REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs

Shichao Pei; Lu Yu; Guoxian Yu; Xiangliang Zhang

doi:10.1145/3394486.3403268

REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs

Shichao Pei
, Lu Yu
, Guoxian Yu
, Xiangliang Zhang

Computer Science

King Abdullah University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	2175-2184
Number of pages	10
ISBN (Electronic)	9781450379984
DOIs	https://doi.org/10.1145/3394486.3403268
State	Published - Aug 23 2020
Event	26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States Duration: Aug 23 2020 → Aug 27 2020

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Country/Territory	United States
City	Virtual, Online
Period	8/23/20 → 8/27/20

ASJC Scopus Subject Areas

Software
Information Systems

Keywords

entity alignment
knowledge graph
noise detection

Access to Document

10.1145/3394486.3403268

Cite this

Pei, S., Yu, L., Yu, G., & Zhang, X. (2020). REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs. In KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2175-2184). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3394486.3403268

REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs. / Pei, Shichao; Yu, Lu; Yu, Guoxian et al.
KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2020. p. 2175-2184 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pei, S, Yu, L, Yu, G & Zhang, X 2020, REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs. in KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 2175-2184, 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020, Virtual, Online, United States, 8/23/20. https://doi.org/10.1145/3394486.3403268

Pei S, Yu L, Yu G, Zhang X. REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs. In KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2020. p. 2175-2184. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3394486.3403268

Pei, Shichao ; Yu, Lu ; Yu, Guoxian et al. / REA : Robust Cross-lingual Entity Alignment between Knowledge Graphs. KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2020. pp. 2175-2184 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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title = "REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs",

keywords = "entity alignment, knowledge graph, noise detection",

author = "Shichao Pei and Lu Yu and Guoxian Yu and Xiangliang Zhang",

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REA: Robust Cross-lingual Entity Alignment between Knowledge Graphs

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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