基于贝叶斯网络的话题建议增强了社区问答中的标签推理

Gerel Tumenbayar, Hung-Yu kao
{"title":"基于贝叶斯网络的话题建议增强了社区问答中的标签推理","authors":"Gerel Tumenbayar, Hung-Yu kao","doi":"10.1109/TAAI.2016.7880110","DOIUrl":null,"url":null,"abstract":"Since Web 2.0 emerges, users became very active in attending Web forum and Q&A Community. For the community about technology, engineering and science, it is likely that most of the professionals follow the same general path to study specific knowledge and this path would be between topics from basic one to specific one or from topic about old technology to a topic about new technology. Our work aims to find this general conditional relationship between topics by using Bayesian Network model and then use this model to suggest the reasonable topics for professionals to further study.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Topic suggestion by Bayesian network enhanced tag inference in community question answering\",\"authors\":\"Gerel Tumenbayar, Hung-Yu kao\",\"doi\":\"10.1109/TAAI.2016.7880110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since Web 2.0 emerges, users became very active in attending Web forum and Q&A Community. For the community about technology, engineering and science, it is likely that most of the professionals follow the same general path to study specific knowledge and this path would be between topics from basic one to specific one or from topic about old technology to a topic about new technology. Our work aims to find this general conditional relationship between topics by using Bayesian Network model and then use this model to suggest the reasonable topics for professionals to further study.\",\"PeriodicalId\":159858,\"journal\":{\"name\":\"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAAI.2016.7880110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAAI.2016.7880110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

自Web 2.0出现以来,用户在参加Web论坛和问答社区方面变得非常活跃。对于技术,工程和科学的社区来说,大多数专业人员可能会遵循相同的一般路径来学习特定的知识,这种路径可能是在从基础到特定的主题之间,或者从旧技术的主题到新技术的主题之间。我们的工作旨在通过贝叶斯网络模型找到主题之间的这种一般条件关系,然后利用该模型提出合理的主题供专业人员进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Topic suggestion by Bayesian network enhanced tag inference in community question answering
Since Web 2.0 emerges, users became very active in attending Web forum and Q&A Community. For the community about technology, engineering and science, it is likely that most of the professionals follow the same general path to study specific knowledge and this path would be between topics from basic one to specific one or from topic about old technology to a topic about new technology. Our work aims to find this general conditional relationship between topics by using Bayesian Network model and then use this model to suggest the reasonable topics for professionals to further study.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A cluster-based opinion leader discovery in social network User behavior analysis and commodity recommendation for point-earning apps Extraction of proper names from myanmar text using latent dirichlet allocation Heuristic algorithm for target coverage with connectivity fault-tolerance problem in wireless sensor networks AFIS: Aligning detail-pages for full schema induction
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1