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Knowledge map construction for question and answer archives
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AI
DOI：10.1016/j.eswa.2019.112923.png)
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摘要
En 中文
The rapid increase in the number of community-based question-and-answer services has built up large archives of questions and answers. These archives deliver a plethora of valuable knowledge to users who primarily browse to locate information. Question-answer pairs, which not only include knowledge content but also indicate knowledge needs, are a new form of information presentation. To facilitate browsing question and answer archives and alleviate information overload during the browsing process, this paper constructs a knowledge map for question and answer archives by exploiting question-answer pair characteristics to determine a more precise location of question-answer pairs. First, the questions and answers are modeled and then the knowledge map structure is completed. Questions and answers are the two main dimensions of this knowledge map, and their intersection comprises a cluster of corresponding question-answer pairs. The knowledge map can be widely and deeply extended to provide a comprehensive representation of the question and answer archive. When labeling the knowledge map to aid in interpretation and understanding, the key words are identified, and typical question-answer pair extraction methods are proposed. Finally, we conduct extensive experiments on a real dataset, and the results show that the proposed approach is feasible and performs well. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Knowledge map
Community question answering
Artificial neural network
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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引用论文
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