Modeling knowledge need awareness using the problematic situations elicited from questions and answers

Being aware of people's unuttered knowledge needs is the prerequisite of providing "active" and "in time" knowledge assistance. However, such an awareness of knowledge needs has been achieved at a high cost since most existing methods rely on the manually defined rules or a large amount of user data to work. In this paper, we formulate the problematic situations in task processing as knowledge application context (KAC), and propose to elicit KACs semi-automatically from domain Q&A archives. Assuming that the KACs frequently occurring and semantically matching with the user's task context are more likely to imply the knowledge needs of the user, we design a mechanism of knowledge need awareness (KNA) to predict users' knowledge needs in complex tasks. Experimental results show that the proposed method has significantly outperformed the information retrieval approaches used as baselines. The study provides a new method for reusing the contextualized knowledge in Q&A and thereby opens up a new way to build efficient active knowledge systems.

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