The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs
Open accessAbstract
Knowledge graph reasoning is a critical task in natural language processing.The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future.This paper proposes Recurrent Event Network (RE-NET), a novel autoregressive architecture for predicting future interactions.The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs.Specifically, our RE-NET employs a recurrent event encoder to encode past facts, and uses a neighborhood aggregator to model the connection of facts at the same timestamp.Future facts can then be inferred in a sequential manner based on the two modules.We evaluate our proposed method via link prediction at future times on five public datasets.Through extensive experiments, we demonstrate the strength of RE-NET, especially on multi-step inference over future timestamps, and achieve state-of-the-art performance on all five datasets 1 .
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Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
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Self-citationFoundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
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Temporal Knowledge Graph Completion: A Survey
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A Survey on Temporal Knowledge Graph: Representation Learning and Applications
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On the Evolution of Knowledge Graphs: A Survey and Perspective
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Metadata
- Authors
- Jin, W., Qu, M., Jin, X., & Ren, X.
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 6669-6683
- OpenAlex
- W3097986917
- Cited by
- 409
- References
- 32
- FWCI
- 16.23
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Jin, W., Qu, M., Jin, X., & Ren, X. (2020). Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.18653/v1/2020.emnlp-main.541
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