International Journal on Digital Libraries

, Volume 18, Issue 3, pp 173–190 | Cite as

Scholarly Ontology: modelling scholarly practices

  • Vayianos Pertsas
  • Panos Constantopoulos


In this paper we present the Scholarly Ontology (SO), an ontology for modelling scholarly practices, inspired by business process modelling and Cultural-Historical Activity Theory. The SO is based on empirical research and earlier models and is designed so as to incorporate related works through a modular structure. The SO is an elaboration of the domain-independent core part of the NeDiMAH Methods Ontology addressing the scholarly ecosystem of Digital Humanities. It thus provides a basis for developing domain-specific scholarly work ontologies springing from a common root. We define the basic concepts of the model and their semantic relations through four complementary perspectives on scholarly work: activity, procedure, resource and agency. As a use case we present a modelling example and argue on the purpose of use of the model through the presentation of indicative SPRQL and SQWRL queries that highlight the benefits of its serialization in RDFS. The SO includes an explicit treatment of intentionality and its interplay with functionality, captured by different parts of the model. We discuss the role of types as the semantic bridge between those two parts and explore several patterns that can be exploited in designing reusable access structures and conformance rules. Related taxonomies and ontologies and their possible reuse within the framework of SO are reviewed.


Process modelling Ontology Digital Humanities  Reuse and patterns Modelling methodologies 



We are grateful to Stavros Angelis, Costis Dallas, Agiatis Benardou, Leonidas Papachristopoulos, Nephelie Chatzidiakou, Eliza Papaki and Lorna Hughes for many productive discussions and insights. This work was in part supported by the projects Network for Digital Methods in the Arts and Humanities (NeDiMAH), DARIAH- ATTIKH: Developing the Greek Research Infrastructure for the Humanities DYAS, and the AUEB Original Publications Programme.


