Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Automated ontology evolution in a multi-agent system
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Using UML 2.1 to Model Multi-agent Systems
SEUS '08 Proceedings of the 6th IFIP WG 10.2 international workshop on Software Technologies for Embedded and Ubiquitous Systems
Dynamic sub-ontology evolution for traditional Chinese medicine web ontology
Journal of Biomedical Informatics
Towards the Mental Health Ontology
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
TICSA Approach: Five Important Aspects of Multi-agent Systems
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
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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.