mlsanityData to 7 Oct 2026

Neurosymbolic Methods for Dynamic Knowledge Graphs

arXiv 2409.04572 ↗First posted 6 Sep 2024cs.AIcited by 3

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Abstract (arXiv)

Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of entities and relations, making these KGs dynamic. This chapter formally defines several types of dynamic KGs and summarizes how these KGs can be represented. Additionally, many neurosymbolic methods have been proposed for learning representations over static KGs for several tasks such as KG completion and entity alignment. This chapter further focuses on neurosymbolic methods for dynamic KGs with or without temporal information. More specifically, it provides an insight into neurosymbolic methods for dynamic (temporal or non-temporal) KG completion and entity alignment tasks. It further discusses the challenges of current approaches and provides some future directions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 95 references that are in the map. First posted on arXiv 6 Sep 2024.

Builds on

36 of 95 references.

References · 36

  1. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 860
  2. 2020Knowledge Graphscited by 619
  3. 2018Learning Sequence Encoders for Temporal Knowledge Graph Completioncited by 145
  4. 2020Tensor Decompositions for temporal knowledge base completioncited by 115
  5. 2019Diachronic Embedding for Temporal Knowledge Graph Completioncited by 104
  6. 2020Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Frameworkcited by 70
  7. 2020Understanding Negative Sampling in Graph Representation Learningcited by 66
  8. 2022Temporal Knowledge Graph Completion: A Surveycited by 61
  9. 2020TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotationcited by 48
  10. 2018EventKG: A Multilingual Event-Centric Temporal Knowledge Graphcited by 43
  11. 2021Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphscited by 39
  12. 2023YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomycited by 37
24 more
  1. 2021Continual Learning of Knowledge Graph Embeddingscited by 35
  2. 2021Temporal Knowledge Graph Completion using Box Embeddingscited by 34
  3. 2019A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?cited by 31
  4. 2020DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completioncited by 30
  5. 2022Lifelong Embedding Learning and Transfer for Growing Knowledge Graphscited by 28
  6. 2020Disentangle-based Continual Graph Representation Learningcited by 25
  7. 2019Efficiently Embedding Dynamic Knowledge Graphscited by 19
  8. 2023Unsupervised Entity Alignment for Temporal Knowledge Graphscited by 19
  9. 2020Tucker decomposition-based Temporal Knowledge Graph Completioncited by 18
  10. 2024Towards Continual Knowledge Graph Embedding via Incremental Distillationcited by 17
  11. 2022Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphscited by 16
  12. 2023A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospectscited by 14
  13. 2024Fast and Continual Knowledge Graph Embedding via Incremental LoRAcited by 14
  14. 2021Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvaturescited by 12
  15. 2024Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completioncited by 10
  16. 2022RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completioncited by 9
  17. 2021A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completioncited by 8
  18. 2022A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphscited by 7
  19. 2022Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completioncited by 6
  20. 2021Leveraging Static Models for Link Prediction in Temporal Knowledge Graphscited by 5
  21. 2018EventKG+TL: Creating Cross-Lingual Timelines from an Event-Centric Knowledge Graphcited by 4
  22. 2023Towards Semantically Enriched Embeddings for Knowledge Graph Completioncited by 3
  23. 2021A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distributioncited by 2
  24. 2022Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representationscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Neurosymbolic Graph Enrichment for Grounded World Modelscited by 6
  2. 2025Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methodscited by 1
  3. 2026From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analyticscited by 0