Research progress of knowledge graph based on knowledge base embedding

Tang Caifang; Rao Yuan; Yu Hualei; Cheng Jiamin

doi:10.1007/978-981-13-2206-8_16

Research progress of knowledge graph based on knowledge base embedding

Tang Caifang
, Rao Yuan
, Yu Hualei
, Cheng Jiamin

电子与信息学部

Xi'an Jiaotong University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

The knowledge Graph (KGs) is a valuable tool and useful resource to describe the entities and their relationships in various natural language processing tasks. Especially, the insufficient semantic of entities and relationship in text limited the efficiency and accuracy of knowledge representation. With the increasing of knowledge base resources, many scholars began to study the knowledge graph’s construction technology based on knowledge base embedding. The basic idea is that the knowledge graph will be treated as a recursive process. Through utilizing the knowledge base’s resources and the semantic representation of text characteristic, we can extend the new features that improve learning performance and knowledge graph completeness. In this paper, we give a general overview of knowledge graph’s construction research based on knowledge embedding, including knowledge representation, knowledge embedding and so on. Then we summarize the challenge for the knowledge graph and the future development trend.

源语言	英语
主期刊名	Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings
编辑	Qinglei Zhou, Hongzhi Wang, Wei Xie, Zeguang Lu, Qiguang Miao, Yan Wang
出版商	Springer Verlag
页	176-191
页数	16
ISBN（印刷版）	9789811322051
DOI	https://doi.org/10.1007/978-981-13-2206-8_16
出版状态	已出版 - 2018
活动	4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018 - Zhengzhou, 中国期限: 21 9月 2018 → 23 9月 2018

出版系列

姓名	Communications in Computer and Information Science
卷	902
ISSN（印刷版）	1865-0929

会议

会议	4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018
国家/地区	中国
市	Zhengzhou
时期	21/09/18 → 23/09/18

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10.1007/978-981-13-2206-8_16

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引用此

Caifang, T., Yuan, R., Hualei, Y., & Jiamin, C. (2018). Research progress of knowledge graph based on knowledge base embedding. 在 Q. Zhou, H. Wang, W. Xie, Z. Lu, Q. Miao, & Y. Wang (编辑), Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings (页码 176-191). (Communications in Computer and Information Science; 卷 902). Springer Verlag. https://doi.org/10.1007/978-981-13-2206-8_16

Caifang, Tang ; Yuan, Rao ; Hualei, Yu 等. / Research progress of knowledge graph based on knowledge base embedding. Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings. 编辑 / Qinglei Zhou ; Hongzhi Wang ; Wei Xie ; Zeguang Lu ; Qiguang Miao ; Yan Wang. Springer Verlag, 2018. 页码 176-191 (Communications in Computer and Information Science).

@inproceedings{54773ebcefbe47a79dfa9ec91086002c,

title = "Research progress of knowledge graph based on knowledge base embedding",

abstract = "The knowledge Graph (KGs) is a valuable tool and useful resource to describe the entities and their relationships in various natural language processing tasks. Especially, the insufficient semantic of entities and relationship in text limited the efficiency and accuracy of knowledge representation. With the increasing of knowledge base resources, many scholars began to study the knowledge graph{\textquoteright}s construction technology based on knowledge base embedding. The basic idea is that the knowledge graph will be treated as a recursive process. Through utilizing the knowledge base{\textquoteright}s resources and the semantic representation of text characteristic, we can extend the new features that improve learning performance and knowledge graph completeness. In this paper, we give a general overview of knowledge graph{\textquoteright}s construction research based on knowledge embedding, including knowledge representation, knowledge embedding and so on. Then we summarize the challenge for the knowledge graph and the future development trend.",

keywords = "Deep learning, Knowledge embedding, Knowledge graph, Knowledge representation",

author = "Tang Caifang and Rao Yuan and Yu Hualei and Cheng Jiamin",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018 ; Conference date: 21-09-2018 Through 23-09-2018",

