Abstract:
:The ability to automatically categorize submitted questions based on topics and suggest similar question and answer to the users reduces the number of redundant questions. Our objective was to compare intra-topic and inter-topic similarity between question and answers by using concept-based similarity computing analysis. We gathered existing question and answers from several popular online health communities. Then, Unified Medical Language System concepts related to selected questions and experts in different topics were extracted and weighted by term frequency -inverse document frequency values. Finally, the similarity between weighted vectors of Unified Medical Language System concepts was computed. Our result showed a considerable gap between intra-topic and inter-topic similarities in such a way that the average of intra-topic similarity (0.095, 0.192, and 0.110, respectively) was higher than the average of inter-topic similarity (0.012, 0.025, and 0.018, respectively) for questions of the top 3 popular online communities including NetWellness, WebMD, and Yahoo Answers. Similarity scores between the content of questions answered by experts in the same and different topics were calculated as 0.51 and 0.11, respectively. Concept-based similarity computing methods can be used in developing intelligent question and answering retrieval systems that contain auto recommendation functionality for similar questions and experts.
journal_name
Health Informatics Jjournal_title
Health informatics journalauthors
Naderi H,Madani S,Kiani B,Etminani Kdoi
10.1177/1460458219881333subject
Has Abstractpub_date
2020-06-01 00:00:00pages
1443-1454issue
2eissn
1460-4582issn
1741-2811journal_volume
26pub_type
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