Acquiring Problem-Solving Knowledge from End Users: Putting Interdependency Models to the Test
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Towards open decision support systems based on semantic focused crawling
Expert Systems with Applications: An International Journal
Meta-evolution strategy to focused crawling on semantic web
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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Current knowledge acquisition tools have limited understanding of how users enter knowledge and how acquired knowledge is used, and provide limited assistance in organizing various knowledge authoring tasks. Users have to make up for these shortcomings by keeping track of past mistakes, current status, potential new problems, and possible courses of actions by themselves. In this paper, we present a novel extension to existing knowledge acquisition tools where the system organizes the episodes of past interactions through a set of declarative meta-level patterns and improves its suggestions based on relevant episodes. In particular, we focus on 1) assessing the level of confidence in suggesting an action, 2) suggesting how a knowledge authoring action can be done based on successful past actions, and 3) monitoring dynamic changes in the environment to suggest relevant modifications in the knowledge base. A preliminary study with varying synthetic user interactions shows that this meta-level assessment may reduce the number of incorrect suggestions, prevent some of the user mistakes and improve the overall problem solving results.