Mei-Hui Wang

A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation

It has been widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like personal diabetic-diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. This paper proposes a novel ontology model, which is based on interval type-2 fuzzy sets (T2FSs), called type-2 fuzzy ontology (T2FO), with applications to knowledge representation in the field of personal diabetic-diet recommendation. The T2FO is composed of 1) a type-2 fuzzy personal profile ontology ( type-2 FPPO); 2) a type-2 fuzzy food ontology ( type-2 FFO); and 3) a type-2 fuzzy-personal food ontology (type-2 FPFO). In addition, the paper also presents a T2FS-based intelligent diet-recommendation agent ( IDRA), including 1) T2FS construction; 2) a T2FS-based personal ontology filter; 3) a T2FS-based fuzzy inference mechanism; 4) a T2FS-based diet-planning mechanism; 5) a T2FS-based menu-recommendation mechanism; and 6) a T2FS-based semantic-description mechanism. In the proposed approach, first, the domain experts plan the diet goal for the involved diabetes and create the nutrition facts of common Taiwanese food. Second, the involved diabetics are requested to routinely input eaten items. Third, the ontology-creating mechanism constructs a T2FO, including a type-2 FPPO, a type-2 FFO, and a set of type-2 FPFOs. Finally, the T2FS-based IDRA retrieves the built T2FO to recommend a personal diabetic meal plan. The experimental results show that the proposed approach can work effectively and that the menu can be provided as a reference for the involved diabetes after diet validation by domain experts.

A Fuzzy Expert System for Diabetes Decision Support Application

An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.

Automated ontology construction for unstructured text documents

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.

Ontology-based intelligent decision support agent for CMMI project monitoring and control

This paper presents an ontology-based intelligent decision support agent (OIDSA) to apply to project monitoring and control (PMC) of capability maturity model integration (CMMI). The OIDSA is composed of three agents, namely a natural language processing agent, a fuzzy inference agent and a performance decision support agent. All the needed information is stored into an ontology repository, including the CMMI ontology and the project personal ontology (PPO), as well as Chinese dictionary. In addition, the natural language processing agent periodically collects the information of the project progress from project member to analyze the features of the terms for semantic concept clustering through document pre-processing and term filter process. Next, based on the CMMI ontology, the project personal ontology, and the processing results from the natural language processing agent, the fuzzy inference agent and performance decision support agent perform an inference mechanism to calculate the completed percentage of the project progress for each project member, then send the results out to the project manager for evaluating the performance of each project member. The experimental results show that the OIDSA can work effectively for PMC of CMMI

Current Frontiers in Computer Go

This paper presents the recent technical advances in Monte Carlo tree search (MCTS) for the game of Go, shows the many similarities and the rare differences between the current best programs, and reports the results of the Computer Go event organized at the 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2009), in which four main Go programs played against top level humans. We see that in 9 × 9, computers are very close to the best human level, and can be improved easily for the opening book; whereas in 19 × 19, handicap 7 is not enough for the computers to win against top level professional players, due to some clearly understood (but not solved) weaknesses of the current algorithms. Applications far from the game of Go are also cited. Importantly, the first ever win of a computer against a 9th Dan professional player in 9 × 9 Go occurred in this event.

Ontological recommendation multi-agent for Tainan City travel

Due to the gradual increase in travel, the travel agent plays an important role in both planning and recommending a personalized travel route. Tainan City, located in the southern Taiwan, is famous for its abundant historic sites and delicious snack food, and it has been one of the top tourist attractions in Taiwan for years. In this paper, we propose an ontological recommendation multi-agent for Tainan City travel. The core technologies of the agent contain the ontology model, fuzzy inference mechanism, and ant colony optimization. The proposed agent can recommend the tourist a personalized travel route to enjoy Tainan City according to the tourists requirements. It includes a context decision agent and a travel route recommendation agent. First, the context decision agent finds a suitable location distance, counts the context relation, and infers the context information based on the tourists requirements and Tainan City travel ontology. Next, the travel route recommendation agent is responsible for finding a personalized tour and plotting this travel route on the Google Map. Finally, the tourist can follow the personalized travel route to enjoy the cultural heritage and the local gourmet food during his stay at Tainan City. The experimental results show that the proposed approach can effectively recommend a travel route matched with the tourists requirements.

Ontology-based computational intelligent multi-agent and its application to CMMI assessment

Abstract This study presents an ontology-based computational intelligent multi-agent system for Capability Maturity Model Integration (CMMI) assessment. An ontology model is developed to represent the CMMI domain knowledge that will be adopted by the computational intelligent multi-agent. The CMMI ontology is predefined by domain experts, and created by the ontology generating system. The computational intelligent multi-agent comprises a natural language processing agent, an ontological reasoning agent and a summary agent. The multi-agent deals with the evaluation reports from the natural language processing agent, infers the term relation strength between the ontology and the evaluation report, and then summarizes the main sentences of the evaluation report. The summary reports are meanwhile transmitted back to the domain expert, which makes the domain expert further adjust the CMMI ontology. Experimental results indicate that the ontology-based computational intelligent multi-agent can effectively summarize the evaluation reports for the CMMI assessment.

Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition

In recent years, the population has been aging gradually, and the number of patients with chronic respiratory disease has grown increasingly; therefore the respiratory healthcare plays an important role in the clinical care. This paper presents an ontology-based intelligent healthcare agent for the respiratory waveform recognition to assist the medical staff in judging the meaning of the graph reading from ventilators. The intelligent healthcare agent contains three modules, including the respiratory waveform ontology, ontology construction mechanism, and fuzzy recognition agent, to classify the respiratory waveform. The respiratory waveform ontology represents the respiratory domain knowledge, which will be utilized to classify and recognize the respiratory waveform by the intelligent healthcare agent. The ontology construction mechanism will infer the fuzzy numbers of each respiratory waveform from the patient or respiratory waveform repository. Next, the fuzzy recognition agent will classify and recognize the respiratory waveform into different types of respiratory waveforms. Finally, after the confirmation of medical experts, the classified and recognized results are stored in the classified waveform repository. The experimental results show that our approach can classify and recognize the respiratory waveform effectively.

Ontology-based multi-agents for intelligent healthcare applications

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.

Ontology-based Intelligent Decision Support Agent for CMMI Project Monitoring and Control

This paper presents an ontology-based intelligent decision support agent (OIDSA) to apply to Project Monitoring and Control (PMC) of Capability Maturity Model Integration (CMMI). The OIDSA is composed of three agents, namely a natural language processing agent, a fuzzy inference agent and a performance decision support agent. All the needed information is stored into an ontology repository, including the CMMI ontology and the project personal ontology (PPO), as well as Chinese dictionary. In addition, the natural language processing agent periodically collects the information of the project progress from project member to analyze the features of the terms for semantic concept clustering through document pre-processing and term filter process. Next, based on the CMMI ontology, the project personal ontology, and the processing results from the natural language processing agent, the fuzzy inference agent and performance decision support agent perform an inference mechanism to calculate the completed percentage of the project progress for each project member, then send the results out to the project manager for evaluating the performance of each project member. The experimental results show that the OIDSA can work effectively for PMC of CMMI.