseki server to analyze the complexity of the data structure
Scientometric Indicators are important for universities in terms of decision making because they are used for university rankings
Summary
The growth of Scientometrics in different contexts has an impact on the way of analyzing science information. Braun and colleagues identify Scientometrics as focused on the study of scientific information, in the analysis of the quantitative aspects of the generation, propagation, and utilization of scientific information to contribute to a better understanding of the mechanism of scientific research activities [2]. We revised previous works on which indicators are semantically modeled and enquired, as well as those where ontologies are used for representing science or scientometric data. Semantic indicator modeling Fox proposed modeling city indicators with a semantic approach in 2018 [11]. His approach includes key aspects such as membership extent, temporal extent, spatial extent, and measurement of populations. The evaluation of the ontology was divided into the representation of the population as the definition of indicators, consistency of indicator definitions against the interpretation of a city, and how it can be used to support data collection of a city
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