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Cited by 23

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion

Bai, Luyi, Ma, Xiangnan, Zhang, Mingcheng, Yu, Wenting·2021·ACM Transactions on Knowledge Discovery from Data
DOI 10.1145/3443687
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Abstract

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.

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Metadata

Authors
Bai, L., Ma, X., Zhang, M., & Yu, W.
Affiliations
School of Computer and Communication Engineering, Northeastern University, China
ORCID
Bai, Luyi
Venue
ACM Transactions on Knowledge Discovery from Data
Publisher
Association for Computing Machinery (ACM)
Published
2021-04-21
Volume
15
Issue
3
Pages
1-17
ISSN
  • 1556-4681
  • 1556-472X
DOI
10.1145/3443687
Cited by
23
References
22
Language
en
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
  • National Natural Science Foundation of China (61402087)
  • Natural Science Foundation of Hebei Province (F2019501030)
  • Natural Science Foundation of Liaoning Province (2019-MS-130)
  • Key Project of Scientific Research Funds in Colleges and Universities of Hebei Education Department (ZD2020402)
  • Fundamental Research Funds for the Central Universities (N2023019)
  • Program for 333 Talents in Hebei Province (A202001066)

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CoChat. (2026). Citation check: TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion. https://app.cochat.ai/papers/10.1145/3443687
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Bai, L., Ma, X., Zhang, M., & Yu, W. (2021). TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion. ACM Transactions on Knowledge Discovery from Data. https://doi.org/10.1145/3443687

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