Temporal Knowledge Graph Reasoning Triggered by Memories

• URL: http://arxiv.org/abs/2110.08765v1
• Date: Sun, 17 Oct 2021 08:58:30 GMT
• Title: Temporal Knowledge Graph Reasoning Triggered by Memories
• Abstract summary: We propose a memory-triggered decision-making (MTDM) network, which incorporates transient memories, long-short-term memories, and deep memories. Specifically, the transient learning network considers transient memories as a static knowledge graph, and the time-aware recurrent evolution network learns representations through a sequence of recurrent evolution units from long-short-term memories.
• Score: 39.081920534877796
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually attracts the attention of researchers since no direct history facts for prediction. Previous works attempted to apply evolutionary representation learning to solve the extrapolation problem. However, these techniques do not explicitly leverage various time-aware attribute representations, i.e. the reasoning performance is significantly affected by the history length. To alleviate the time dependence when reasoning future missing facts, we propose a memory-triggered decision-making (MTDM) network, which incorporates transient memories, long-short-term memories, and deep memories. Specifically, the transient learning network considers transient memories as a static knowledge graph, and the time-aware recurrent evolution network learns representations through a sequence of recurrent evolution units from long-short-term memories. Each evolution unit consists of a structural encoder to aggregate edge information, a time encoder with a gating unit to update attribute representations of entities. MTDM utilizes the crafted residual multi-relational aggregator as the structural encoder to solve the multi-hop coverage problem. We also introduce the dissolution learning constraint for better understanding the event dissolution process. Extensive experiments demonstrate the MTDM alleviates the history dependence and achieves state-of-the-art prediction performance. Moreover, compared with the most advanced baseline, MTDM shows a faster convergence speed and training speed.

Related papers

• TRAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory [35.19442792860492]
  As reasoning trajectories grow, models may become less effective at using information established earlier in the context.<n>Existing approaches primarily address this problem by sustaining visual grounding throughout reasoning.<n>We introduce TRAM (TRajectory-derived Auxiliary Memory), a training-free method that augments standard decoding with an auxiliary memory pathway.
  arXiv  Detail & Related papers  (2026-08-03T08:57:00Z)
• Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation [73.84423888025171]
  Autoregressive (AR) video diffusion has recently emerged as a promising paradigm for long video generation.<n>We show that extending generation to minute-scale horizons remains challenging due to progressive temporal degradation.<n>We introduce Relax Forcing, a structured temporal memory mechanism for AR diffusion.
  arXiv  Detail & Related papers  (2026-03-22T18:59:24Z)
• AMA: Adaptive Memory via Multi-Agent Collaboration [54.490349689939166]
  We propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities.<n>AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
  arXiv  Detail & Related papers  (2026-01-28T08:09:49Z)
• MemVerse: Multimodal Memory for Lifelong Learning Agents [35.218549149012844]
  We introduce MemVerse, a model-agnostic, plug-and-play memory framework.<n>MemVerse bridges fast parametric recall with hierarchical retrieval-based memory.<n>It enables scalable and adaptive multimodal intelligence.
  arXiv  Detail & Related papers  (2025-12-03T10:06:14Z)
• MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning [22.89546852658161]
  Temporal Knowledge Graphs offer a reliable source for temporal reasoning.<n>Existing TKG-based LLM reasoning methods still struggle with four major challenges.<n>We propose MemoTime, a memory-augmented temporal knowledge graph framework.
  arXiv  Detail & Related papers  (2025-10-15T14:43:31Z)
• MemMamba: Rethinking Memory Patterns in State Space Model [6.537535831000493]
  We show that selective state-space models such as Mamba have high efficiency with O(n) time and O(1) recurrent inference, yet their long-range memory decays exponentially.<n>Inspired by how humans distill and retain salient information when reading long documents, we propose MemMamba.<n>MemMamba achieves significant improvements over existing Mamba variants and Transformers on long-sequence benchmarks.
  arXiv  Detail & Related papers  (2025-09-28T14:40:58Z)
• Remember and Recall: Associative-Memory-based Trajectory Prediction [25.349986959111757]
  We propose the Fragmented-Memory-based Trajectory Prediction (FMTP) model, inspired by the remarkable learning capabilities of humans. The FMTP model employs discrete representations to enhance computational efficiency by reducing information redundancy. We develop an advanced reasoning engine based on language models to deeply learn the associative rules among these discrete representations.
  arXiv  Detail & Related papers  (2024-10-03T04:32:21Z)
• Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps [56.827895559823126]
  Self-organizing map (SOM) is a neural model often used in clustering and dimensionality reduction. We propose a generalization of the SOM, the continual SOM, which is capable of online unsupervised learning under a low memory budget. Our results, on benchmarks including MNIST, Kuzushiji-MNIST, and Fashion-MNIST, show almost a two times increase in accuracy.
  arXiv  Detail & Related papers  (2024-02-19T19:11:22Z)
• PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks [22.47336262812308]
  Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and long-term temporal dependencies. This paper studies the efficient training of MDGNNs at scale, focusing on the temporal discontinuity in training MDGNNs with large temporal batch sizes.
  arXiv  Detail & Related papers  (2024-02-06T01:34:56Z)
• Long Short-term Memory with Two-Compartment Spiking Neuron [64.02161577259426]
  We propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.
  arXiv  Detail & Related papers  (2023-07-14T08:51:03Z)
• Recurrence-in-Recurrence Networks for Video Deblurring [58.49075799159015]
  State-of-the-art video deblurring methods often adopt recurrent neural networks to model the temporal dependency between the frames. In this paper, we propose recurrence-in-recurrence network architecture to cope with the limitations of short-ranged memory.
  arXiv  Detail & Related papers  (2022-03-12T11:58:13Z)
• Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay [67.50637511633212]
  A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day.
  arXiv  Detail & Related papers  (2021-12-09T07:11:14Z)
• Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning [8.030924531643532]
  Continual learning is considered a promising step towards next-generation Artificial Intelligence (AI) It is still quite primitive, with existing works focusing primarily on avoiding (catastrophic) forgetting. We propose a novel framework for continual learning with external memory that builds on recent advances in neuroscience.
  arXiv  Detail & Related papers  (2021-05-05T14:32:47Z)
• Temporal Memory Relation Network for Workflow Recognition from Surgical Video [53.20825496640025]
  We propose a novel end-to-end temporal memory relation network (TMNet) for relating long-range and multi-scale temporal patterns. We have extensively validated our approach on two benchmark surgical video datasets.
  arXiv  Detail & Related papers  (2021-03-30T13:20:26Z)
• Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping [10.970706194360451]
  Catastrophic forgetting in neural networks is a significant problem for continual learning. We introduce Relevance Mapping Networks (RMNs) which are inspired by the Optimal Overlap Hypothesis. We show that RMNs learn an optimized representational overlap that overcomes the twin problem of catastrophic forgetting and remembering.
  arXiv  Detail & Related papers  (2021-02-22T20:34:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.