  1. 1.
    Berman, F., Fox, G., Hey, A.J.G.: Grid Computing. Wiley, New York (2003)CrossRefGoogle Scholar
  2. 2.
    Research Infrastructures in the Digital Humanities. Science Policy Briefing, vol. 42. European Science Foundation, ISBN: 978-2-918428-50-3 (2011).
  3. 3.
    Benardou, A., Constantopoulos, P., Dallas, C.: An approach to analyzing working practices of research communities in the humanities. Int. J. Humanit. Arts Comput. 7, 105–127 (2013)CrossRefGoogle Scholar
  4. 4.
    Case, D.O.: Looking for information: a survey of research on information seeking, needs, and behavior. Academic Press, San Diego, CA (2002)Google Scholar
  5. 5.
    Bearman, D.: Overview and discussion points. In: Research agenda for networked cultural heritage. Getty AHIP, pp. 7–22 . Santa Monica, CA (1996)Google Scholar
  6. 6.
    Brodaric, B., Gahegan, M.: Ontology use for semantic e-science. Semant. Web (2010). doi: 10.3233/SW-2010-0021
  7. 7.
    Thanos, C.: The future of digital scholarship. Procedia Comput. Sci. 38, 22–27 (2014)CrossRefGoogle Scholar
  8. 8.
    Thanos, C.: Mediation: the technological foundation of modern science. Data Sci. J. 13, 88–105 (2014)CrossRefGoogle Scholar
  9. 9.
    Meho, L.I., Tibbo, H.R.: Modeling the information-seeking behaviour of social scientists: Ellis’s study revisited. J. Am. Soc. Inf. Sci. Tech. 54, 570–587 (2003)Google Scholar
  10. 10.
    Palmer, C.L., Cragin, M.H.: Scholarship and disciplinary practices. Annu. Rev. Inf. Sci. (2008)Google Scholar
  11. 11.
    Unsworth, J.: Scholarly primitives: what methods do humanities researchers have in common, and how might our tools reflect this? King’s College, London (2000). Accessed 4 Aug 2009
  12. 12.
    Victor Kaptelinin, B.A.N.: Acting with Technology: Activity Theory and Interaction Design, p. 1347 (2006)Google Scholar
  13. 13.
    Yu, E., Giorgini, P., Maiden, N., Mylopoulos, J., Fickas, S.: Modelling strategic relationships for process reengineering. In: Social Modelling for Requirements Engineering, p. 11152Google Scholar
  14. 14.
    Sun, J., Loucopoulos, P., Zhao, L.: Representing and elaborating quality requirements: the QRA approach. Lecture Notes in Computer Science, vol. 8217, pp. 446–453. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41924-9_37
  15. 15.
    Tsakonas, G., Papatheodorou, C.: An ontological representation of the digital library evaluation domain. J. Am. Soc. Inf. Sci. Technol. 62(8), 1577–1593 (2011)CrossRefGoogle Scholar
  16. 16.
    Dietz, J.L.G.: Enterprise Ontology: Theory and Methodology, Enterprise Ontology (2006)Google Scholar
  17. 17.
    Weske, M.: Business Process Management. Springer, Berlin (2012). doi: 10.1007/978-3-642-28616-2 CrossRefGoogle Scholar
  18. 18.
    Fox, M.S.: The TOVE project towards a common-sense model of the enterprise. In: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, vol. 604, p. 2534. Springer, Berlin (1992). doi: 10.1007/BFb0024952
  19. 19.
    Uschold, M., King, M., Moralee, S.: The enterprise ontology. Knowl. Eng. Rev. 13(1), 31–89 (1998)CrossRefGoogle Scholar
  20. 20.
    Malone, T.W., Crowston, K., Herman, G.A.G.A1.: Organizing Business Knowledge: The MIT Process Handbook. MIT Press, Cambridge (2003)Google Scholar
  21. 21.
    Crofts, N., Doerr, M., Gill, T., Stead, S., Stiff, M. (eds.): Definition of the CIDOC conceptual reference model (version 5.0.1). ICOM/CIDOC CRM Special Interest Group (2009).
  22. 22.
    Guizzardi, G., Wagner, G.: A unified foundational ontology and some applications of it in business modelling. In: CAiSE Workshops, pp. 129–143 (2004)Google Scholar
  23. 23.
    Workflows for e-Science: Scientific workflows for grids (2006)Google Scholar
  24. 24.
    Deelman, E., Gannon, D., Shields, M., Taylor, I.: Workflows and e-Science: an overview of workflow system features and capabilities. Futur. Gener. Comput. Syst. 25, 528–540 (2009)CrossRefGoogle Scholar
  25. 25.
    Benardou, A., Constantopoulos, P., Dallas, C., Gavrilis, D.: Understanding the information requirements of arts and humanities scholarship. IJDC 5, 18–33 (2010)CrossRefGoogle Scholar
  26. 26.
    Constantopoulos, P., Dallas, C., Doorn, P., Gavrilis, D., Gros, A., Stylianou, G.: Preparing DARIAH. In: Proceedings of the International Conference on Virtual Systems and MultiMedia (VSMM08). Nicosia, Cyprus (2008).
  27. 27.
    NeDiMAH Methods Ontology (NeMO).
  28. 28.
    Benardou, A., et al.: A conceptual model for scholarly research activity. In: Unsworth, J., Rosenbaum, H., Fisher, K.E. (eds.) iConference 2010 Proceedings (Urbana–Champaign, Ill., 2010), p. 2632 (2010)Google Scholar
  29. 29.
    Doerr, M., Kritsotaki, A., Christophides, V., Kotzinos, D.: Reference Ontology for Knowledge Creation Processes. Collaborative Knowledge Creation, pp. 31–52. Sense Publishers, Rotterdam (2012). doi: 10.1007/978-94-6209-004-0_3
  30. 30.
    Francesconi, F., Dalpiaz, F., Mylopoulos, J.: TBIM: A Language for Modelling and Reasoning about Business Plans. Conceptual Modelling. Springer, Berlin (2013)Google Scholar
  31. 31.
    Doerr, M., Tzobanakis, M.: On information organization in annotation systems. In: Lecture Notes in Computer Science, p. 189200. Springer, Berlin (2005)Google Scholar
  32. 32.
    Suzuki, T., Hosoya, M.: Computational stylistic analysis of popular songs of japanese female singer-songwriters. Digit. Humanit. Q. 8 (2014).
  33. 33.
    Stocker, M., Smith, M.: Owlgres: a scalable OWL reasoner. Owled (2008)Google Scholar
  34. 34.
    Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. 5(2), 5153 (2007). doi: 10.1016/j.websem.2007.03.004
  35. 35.
    O’Connor, M.J., Das, A.K.: SQWRL: a query language for OWL. OWLED (2009)Google Scholar
  36. 36.
    SWRL: a semantic web rule language combining OWL and RuleML.
  37. 37.
    TOULMIN, S.E.: The Uses of Argument, 2nd edn. Cambridge University Press, Cambridge (2003)CrossRefGoogle Scholar
  38. 38.
    FaBiO and CiTO: ontologies for describing bibliographic resources and citations. 17, 3343 (2012). doi: 10.1016/j.websem.2012.08.001
  39. 39.
    Soldatova, L.N., King, R.D.: An ontology of scientific experiments. J. R. Soc. Interface 3(11), 795–803 (2006)CrossRefGoogle Scholar
  40. 40.
    Newman, D., Bechhofer, S., De Roure, D.: myExperiment: an ontology for e-research (2009)Google Scholar
  41. 41.
    Doerr, M., Rousakis, Y., Hiebel, G.: CRMsci: the scientific observation model. 135 (2014)Google Scholar
  42. 42.
    Sure, Y., Bloehdorn, S., Haase, P., Hartmann, J., Oberle, D.: The SWRC ontology semantic web for research communities. In: Presented at the EPIA’05: Proceedings of the 12th Portuguese Conference on Progress in Artificial Intelligence, Berlin (2005)Google Scholar
  43. 43.
    Tifous, A., Ghali, El, A., Dieng-Kuntz, R., Giboin, A., Christina, C., Vidou, G., : An Ontology for Supporting Communities of Practice. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of InformaticsAthens University of Economics and BusinessAthensGreece
  2. 2.Digital Curation Unit, IMISAthena Research CentreAthensGreece

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