The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion
Abstract
Static knowledge graphs (KGs), despite their wide usage in relational reasoning and downstream tasks, fall short of realistic modeling of knowledge and facts that are only temporarily valid. Compared to static knowledge graphs, temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge. Naturally, automatic TKG completion has drawn much research interests for a more realistic modeling of relational reasoning. However, most of the existing models for TKG completion extend static KG embeddings that do not fully exploit TKG structure, thus lacking in 1) accounting for temporally relevant events already residing in the local neighborhood of a query, and 2) path-based inference that facilitates multi-hop reasoning and better interpretability. In this paper, we propose T-GAP, a novel model for TKG completion that maximally utilizes both temporal information and graph structure in its encoder and decoder. T-GAP encodes query-specific substructure of TKG by focusing on the temporal displacement between each event and the query timestamp, and performs path-based inference by propagating attention through the graph. Our empirical experiments demonstrate that T-GAP not only achieves superior performance against state-of-the-art baselines, but also competently generalizes to queries with unseen timestamps. Through extensive qualitative analyses, we also show that T-GAP enjoys transparent interpretability, and follows human intuition in its reasoning…
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In the citing paper
“Therefore, the inferred attention distribution is used as the natural interpretation of the prediction.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Jung et al., 2021
In the citing paper
“To better learn the longterm dependency of facts, [Jung et al., 2021] develops a pathbased multi-hop reasoning process by propagating attention through the edges on the knowledge graph (with attention flow [Xu et al., 2018]).”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Jung et al., 2021
In the citing paper
“A two-stage GNN is adopted, with the temporal displacement included, so that the structural features of query-related entities and relations are also captured by the reasoning process.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Jung et al., 2021
Citations
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Cited by
Sorted by citations
A Survey on Knowledge Graph Embeddings for Link Prediction
Wang, M., Qiu, L., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13030485 · Cited by 212
Temporal Knowledge Graph Completion: A Survey
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/734 · Cited by 133
Metadata
- Authors
- Jung, J., Jung, J., & Kang, U.
- Affiliations
- Seoul National University & Kakao Enterprise, Seoul, South Korea
- Jeonbuk National University, Jeonju, South Korea
- Seoul National University, Seoul, South Korea
- Venue
- Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2021-08-14
- Pages
- 786-795
- Cited by
- 82
- References
- 3
- Funders
- National Research Foundation of Korea (2019R1A2C2004990)
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- Jung, J., Jung, J., & Kang, U. (2021). Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467292
