Name
Abstract | ||
|---|---|---|
This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1017/S0890060403173052 | AI EDAM |
Keywords | Field | DocType |
traditional approach,knowledge-based system,traditional interview-based knowledge acquisition,rapid development,integrated knowledge acquisition,data,heuristic expert knowledge,automatic rule generation,novel approach,knowledge base,linguistic rule,fuzzy inference,optional final interview,knowledge acquisition,optional initial interview,knowledge based system,data collection | Data mining,Systems engineering,Computer science,Artificial intelligence,Knowledge base,Heuristic,IDEF3,Knowledge-based systems,Knowledge extraction,Machine learning,Knowledge acquisition,Open Knowledge Base Connectivity,Legal expert system | Journal |
Volume | Issue | ISSN |
17 | 3 | 0890-0604 |
Citations | PageRank | References |
4 | 0.55 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Xing | 1 | 25 | 1.85 |
| Samuel H. Huang | 2 | 193 | 19.64 |
| J. Shi | 3 | 4 | 0.55 |