TY - GEN
T1 - Meta-level patterns for interactive knowledge capture
AU - Kim, Jihie
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Knowledge acquisition
UR - http://www.scopus.com/inward/record.url?scp=33846533649&partnerID=8YFLogxK
U2 - 10.1145/1088622.1088671
DO - 10.1145/1088622.1088671
M3 - Conference contribution
AN - SCOPUS:33846533649
SN - 1595931635
SN - 9781595931634
T3 - Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05
SP - 207
EP - 208
BT - Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05
T2 - 3rd International Conference on Knowledge Capture, K-CAP'05
Y2 - 2 October 2005 through 5 October 2005
ER -