A knowledge creation innovation for Web-Knowledge-Base System using knowledge management, and data and knowledge engineering
In the inference engine implementation of Knowledge Innovation such as Web-Knowledge-Based System (KBS), Data and Knowledge Engineering Technology (DKET) called Ensemble Learning is widely selected to manage and create the specific knowledge in both Tacit and Explicit knowledge. By the concept of th...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference Proceeding |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84962860437&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/53446 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Summary: | In the inference engine implementation of Knowledge Innovation such as Web-Knowledge-Based System (KBS), Data and Knowledge Engineering Technology (DKET) called Ensemble Learning is widely selected to manage and create the specific knowledge in both Tacit and Explicit knowledge. By the concept of this approach is the combination of data sets or Knowledge Discovery in Database methods for training or testing the developed system. This method is applied to develop several software systems especially, intelligent system, Knowledge Management System etc. In this paper, CBR-C5.0-CART Web-Knowledge-Based System is proposed in term of theory and application based on the incomplete information problem of medical diagnosis (Thalassemia: a kind of genetic disorder) using Knowledge Management processes. Moreover, Systems Thinking is presented to create the novel knowledge such as the new methodology in form of the system analysis and design presented by the framework. The results of this study reveal that the accuracy percentages of C5.0, CART and CBR-C5.0-CART based on expert opinion are 84.25, 77.17 and 76.38 respectively. On the other hand, the result of CBR-C5.0-CART without expert opinion is 89.76. In the future, the time series data related to this disease will be collected for constructing the Web-KBS and the obtained result will be compared to discover the best algorithm. |
|---|