year = "2018",

doi = "10.1007/978-981-13-2206-8\_16",

language = "英语",

isbn = "9789811322051",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "176--191",

editor = "Qinglei Zhou and Hongzhi Wang and Wei Xie and Zeguang Lu and Qiguang Miao and Yan Wang",

booktitle = "Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings",

}

Caifang, T, Yuan, R, Hualei, Y & Jiamin, C 2018, Research progress of knowledge graph based on knowledge base embedding. 在 Q Zhou, H Wang, W Xie, Z Lu, Q Miao & Y Wang (编辑), Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings. Communications in Computer and Information Science, 卷 902, Springer Verlag, 页码 176-191, 4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018, Zhengzhou, 中国, 21/09/18. https://doi.org/10.1007/978-981-13-2206-8_16

Research progress of knowledge graph based on knowledge base embedding. / Caifang, Tang; Yuan, Rao; Hualei, Yu 等.
Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings. 编辑 / Qinglei Zhou; Hongzhi Wang; Wei Xie; Zeguang Lu; Qiguang Miao; Yan Wang. Springer Verlag, 2018. 页码 176-191 (Communications in Computer and Information Science; 卷 902).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Research progress of knowledge graph based on knowledge base embedding

AU - Caifang, Tang

AU - Yuan, Rao

AU - Hualei, Yu

AU - Jiamin, Cheng

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.

PY - 2018

Y1 - 2018

N2 - The knowledge Graph (KGs) is a valuable tool and useful resource to describe the entities and their relationships in various natural language processing tasks. Especially, the insufficient semantic of entities and relationship in text limited the efficiency and accuracy of knowledge representation. With the increasing of knowledge base resources, many scholars began to study the knowledge graph’s construction technology based on knowledge base embedding. The basic idea is that the knowledge graph will be treated as a recursive process. Through utilizing the knowledge base’s resources and the semantic representation of text characteristic, we can extend the new features that improve learning performance and knowledge graph completeness. In this paper, we give a general overview of knowledge graph’s construction research based on knowledge embedding, including knowledge representation, knowledge embedding and so on. Then we summarize the challenge for the knowledge graph and the future development trend.

AB - The knowledge Graph (KGs) is a valuable tool and useful resource to describe the entities and their relationships in various natural language processing tasks. Especially, the insufficient semantic of entities and relationship in text limited the efficiency and accuracy of knowledge representation. With the increasing of knowledge base resources, many scholars began to study the knowledge graph’s construction technology based on knowledge base embedding. The basic idea is that the knowledge graph will be treated as a recursive process. Through utilizing the knowledge base’s resources and the semantic representation of text characteristic, we can extend the new features that improve learning performance and knowledge graph completeness. In this paper, we give a general overview of knowledge graph’s construction research based on knowledge embedding, including knowledge representation, knowledge embedding and so on. Then we summarize the challenge for the knowledge graph and the future development trend.

KW - Deep learning

KW - Knowledge embedding

KW - Knowledge graph

KW - Knowledge representation

UR - https://www.scopus.com/pages/publications/85053994602

U2 - 10.1007/978-981-13-2206-8_16

DO - 10.1007/978-981-13-2206-8_16

M3 - 会议稿件

AN - SCOPUS:85053994602

SN - 9789811322051

T3 - Communications in Computer and Information Science

SP - 176

EP - 191

BT - Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings

A2 - Zhou, Qinglei

A2 - Wang, Hongzhi

A2 - Xie, Wei

A2 - Lu, Zeguang

A2 - Miao, Qiguang

A2 - Wang, Yan

PB - Springer Verlag

T2 - 4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

Y2 - 21 September 2018 through 23 September 2018

ER -

Caifang T, Yuan R, Hualei Y, Jiamin C. Research progress of knowledge graph based on knowledge base embedding. 在 Zhou Q, Wang H, Xie W, Lu Z, Miao Q, Wang Y, 编辑, Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings. Springer Verlag. 2018. 页码 176-191. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-2206-8_16

Research progress of knowledge graph based on knowledge base embedding

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