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Type-2 Fuzzy Ontology and its Application to Personal Diabetic Diet Recommendation","{\"openalex\":\"W2101161011\",\"doi\":\"10.1109/tfuzz.2010.2042454\"}","2026-05-23T06:35:43.210648618","2026-05-23T06:35:43.210650365","doi","10.1109/tfuzz.2010.2042454","arxivId","openalexId","W2101161011","abstractText"," 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. ","publishedDate","2010-01-01","publishedYear",2010,"venue","IEEE Transactions on Fuzzy Systems",222,"authors","keywords",[165,166,167],"pdfUrl","topVenue","Text and Document Classification Technologies","Rough Sets and Fuzzy Logic","Semantic Web and Ontologies","paper_W2126029446","A Fuzzy Expert System for Diabetes Decision Support Application","{\"openalex\":\"W2126029446\",\"doi\":\"10.1109/tsmcb.2010.2048899\"}","2026-05-22T08:38:48.182393211","2026-05-22T08:38:48.182395063","10.1109/tsmcb.2010.2048899","W2126029446","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.","2010-05-28","IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)",254,[180,181,182],"Artificial Intelligence in Healthcare","Biomedical Text Mining and Ontologies","Data Mining Algorithms and Applications","paper_W2106866704","A Fuzzy Ontology and Its Application to News Summarization","{\"openalex\":\"W2106866704\",\"doi\":\"10.1109/tsmcb.2005.845032\"}","2026-05-20T02:53:15.200763787","2026-05-20T02:53:15.208795536","10.1109/tsmcb.2005.845032","W2106866704","In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.","2005-09-20",2005,359,[195,196,197],"Topic Modeling","Advanced Text Analysis Techniques","Natural Language Processing Techniques",{"_60":199,"_62":200},[],[201],{"_65":202,"_67":203,"_69":204,"_71":205},1780632830207,{"_74":207,"_76":77,"_78":202,"_80":-5,"_81":82,"_83":82,"_84":82,"_85":-5,"_86":-5,"_87":88,"_35":89,"_90":91},[73,206],"[\"portal\",\"industries\"]","industries",[208,209,210,211,212,213,214,215,216,217,218,219],{"_15":281,"_29":282,"_31":-5,"_32":283,"_33":284,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":285,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":282,"_59":284},{"_15":277,"_29":278,"_31":-5,"_32":-5,"_33":279,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":280,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":278,"_59":279},{"_15":271,"_29":272,"_31":-5,"_32":273,"_33":274,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":275,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":272,"_59":274},{"_15":267,"_29":268,"_31":-5,"_32":-5,"_33":269,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":270,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":268,"_59":269},{"_15":263,"_29":264,"_31":-5,"_32":-5,"_33":265,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":266,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":264,"_59":265},{"_15":257,"_29":258,"_31":-5,"_32":259,"_33":260,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":261,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":258,"_59":260},{"_15":252,"_29":253,"_31":-5,"_32":-5,"_33":254,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":255,"_232":82,"_233":256,"_234":82,"_235":-5,"_58":253,"_59":254},{"_15":248,"_29":249,"_31":-5,"_32":-5,"_33":250,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":251,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":249,"_59":250},{"_15":244,"_29":245,"_31":-5,"_32":-5,"_33":246,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":247,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":245,"_59":246},{"_15":240,"_29":241,"_31":-5,"_32":-5,"_33":242,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":243,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":241,"_59":242},{"_15":236,"_29":237,"_31":-5,"_32":-5,"_33":238,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":239,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":237,"_59":238},{"_15":220,"_29":221,"_31":-5,"_32":-5,"_33":222,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":77,"_228":229,"_230":231,"_232":82,"_233":88,"_234":82,"_235":-5,"_58":221,"_59":222},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[262],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[276],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[286,287],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_290":24},"SITE_BASE_URL"]

Chang-Shing Lee 人物

Chang-Shing Lee · 相关论文