A Method for Detecting Behavior-Based User Profiles in Collaborative Ontology Engineering

Research output: Chapter in Book/Report/Conference proceedingConference paper

6 Citations (Scopus)


Ontology engineering is far from trivial and most collaborative methods and tools start from a prede fined set of rules, stakeholders can have in the ontology engineering process. We, however, believe that the di fferent types of user behavior are a priori not known and depend on the ontology engineering project. The detection of such user profi les based on unsupervised learning allows finding roles and responsibilities along peers in a collaborative setting. In this paper, we present a method for automatic detection of user profi les in a collaborative ontology engineering environment by means of the K-means clustering algorithm only by looking at the type of interactions a user makes. In this paper we use the GOSPL ontology engineering tool and method to demonstrate this method. The data used to demonstrate the method stems from two ontology engineering projects involving respectively 42 and 36 users.
Original languageEnglish
Title of host publicationOn the Move to Meaningful Internet Systems: OTM 2014 Conferences
EditorsRobert Meersman, Hervé Panetto, Tharam Dillon, Michele Missikoff, Lin Liu, Oscar Pastor, Alfredo Cuzzocrea, Timos Sellis
Place of PublicationBerlin Heidelberg
Number of pages17
ISBN (Electronic)978-3-662-45563-0
ISBN (Print)978-3-662-45562-3
Publication statusPublished - 27 Oct 2014
EventConfederated International Conferences: CoopIS, and ODBASE 2014 - Calabria, Amantea, Italy
Duration: 27 Oct 201431 Oct 2014

Publication series

NameLecture Notes in Computer Science


ConferenceConfederated International Conferences: CoopIS, and ODBASE 2014


  • Collaborative Ontology Engineering
  • User Profiling
  • Clustering


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