mlsanityData to 14 Oct 2026

Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes

arXiv 2111.06854 ↗First posted 12 Nov 2021cs.AIcited by 6

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Abstract (arXiv)

Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement textitmay be associated with a temporal scope, has attracted growing attention. Prior works assume that each statement in a TKB textitmust be associated with a temporal scope. This ignores the fact that the scoping information is commonly missing in a KB. Thus prior work is typically incapable of handling generic use cases where a TKB is composed of temporal statements with/without a known temporal scope. In order to address this issue, we establish a new knowledge base embedding framework, called TIME2BOX, that can deal with atemporal and temporal statements of different types simultaneously. Our main insight is that answers to a temporal query always belong to a subset of answers to a time-agnostic counterpart. Put differently, time is a filter that helps pick out answers to be correct during certain periods. We introduce boxes to represent a set of answer entities to a time-agnostic query. The filtering functionality of time is modeled by intersections over these boxes. In addition, we generalize current evaluation protocols on time interval prediction. We describe experiments on two datasets and show that the proposed method outperforms state-of-the-art (SOTA) methods on both link prediction and time prediction.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 12 Nov 2021.

Builds on

19 of 35 references.

References · 19

  1. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  2. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  3. 2017Deep Setscited by 1,421
  4. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  5. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  6. 2016Complex Embeddings for Simple Link Predictioncited by 902
  7. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  8. 2020Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddingscited by 171
  9. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  10. 2018Learning Sequence Encoders for Temporal Knowledge Graph Completioncited by 145
  11. 2020Tensor Decompositions for temporal knowledge base completioncited by 115
  12. 2019Diachronic Embedding for Temporal Knowledge Graph Completioncited by 104
7 more
  1. 2018Probabilistic Embedding of Knowledge Graphs with Box Lattice Measurescited by 64
  2. 2015Modeling Relation Paths for Representation Learning of Knowledge Basescited by 56
  3. 2020Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocolscited by 30
  4. 2018Embedding Models for Episodic Knowledge Graphscited by 25
  5. 2019Temporal Knowledge Graph Embedding Model based on Additive Time Series Decompositioncited by 24
  6. 2019TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Predictioncited by 17
  7. 2019Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphscited by 8

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Citing papers per year: 2021: 1; 2022: 1; 2023: 4.

6 papers.