MemTimes: temporal scoping of facts with memory network

Siyuan Cao; Qiang Yang; Zhixu Li; Guanfeng Liu; Detian Zhang; Jiajie Xu

doi:10.1007/978-3-030-59419-0_5

MemTimes: temporal scoping of facts with memory network

Siyuan Cao, Qiang Yang, Zhixu Li^*, Guanfeng Liu, Detian Zhang, Jiajie Xu

^*Corresponding author for this work

School of Computing

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

Abstract

This paper works on temporal scoping, i.e., adding time interval to facts in Knowledge Bases (KBs). The existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we propose a novel neural model based on Memory Network to do temporal reasoning among sentences for the purpose of temporal scoping. We design proper ways to encode both semantic and temporal information contained in the mention set of each fact, which enables temporal reasoning with Memory Network. We also find ways to remove the effect brought by noisy sentences, which can further improve the robustness of our approach. The experiments show that this solution is highly effective for detecting temporal scope of facts.

Original language	English
Title of host publication	Database Systems for Advanced Applications
Subtitle of host publication	25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part III
Editors	Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	70-86
Number of pages	17
ISBN (Electronic)	9783030594190
ISBN (Print)	9783030594183
DOIs	https://doi.org/10.1007/978-3-030-59419-0_5
Publication status	Published - 2020
Event	25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 - Jeju, Korea, Republic of Duration: 24 Sept 2020 → 27 Sept 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12114 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Country/Territory	Korea, Republic of
City	Jeju
Period	24/09/20 → 27/09/20

Keywords

Iterative Model
Memory Network
Temporal Scoping

Access to Document

10.1007/978-3-030-59419-0_5

Cite this

Cao, S., Yang, Q., Li, Z., Liu, G., Zhang, D., & Xu, J. (2020). MemTimes: temporal scoping of facts with memory network. In Y. Nah, B. Cui, S.-W. Lee, J. X. Yu, Y.-S. Moon, & S. E. Whang (Eds.), Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part III (pp. 70-86). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12114 LNCS). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-59419-0_5

Cao, Siyuan ; Yang, Qiang ; Li, Zhixu et al. / MemTimes : temporal scoping of facts with memory network. Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part III. editor / Yunmook Nah ; Bin Cui ; Sang-Won Lee ; Jeffrey Xu Yu ; Yang-Sae Moon ; Steven Euijong Whang. Cham, Switzerland : Springer, Springer Nature, 2020. pp. 70-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3d58ae993ba84fcb9d117fffcdc80381,

title = "MemTimes: temporal scoping of facts with memory network",

abstract = "This paper works on temporal scoping, i.e., adding time interval to facts in Knowledge Bases (KBs). The existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we propose a novel neural model based on Memory Network to do temporal reasoning among sentences for the purpose of temporal scoping. We design proper ways to encode both semantic and temporal information contained in the mention set of each fact, which enables temporal reasoning with Memory Network. We also find ways to remove the effect brought by noisy sentences, which can further improve the robustness of our approach. The experiments show that this solution is highly effective for detecting temporal scope of facts.",

keywords = "Iterative Model, Memory Network, Temporal Scoping",

author = "Siyuan Cao and Qiang Yang and Zhixu Li and Guanfeng Liu and Detian Zhang and Jiajie Xu",

year = "2020",

doi = "10.1007/978-3-030-59419-0_5",

language = "English",

isbn = "9783030594183",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Springer Nature",

pages = "70--86",

editor = "Yunmook Nah and Bin Cui and Sang-Won Lee and Yu, {Jeffrey Xu} and Yang-Sae Moon and Whang, {Steven Euijong}",

booktitle = "Database Systems for Advanced Applications",

address = "United States",

note = "25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 ; Conference date: 24-09-2020 Through 27-09-2020",

}

Cao, S, Yang, Q, Li, Z, Liu, G, Zhang, D & Xu, J 2020, MemTimes: temporal scoping of facts with memory network. in Y Nah, B Cui, S-W Lee, JX Yu, Y-S Moon & SE Whang (eds), Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12114 LNCS, Springer, Springer Nature, Cham, Switzerland, pp. 70-86, 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020, Jeju, Korea, Republic of, 24/09/20. https://doi.org/10.1007/978-3-030-59419-0_5

MemTimes: temporal scoping of facts with memory network. / Cao, Siyuan; Yang, Qiang; Li, Zhixu et al.
Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part III. ed. / Yunmook Nah; Bin Cui; Sang-Won Lee; Jeffrey Xu Yu; Yang-Sae Moon; Steven Euijong Whang. Cham, Switzerland: Springer, Springer Nature, 2020. p. 70-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12114 LNCS).

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

TY - GEN

T1 - MemTimes

T2 - 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020

AU - Cao, Siyuan

AU - Yang, Qiang

AU - Li, Zhixu

AU - Liu, Guanfeng

AU - Zhang, Detian

AU - Xu, Jiajie

PY - 2020

Y1 - 2020

N2 - This paper works on temporal scoping, i.e., adding time interval to facts in Knowledge Bases (KBs). The existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we propose a novel neural model based on Memory Network to do temporal reasoning among sentences for the purpose of temporal scoping. We design proper ways to encode both semantic and temporal information contained in the mention set of each fact, which enables temporal reasoning with Memory Network. We also find ways to remove the effect brought by noisy sentences, which can further improve the robustness of our approach. The experiments show that this solution is highly effective for detecting temporal scope of facts.

AB - This paper works on temporal scoping, i.e., adding time interval to facts in Knowledge Bases (KBs). The existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we propose a novel neural model based on Memory Network to do temporal reasoning among sentences for the purpose of temporal scoping. We design proper ways to encode both semantic and temporal information contained in the mention set of each fact, which enables temporal reasoning with Memory Network. We also find ways to remove the effect brought by noisy sentences, which can further improve the robustness of our approach. The experiments show that this solution is highly effective for detecting temporal scope of facts.

KW - Iterative Model

KW - Memory Network

KW - Temporal Scoping

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U2 - 10.1007/978-3-030-59419-0_5

DO - 10.1007/978-3-030-59419-0_5

M3 - Conference proceeding contribution

AN - SCOPUS:85092102852

SN - 9783030594183

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 70

EP - 86

BT - Database Systems for Advanced Applications

A2 - Nah, Yunmook

A2 - Cui, Bin

A2 - Lee, Sang-Won

A2 - Yu, Jeffrey Xu

A2 - Moon, Yang-Sae

A2 - Whang, Steven Euijong

PB - Springer, Springer Nature

CY - Cham, Switzerland

Y2 - 24 September 2020 through 27 September 2020

ER -

Cao S, Yang Q, Li Z, Liu G, Zhang D, Xu J. MemTimes: temporal scoping of facts with memory network. In Nah Y, Cui B, Lee SW, Yu JX, Moon YS, Whang SE, editors, Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part III. Cham, Switzerland: Springer, Springer Nature. 2020. p. 70-86. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59419-0_5

MemTimes: temporal scoping of facts with memory network

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Keywords

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