Knowledge map construction for question and answer archives. (1st March 2020)

• Record Type: Journal Article
• Authors: Li, Ming; Lu, Xiuzhi; Chen, Lisheng; Wang, Jun
• Is Part Of: Expert systems with applications. Volume 141(2020)
• Publication Date: 2020-03-01
• Subjects: Knowledge map -- Community question answering -- Artificial neural network; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.112923
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel knowledge map for question and answer archives is constructed. A novel growing hierarchical 2-dimensional self-organizing map is proposed. Extracting typical Q&A pairs is proposed to help users quickly grasp main contents. A merging mechanism is proposed during vertical expansion to avoid parsing. Experimental results show that the knowledge map is feasible and performs well. Abstract: 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.