knowledge-base-3

Temporal Knowledge Graph Completion

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Learning Sequence Encoders for Temporal Knowledge Graph Completion

Mathias Niepert, Alberto García-Durán, Sebastijan Dumancic•Sun Sep 09 2018

This work utilizes recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods to incorporate temporal information.

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Diachronic Embedding for Temporal Knowledge Graph Completion

Marcus A. Brubaker, Rishab Goel, P. Poupart, Seyed Mehran Kazemi•Fri Jul 05 2019

Novel models for temporal KG completion are built through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time where only static entity features are provided.

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Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols

Prachi Jain, Mausam, Soumen Chakrabarti, Sushant Rathi•Fri May 01 2020

This paper presents TIMEPLEX, a novel time-aware KBC method, that also automatically exploits the recurrent nature of some relations and temporal interactions between pairs of relations, and achieves state-of-the-art performance on both prediction tasks.

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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

William L. Hamilton, J. Cheung, Mengyao Cao, Jiapeng Wu•Tue Oct 06 2020

The Temporal Message Passing (TeMP) framework is proposed, which combines graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques to address the temporal sparsity and variability of entity distributions in TKGs.

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DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Yunpu Ma, Volker Tresp, Zhen Han, Peng Chen•Sat Oct 31 2020

Dy- ERNIE is proposed, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data.

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Tucker decomposition-based Temporal Knowledge Graph Completion

Feihu Che, J. Tao, Pengpeng Shao, Guohua Yang, Dawei Zhang•Sun Nov 15 2020

A new tensor decomposition model for temporal knowledge graphs completion inspired by the Tucker decomposition of order 4 tensor is built and it is demonstrated that the model outperforms baselines with an explicit margin on link prediction task.

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TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

J. Cheung, Chen Ma, Jiapeng Wu, Yishi Xu, Yingxue Zhang, M. Coates•Fri Apr 16 2021

The Time-aware Incremental Embedding (TIE) framework is presented, which combines TKG representation learning, experience replay, and temporal regularization, and introduces a set of metrics that characterizes the intransigence of the model and proposes a constraint that associates the deleted facts with negative labels.

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Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings

M. Nayyeri, Jens Lehmann, Chengjin Xu, Yung-Yu Chen•Mon May 31 2021

A novel time-aware knowledge graph embebdding approach, TeLM, which performs 4th-order tensor factorization of a Temporal knowledge graph using a Linear temporal regularizer and Multivector embeddings and investigates the effect of the temporal dataset’s time granularity on temporal knowledge graph completion.

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Temporal Knowledge Graph Completion using Box Embeddings

Ralph Abboud, .Ismail .Ilkan Ceylan, Johannes Messner•Fri Sep 17 2021

This paper proposes BoxTE, a box embedding model for TKGC, building on the static knowledge graph embeddingmodel BoxE, and shows that BoxTE is fully expressive, and possesses strong inductive capacity in the temporal setting.

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Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations

G. Neumann, Ioannis Dikeoulias, Saadullah Amin•Sat Apr 09 2022

A cycle-aware time-encoding scheme for time features, which is model-agnostic and offers a more generalized representation of time, is proposed and implemented in a unified temporal knowledge graph embedding framework.

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