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dc.contributor.authorHadzic, Maja
dc.contributor.authorDillon, Darshan
dc.contributor.editorRobert Meersman, Pilar Herrero and Tharam Dillon
dc.date.accessioned2017-01-30T13:26:51Z
dc.date.available2017-01-30T13:26:51Z
dc.date.created2010-03-29T20:04:20Z
dc.date.issued2009
dc.identifier.citationHadzic, Maja and Dillon, Darshan. 2009. An agent-based data mining system for ontology evolution, in Robert Meersman, Pilar Herrero and Tharam Dillon (ed), On the move to meaningful internet systems: OTM 2009 workshops. pp. 836-847. Heidelberg: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/31689
dc.identifier.doi10.1007/978-3-642-05290-3_102
dc.description.abstract

We have developed an evidence-based mental health ontological model that represents mental health in multiple dimensions. The ongoing addition of new mental health knowledge requires a continual update of the Mental Health Ontology. In this paper, we describe how the ontology evolution can be realized using a multi-agent system in combination with data mining algorithms. We use the TICSA methodology to design this multi-agent system which is composed of four different types of agents: Information agent, Data Warehouse agent, Data Mining agents and Ontology agent. We use UML 2.1 sequence diagrams to model the collaborative nature of the agents and a UML 2.1 composite structure diagram to model the structure of individual agents. The Mental Heath Ontology has the potential to underpin various mental health research experiments of a collaborative nature which are greatly needed in times of increasing mental distress and illness.

dc.publisherSpringer
dc.subjectmental health ontology
dc.subjectdata mining
dc.subjectontology evolution
dc.subjectmulti-agent system
dc.subjectmental health
dc.subjectmulti-agent system design
dc.titleAn agent-based data mining system for ontology evolution
dc.typeBook Chapter
dcterms.source.startPage836
dcterms.source.endPage847
dcterms.source.titleOn the move to meaningful internet systems: OTM 2009 workshops
dcterms.source.isbn9783642052897
dcterms.source.placeHeidelberg
dcterms.source.chapter112
curtin.note

The original publication is available at : http://www.springerlink.com

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available


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