面向专家的知识库优化

Expert-oriented optimization of knowledge base

知识库的质量是影响智能系统性能的主要因素 ,而知识获取一直是设计智能系统的瓶颈问题 ,这是由于目前人类认识的局限性 ,导致知识工程师和专家之间的不协调关系造成的 .为克服上述不利局面 ,本文利用粗糙集等理论 ,得到含有噪声的初始知识库 ,然后采用遗传算法、可视化技术和知识校验等技术对规则库和案例库进行了优化 .从而在知识获取过程中建立了知识工程师和专家之间的新型的关系 ,其中专家处于中心地位 ,知识工程师只是起辅助作用 。

The quality of knowledge base is important for knowledge intensive systems(KIS), such as expert systems and intelligent decision support systems. But over a long time, knowledge acquisition is a bottleneck in designing KIS due to the bad coordination between knowledge engineers and experts, which is limited by human epistemology level nowadays. To solve the problem, this paper utilizes some machine learning theories (mainly rough set theory) to get a rough knowledge base and then choose genetic algorithms, visualization technology, knowledge verification and validation technology to optimize it. We, in this way, build a new relation between knowledge engineers and experts in which experts play a decisive role while knowledge engineers are accessory, i.e., it is expert oriented during the knowledge acquisition course.