Ontology-based multi-agents for intelligent healthcare applications

Abstract

A healthy diet and lifestyle are the most effective approaches to prevent disease. Good eating habits are central to a healthy lifestyle. When a person eats too much or too little on a continual basis, the risk of disease will increase. Therefore, developing healthy and balanced eating habits is essential to disease prevention. This paper proposes an ontology-based multi-agents (OMAS), including a personal knowledge agent, a fuzzy inference agent, and a semantic generation agent, for evaluating the health of diets. Using the proposed approach, domain experts can create nutritional facts for common Taiwanese foods. Next, the users are requested to input foods eaten. Finally, the food ontology and personal profile ontology are constructed by domain experts. Fuzzy markup language (FML) is used to describe the knowledge base and rule base of the OMAS. Additionally, web ontology language (OWL) is employed to describe the food ontology and personal profile ontology. Finally, the OMAS semantically analyzes dietary status for users based on the pre-constructed ontology and fuzzy inference results. Using the generated semantic analysis, people can obtain health information about what they eat, which can lead to a healthy lifestyle and healthy diet. Experimental results show that the proposed approach works effectively and diet health status can be provided as a reference to promote healthy living. © Springer-Verlag 2010

A survey on situation-aware ambient intelligence systems

“…For example, agent based systems are used to create improved context awareness by perceiving input from the user and the environment (Tapia et al 2010). Different ontology-related approaches are already applied for detecting activities based on biometric information (Xefteris et al 2011) or for evaluating the quality of healthcare measures (Wang et al 2010). Their usage for gaining SAW is proposed by various researchers (Klein et al 2007), (Kokar et al 2009), (Endsley and Garland 2000), (Baumgartner and Retschitzegger 2006) and seems to be promising.…”

A semantic frame-based intelligent agent for topic detection

“…Recently, we have acknowledged an increasing interest in utilizing ontologies as artifacts to represent human knowledge and critical components in knowledge management, which can be observed in the Semantic Web, business-to-business applications, and several other application areas. Based on the levels of organization mentioned in Lee et al (2005), Wang et al (2010), this paper adopts a novel structure to construct the NE ontology for semantic labeling. Figure 2 depicts the architecture of the NE ontology, which includes a topic layer, a semantic layer, and an instance layer.…”

Decision making with a fuzzy ontology

“…Ontologies have become increasingly popular and explored since the beginning of the 1990s; Parry (2004) has given a definition for fuzzy ontology and proposed to represent it as a fuzzy relation (Parry 2004(Parry , 2006. The combination of ontologies and fuzzy sets theory have shown its utility in many fields as, for instance, information retrieval (Parry 2006;Knappe et al 2007), summarisation (Lee et al 2005), similarity matching (Carlsson et al 2010), image representation (Hudelot et al 2008), control theory , dietary assessments (Lee et al 2010a, b) and health care (Lee et al 2009;Wang et al 2010). Here, we propose to represent a fuzzy ontology by means of a fuzzy relation characterised by the following membership function…”