{"title":"基于本体知识的机器学习框架概念","authors":"Kanjana Sudathip, M. Sodanil","doi":"10.1145/3011141.3011207","DOIUrl":null,"url":null,"abstract":"In the objective of this paper was to present ontology knowledge-based design and development to explain concepts and machine learning techniques which were compiled from book, articles, research and websites that publish information. The database structure includes 4 application domains: 1) learning 2) learning techniques 3) learning evaluation and 4) machine learning technique applications. The experimental evaluation was conducted by retrieving data using question sets. The results of the evaluation showed precision value at 99.65 percent and recall value at 95.90 percent. This machine learning ontology could be applied to other related information systems and databases for future development and further research.","PeriodicalId":247823,"journal":{"name":"Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Ontology knowledge-based framework for machine learning concept\",\"authors\":\"Kanjana Sudathip, M. Sodanil\",\"doi\":\"10.1145/3011141.3011207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the objective of this paper was to present ontology knowledge-based design and development to explain concepts and machine learning techniques which were compiled from book, articles, research and websites that publish information. The database structure includes 4 application domains: 1) learning 2) learning techniques 3) learning evaluation and 4) machine learning technique applications. The experimental evaluation was conducted by retrieving data using question sets. The results of the evaluation showed precision value at 99.65 percent and recall value at 95.90 percent. This machine learning ontology could be applied to other related information systems and databases for future development and further research.\",\"PeriodicalId\":247823,\"journal\":{\"name\":\"Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3011141.3011207\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3011141.3011207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ontology knowledge-based framework for machine learning concept
In the objective of this paper was to present ontology knowledge-based design and development to explain concepts and machine learning techniques which were compiled from book, articles, research and websites that publish information. The database structure includes 4 application domains: 1) learning 2) learning techniques 3) learning evaluation and 4) machine learning technique applications. The experimental evaluation was conducted by retrieving data using question sets. The results of the evaluation showed precision value at 99.65 percent and recall value at 95.90 percent. This machine learning ontology could be applied to other related information systems and databases for future development and further research.