Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding

Xu, Chenjin; Nayyeri, Mojtaba; Alkhoury, Fouad; Yazdi, Hamed Shariat; Lehmann, Jens

doi:10.1007/978-3-030-62419-4_37

“…With the development of TKGs, TKG embedding (TKGE) draws increasing attention (Leblay and Chekol, 2018;Xu et al, 2020cXu et al, ,a, 2021Lacroix et al, 2020). An example of typical TKGE models is TTransE (Leblay and Chekol, 2018) which represents timestamps as latent vectors with entities and relations and incorporates time embeddings into its score function ||e s + r + τ − e o ||.…”

Section: Introductionmentioning

confidence: 99%

Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs

Xu¹,

Su²,

Lehmann³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time in such TKGs. Existing embeddingbased entity alignment approaches disregard time information that commonly exists in many large-scale KGs, leaving much room for improvement. In this paper, we focus on the task of aligning entity pairs between TKGs and propose a novel Time-aware Entity Alignment approach based on Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation and time information into the GNN structure of our model, we use a time-aware attention mechanism which assigns different weights to different nodes with orthogonal transformation matrices computed from embeddings of the relevant relations and timestamps in a neighborhood. Experimental results on multiple real-world TKG datasets show that our method significantly outperforms the state-ofthe-art methods due to the inclusion of time information. Our datasets and source code are available at https://github.com/ soledad921/TEA-GNN

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“…Temporal KG Reasoning. Reasoning on temporal KG can broadly be categorized into two settings, interpolation (Sadeghian et al, 2016;García-Durán et al, 2018;Leblay and Chekol, 2018;Dasgupta et al, 2018;Wu et al, 2019;Xu et al, 2020;Goel et al, 2020;Wu et al, 2020;Han et al, 2020a;Jung et al, 2020) and extrapolation (Trivedi et al, 2017(Trivedi et al, , 2018Han et al, 2020b;Deng et al, 2020;Jin et al, 2019Jin et al, , 2020Zhu et al, 2020;Li et al, 2021), as mentioned in Jin et al (2020). Under the former setting, models attempt to infer missing facts at historical timestamps.…”

Section: Related Workmentioning

confidence: 99%

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Li¹,

Jin²,

Guan³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.

show abstract

“…Temporal KG Reasoning. Reasoning on temporal KG can broadly be categorized into two settings, interpolation (Sadeghian et al, 2016;García-Durán et al, 2018;Leblay and Chekol, 2018;Dasgupta et al, 2018;Wu et al, 2019;Xu et al, 2020;Goel et al, 2020;Wu et al, 2020;Han et al, 2020a;Jung et al, 2020) and extrapolation (Trivedi et al, 2017(Trivedi et al, , 2018Han et al, 2020b;Deng et al, 2020;Jin et al, 2019Jin et al, , 2020Zhu et al, 2020;Li et al, 2021), as mentioned in Jin et al (2020). Under the former setting, models attempt to infer missing facts at historical timestamps.…”

Section: Related Workmentioning

confidence: 99%

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Li

¹

,

Jin

²

,

Guan

³

et al. 2021

Preprint

View full text Add to dashboard Cite

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.

show abstract

Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding

Cited by 54 publications

References 16 publications

Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs

Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

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