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InDO: the Institute Demographic Ontology

Part of the Communications in Computer and Information Science book series (CCIS,volume 1459)

Abstract

Graduate education institutes in the United States (US) have been working on programs to increase the number of students and faculty from marginalized communities. When choosing to pursue a doctoral degree, the common question is ‘where is the best fit for me?’ Aspiring graduate students may feel the need for a reference point - someone with a similar background who has experienced or is currently experiencing the doctoral process, whether that be a student or a faculty member. Currently, there is no single location where that question can be answered for those in marginal communities, however answering that question also has an impact on the student’s post-graduation career path. In lieu of a single person, and to help provide information critical to answering the question, we built the Institute Demographic Ontology (InDO). InDO integrates US graduate institute’s doctoral recipient demographic data with data describing broad field of study, fine field of study, and the pursued career path to produce a knowledge graph for each prospective student’s query. The terminology is structured in five levels of hierarchy providing room for the most abstract top level (basic components used to describe an institute’s demographics), to the most concrete bottom levels (particular graduate program offered by the institute, along with corresponding provenance). Our resource (InDO) could be used by students within a marginalized community in the US to infer whether a given institute has the resources to support a given program, based on demographic information such as number of doctorates awarded in a given field. We design a use case where an InDO-based knowledge graph is created incorporating some of the National Science Foundation (NSF) Doctoral Recipient Survey 2019 data. Our use case demonstrates the usage of InDO in the real world while providing a way to access NSF data in a machine readable format. Evaluation of our ontology is done with a set of competency questions created from the perspective of an aspirant marginalized graduate student who would be willing to use our system to gather information for making an informed decision. InDO provides an ontological foundation towards building a social machine as an aid to higher education and graduate mobility in the US.

Resource Website:

https://tetherless-world.github.io/institute-demographic-ontology

Keywords

  • Ontology
  • Knowledge graph
  • Institute demographics
  • Graduate mobility
  • NSF doctoral recipients survey data

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Notes

  1. 1.

    https://tetherless-world.github.io/institute-demographic-ontology.

  2. 2.

    http://swat.cse.lehigh.edu/onto/univ-bench.owl.

  3. 3.

    https://tetherless-world.github.io/institute-demographic-ontology.

  4. 4.

    https://tetherless-world.github.io/institute-demographic-ontology.

References

  1. Gay, G.: Navigating marginality enroute to the professoriate: graduate students of color learning and living in academia. Int. J. Qual. Stud. Educ. 17(2), 265–288 (2004)

    Google Scholar 

  2. Sevelius, J.M., et al.: Research with marginalized communities: challenges to continuity during the Covid-19 pandemic. AIDS Behav. 24(7), 2009–2012 (2020)

    Google Scholar 

  3. Keshan, N.: Building a social machine for graduate mobility. In: 13th ACM Web Science Conference 2021, pp. 156–157 (2021)

    Google Scholar 

  4. Foley, D.: Survey of Doctorate Recipients, 2019. NSF 21–230. National Center for Science and Engineering Statistics (NCSES), National Science Foundation, Alexandria (2021). https://ncses.nsf.gov/pubs/nsf21320/

  5. Thomas, K.N., Willis, L.A., Davis, J.: Mentoring minority graduate students: issues and strategies for institutions, faculty, and students. Equ. Oppor. Int. 26, 178–192 (2007)

    Google Scholar 

  6. McGee Jr., R.: Saran, S., Krulwich. T.A.: Diversity in the biomedical research workforce: developing talent. Mt. Sinai. J. Med. 79(3), 397–411 (2012)

    Google Scholar 

  7. Ullrich, L.E., Ogawa, J.R., Jones-London, M.D.: Factors that influence career choice among different populations of neuroscience trainees. bioRxiv (2021)

    Google Scholar 

  8. Girves, J.E., Zepeda, Y., Gwathmey, J.K.: Mentoring in a post-affirmative action world. J. Soc. Iss. 61(3), 449–479 (2005)

    Google Scholar 

  9. Bartman, C.C.: African American women in higher education: issues and support strategies (2015)

    Google Scholar 

  10. Balhoff, J.P., et al.: Tailoring the NCI thesaurus for use in the obo library. In: ICBO (2017)

    Google Scholar 

  11. Balshaw, D.M., Collman, G.W., Gray, K.A., Thompson, C.L.: The children’s health exposure analysis resource (chear): enabling research into the environmental influences on children’s health outcomes. Curr. Opin. Pediatr. 29(3), 385 (2017)

    Google Scholar 

  12. Ahmed, N., Khan, S., Latif, K.: Job description ontology. In: 2016 International Conference on Frontiers of Information Technology (FIT), pp. 217–222. IEEE (2016)

    Google Scholar 

  13. Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft Comput. 25(13), 8337–8355 (2021)

    Google Scholar 

  14. Paulheim, Heiko: Knowledge graph refinement: a survey of approaches and evaluation methods. Seman. Web 8(3), 489–508 (2017)

    CrossRef  Google Scholar 

  15. Guo, O., et al.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. (2020)

    Google Scholar 

  16. Kendall, E.F., McGuinness, D.L.: Ontology engineering. Synthesis lectures on the semantic web. Theory Technol. 9(1), i–102 (2019)

    Google Scholar 

Download references

Acknowledgement

This work is part of the “Building a Social Machine for Graduate Mobility” project and is supported in part by the Rensselaer-IBM Artificial Intelligence Research Collaboration. We would like to thank all the participants of the RPI IRB study #1924 that provided more insights into the graduate mobility gap. We would also like to thank Dean Stanley Dunn, Dean of Graduate Education, who provided expert insights into this issue. We would also like to thank the members of the Tetherless World Constellation at RPI who provided insights into this research.

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Correspondence to Neha Keshan .

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Keshan, N., Fontaine, K., Hendler, J.A. (2021). InDO: the Institute Demographic Ontology. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-91305-2_1

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