Semiautomated process for generating knowledge graphs for marginalized community doctoral-recipients

Abstract

Purpose This paper aims to describe the “InDO: Institute Demographic Ontology” and demonstrates the InDO-based semiautomated process for both generating and extending a knowledge graph to provide a comprehensive resource for marginalized US graduate students. The knowledge graph currently consists of instances related to the semistructured National Science Foundation Survey of Earned Doctorates (NSF SED) 2019 analysis report data tables. These tables contain summary statistics of an institute’s doctoral recipients based on a variety of demographics. Incorporating institute Wikidata links ultimately produces a table of unique, clearly readable data. Design/methodology/approach The authors use a customized semantic extract transform and loader (SETLr) script to ingest data from 2019 US doctoral-granting institute tables and preprocessed NSF SED Tables 1, 3, 4 and 9. The generated InDO knowledge graph is evaluated using two methods. First, the authors compare competency questions’ sparql results from both the semiautomatically and manually generated graphs. Second, the authors expand the questions to provide a better picture of an institute’s doctoral-recipient demographics within study fields. Findings With some preprocessing and restructuring of the NSF SED highly interlinked tables into a more parsable format, one can build the required knowledge graph using a semiautomated process. Originality/value The InDO knowledge graph allows the integration of US doctoral-granting institutes demographic data based on NSF SED data tables and presentation in machine-readable form using a new semiautomated methodology.

Guest editorial: Special issue on “current topics of knowledge graphs and semantic web”

“…The selected papers are on many different topics, such as process mining application on patient waiting time (Dogan, 2022); maintenance of RDB2RDF in enterprise knowledge graphs (Vidal et al , 2022); deep neural network-based approach for fake news detection (Katariya et al , 2022); ranking community detection algorithms for complex social networks (Rani and Kumar, 2022); agglomerative clustering enhanced GA for optimal seed selection (Mehta, 2022); CNN-BERT for measuring agreement (Harly and Girsang, 2022); hotel room personalization via ontology and rule-based reasoning (Ojino et al , 2022); fake news detection on Twitter [1]; semiautomated process for generating knowledge graphs (Keshan et al , 2022); From ontology to knowledge graph with agile methods (DeBellis and Dutta, 2022); keyword-based faceted search interface for Knowledge Graph construction and exploration (Sellami and Zarour, 2022); and finally, applied personal profile ontology for personnel appraisals (Usip et al , 2022).…”