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Detecting User Profiles in Collaborative Ontology Engineering Using a User’s Interactions

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

Collaborative ontology-engineering methods usually prescribe a set of processes, activities, stakeholders, and the roles each stakeholder plays in these activities. We, however, believe that (a) the stakeholder community of each ontology-engineering project is different, and (b) one can observe different types of user behavior. It may thus very well be that the prescribed set of stakeholder types and roles do not suffice. If one were able to identify these user behavior types, which we will call user profiles, one can compliment or revisit those predefined roles. For instance, those user profiles can be used to provide customized interfaces for optimizing activities in certain ontology-engineering projects. We present a method for discovering different user profiles based on the interactions users have with each other on a collaborative ontology-engineering environment. Our approach clusters users based on the types of interactions they perform, which are retrieved from datasets that were annotated with an interaction ontology—built on top of SIOC—that we have developed. We demonstrate our method using the database of two instances of the GOSPL ontology-engineering tool. The databases contain the interactions of two distinct ontology-engineering projects involving, respectively, 42 and 36 users. For each dataset, we discuss the findings by analyzing the different clusters. We found that we are able to discover different user profiles, indicating that the approach we have taken is viable, though more experiments are needed to validate the results and to discover patterns across ontology-engineering projects.

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Notes

  1. http://blogs.gartner.com/debra_logan/2010/01/11/what-is-information-governance-and-why-is-it-so-hard/ (last accessed on August 22, 2016).

  2. That is, meaningful for that community.

  3. The ontology can be found on http://minf.vub.ac.be/ODBASE/interactions.rdf.

  4. http://www.foaf-project.org/.

  5. http://sioc-project.org/.

  6. http://dublincore.org/.

  7. The ontology can be found on http://minf.vub.ac.be/ODBASE/gosplinteractions.rdf.

  8. Note that for the same structured dataset we can now extract 11 dimensions since here the semantic interoperability required is now captured in the GOSPL tool, whereas in the first dataset these requirements were not explicitly captured.

  9. Dimensions 4, 5, 6 and 7 have very low interaction amounts over the complete dataset.

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Acknowledgments

We would like to thank Em. Prof. Dr. Meersman R. for his valuable input, and the statistical team Prof. Dr. Coomans D., Prof. Dr. Questier F. and Simons K. for giving input on clustering principles using PCA.

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Authors and Affiliations

  1. Biostatistics and Medical Informatics (BISI) Research Group, Department of Public Health, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Jette, Belgium

    Sven Van Laere, Ronald Buyl & Marc Nyssen

  2. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Christophe Debruyne

  3. WISE Lab, Vrije Universiteit Brussel, Brussels, Belgium

    Christophe Debruyne

Authors
  1. Sven Van Laere
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  2. Ronald Buyl
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  3. Marc Nyssen
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  4. Christophe Debruyne
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Corresponding author

Correspondence to Sven Van Laere.

Descriptive Statistics of Datasets

Descriptive Statistics of Datasets

Descriptive statistics of the two datasets are provided in Tables 4 and 5. The dimensions of each table are:

  • dim 1:    interactions about glosses

  • dim 2:    interactions about lexons

  • dim 3:    interactions about constraints

  • dim 4:    interactions about supertype relations

  • dim 5:    interactions about equivalence between glosses

  • dim 6:    interactions about synonyms

  • dim 7:    interactions considering general requests

  • dim 8:    interactions considering replies

  • dim 9:    interactions to close topics

  • dim 10:    interactions about casting votes

  • dim 11:    interactions about semantic interoperability requirements

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Van Laere, S., Buyl, R., Nyssen, M. et al. Detecting User Profiles in Collaborative Ontology Engineering Using a User’s Interactions. J Data Semant 6, 71–82 (2017). https://doi.org/10.1007/s13740-016-0074-3

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  • DOI: https://doi.org/10.1007/s13740-016-0074-3

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