CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Question Answering over Knowledge Bases
Liu Kang; Zhao Jun; He Shizhu; Zhang Yuanzhe
2015-09-01
发表期刊IEEE INTELLIGENT SYSTEMS
卷号30期号:5页码:26-35
文章类型Article
摘要The key of such translation is to understand the meaning of the question. The dominant methods usually convert a natural language question into a complete and formal meaning representation (FMR) first, such as logical form. Based on FMR, the structured query is then smoothly generated. However, completing this aim isn’t trivial. Four questions should be addressed: • How do we represent the meaning of questions grounded to a specific KB? This meaning representation should reflect the corresponding concepts in the KB and organize them according to their semantic relations in the question. The representation Deep Web search is on the cusp of a profound change, from simple document retrieval to natural language question answering (QA).1 Ultimately, search needs to precisely understand the meanings of users’ natural language questions, extract useful facts from all information on the Web, and select Natural L a n guage Processi n g september/october 2015 www.computer.org/intelligent 27 should be unambiguous and adapted to the machine for automated inference and processing. • How do we convert natural language questions to the predefined formal meanings? Text is usually ambiguous, which makes this conversion very difficult. Different formal meanings can be generated from the same text under different contexts, so efficient disambiguation models or rules should be constructed. • How do we scale the QA to a largescale, open domain KB? With the increase of Web data, the sizes of KBs inflate quickly. A KB could contain billions of entities, millions of relations, and thousands of domains, which makes the textual ambiguity more serious and also makes traditional rule-based or labeled datadependent methods infeasible. • How do we answer questions over multiple, interlinked KBs? In many scenarios, answering a question requires synthesizing multiple KBs. The contents from different KBs can make up each other, although their structures are heterogeneous. Many unknown alignments exist among entities, classes, and relations from various KBs. However, discovering such alignments and performing question conversion over multiple heterogeneous KBs isn’t easy. This article gives a rough sketch of question answering over KBs, looking at traditional approaches and attempting to answer these four questions.
关键词Question Answering Knowledge Bases
WOS标题词Science & Technology ; Technology
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000361315900004
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9006
专题模式识别国家重点实验室_自然语言处理
通讯作者Liu Kang
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714
Liu Kang,Zhao Jun,He Shizhu,et al. Question Answering over Knowledge Bases[J]. IEEE INTELLIGENT SYSTEMS,2015,30(5):26-35.
APA Liu Kang,Zhao Jun,He Shizhu,&Zhang Yuanzhe.(2015).Question Answering over Knowledge Bases.IEEE INTELLIGENT SYSTEMS,30(5),26-35.
MLA Liu Kang,et al."Question Answering over Knowledge Bases".IEEE INTELLIGENT SYSTEMS 30.5(2015):26-35.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Question Answering o(1292KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Liu Kang]的文章
[Zhao Jun]的文章
[He Shizhu]的文章
百度学术
百度学术中相似的文章
[Liu Kang]的文章
[Zhao Jun]的文章
[He Shizhu]的文章
必应学术
必应学术中相似的文章
[Liu Kang]的文章
[Zhao Jun]的文章
[He Shizhu]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Question Answering over Knowledge Base.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。