Ontology learning supports ontology engineers in the complex task of creating an ontology. Updating ontologies
at regular intervals greatly increases the need for expensive expert contribution. This naturally leads to
endeavors to automate the process wherever applicable. This paper presents a model for automated ontology
learning and a prototype which demonstrates the feasibility of the proposed approach in learning lightweight
domain ontologies. The system learns ontologies from heterogeneous sources periodically and delegates all
evaluation processes, eg. the verification of new concept candidates, to a crowdsourcing framework which
currently relies on Games with a Purpose. Furthermore, we sketch ontology evolution experiments to trace
trends and patterns facilitated by the system.(authors' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4106 |
| Date | January 2013 |
| Creators | Wohlgenannt, Gerhard, Belk, Stefan, Schett, Matthias |
| Publisher | SciTePress |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Book Section, PeerReviewed |
| Format | application/pdf |
| Relation | http://www.keod.ic3k.org, http://epub.wu.ac.at/4106/ |
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