{"title":"A Method of Knowledge Creation and Knowledge Utilization by Generalized Inverse Operator","authors":"Ryotaro Okada, T. Nakanishi, T. Kitagawa","doi":"10.1109/IIAI-AAI.2014.60","DOIUrl":null,"url":null,"abstract":"We represent a new model of knowledge creation and knowledge utilization. Recently, the one of the important knowledge management issues is how to create knowledge and how to utilize their knowledge. Actually, we cannot maximize the accumulated know-how on various fields, although there are various and a massive of data on the World Wide Web. It is important to consider how to organize knowledge and how to use their knowledge. The one of the solutions is to measurement correlation between data sets on heterogeneous fields. Furthermore, we have to consider how to use the organized data in the interconnection. We propose a representation method of knowledge as a matrix. Generally, a matrix represents relationships between each element of rows and columns. This is the one of the knowledge creation, because the matrix creation process is as same as the relationships between each element of data as knowledge. Moreover, we represent that the generalized inverse operation of the matrix is as same as knowledge utilization. Knowledge creation and knowledge utilization can be regarded as reverse operation. Therefore, we can define that knowledge utilization operator as generalized inverse operator, when we abstract knowledge as a matrix from various data. The features of this method are simplification of a knowledge representation, an inverse operation of knowledge creation and knowledge utilization, and the knowledge management by correlation. By our proposed method, we get new values by interconnection among data sets that are in heterogeneous fields.","PeriodicalId":432222,"journal":{"name":"2014 IIAI 3rd International Conference on Advanced Applied Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IIAI 3rd International Conference on Advanced Applied Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI.2014.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We represent a new model of knowledge creation and knowledge utilization. Recently, the one of the important knowledge management issues is how to create knowledge and how to utilize their knowledge. Actually, we cannot maximize the accumulated know-how on various fields, although there are various and a massive of data on the World Wide Web. It is important to consider how to organize knowledge and how to use their knowledge. The one of the solutions is to measurement correlation between data sets on heterogeneous fields. Furthermore, we have to consider how to use the organized data in the interconnection. We propose a representation method of knowledge as a matrix. Generally, a matrix represents relationships between each element of rows and columns. This is the one of the knowledge creation, because the matrix creation process is as same as the relationships between each element of data as knowledge. Moreover, we represent that the generalized inverse operation of the matrix is as same as knowledge utilization. Knowledge creation and knowledge utilization can be regarded as reverse operation. Therefore, we can define that knowledge utilization operator as generalized inverse operator, when we abstract knowledge as a matrix from various data. The features of this method are simplification of a knowledge representation, an inverse operation of knowledge creation and knowledge utilization, and the knowledge management by correlation. By our proposed method, we get new values by interconnection among data sets that are in heterogeneous fields.