Concept Learning For Achieving Personalized Ontologies: An Active Learning Approach

Murat Sensoy, Pinar Yolum

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)


In many multiagent approaches, it is usual to assume the existence of a common ontology among agents. However, in dynamic systems, the existence of such an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are different from each other. Therefore, to achieve personalized ontologies, agents must have a means to understand the concepts used by others. Consequently, this paper proposes an approach that enables agents to teach each other concepts from their ontologies using examples. Unlike other concept learning approaches, our approach enables the learner to elicit most informative examples interactively from the teacher. Hence, the learner participates to the learning process actively. We empirically compare the proposed approach with the previous concept learning approaches. Our experiments show that using the proposed approach, agents can learn new concepts successfully and with fewer examples.
Original languageEnglish
Title of host publicationAgents and Data Mining Interaction
Subtitle of host publication4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers
EditorsLongbing Cao, Vladimir Gorodetsky, Jiming Liu, Gerhard Weiss, Philip S Yu
PublisherSpringer Berlin / Heidelberg
Number of pages13
ISBN (Electronic)978-3-642-03603-3
ISBN (Print)978-3-642-03602-6
Publication statusPublished - 4 Aug 2009

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


  • concept learning
  • data mining


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