An agent-based data mining system for ontology evolution
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 realiz...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/31689 |
| _version_ | 1848753452032720896 |
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| author | Hadzic, Maja Dillon, Darshan |
| author2 | Robert Meersman |
| author_facet | Robert Meersman Hadzic, Maja Dillon, Darshan |
| author_sort | Hadzic, Maja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:24:44Z |
| format | Book Chapter |
| id | curtin-20.500.11937-31689 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:24:44Z |
| publishDate | 2009 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-316892022-12-09T06:09:42Z An agent-based data mining system for ontology evolution Hadzic, Maja Dillon, Darshan Robert Meersman Pilar Herrero Tharam Dillon mental health ontology data mining ontology evolution multi-agent system mental health multi-agent system design 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. 2009 Book Chapter http://hdl.handle.net/20.500.11937/31689 10.1007/978-3-642-05290-3_102 Springer restricted |
| spellingShingle | mental health ontology data mining ontology evolution multi-agent system mental health multi-agent system design Hadzic, Maja Dillon, Darshan An agent-based data mining system for ontology evolution |
| title | An agent-based data mining system for ontology evolution |
| title_full | An agent-based data mining system for ontology evolution |
| title_fullStr | An agent-based data mining system for ontology evolution |
| title_full_unstemmed | An agent-based data mining system for ontology evolution |
| title_short | An agent-based data mining system for ontology evolution |
| title_sort | agent-based data mining system for ontology evolution |
| topic | mental health ontology data mining ontology evolution multi-agent system mental health multi-agent system design |
| url | http://hdl.handle.net/20.500.11937/31689 |