Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)
Copied to clipboard
| Challenge: | Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time. |
| Approach: | They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets. |
Similar Papers
TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models suffer from temporal redundancy when leveraged under dynamic settings. |
| Approach: | They propose a temporal knowledge graph extrapolation method which solves temporal redundancy issues by using cyclic rules to capture more information lurking in TKGs. |
| Outcome: | The proposed model captures more information lurking in TKGs, and also mines and properly leverages acyclic rules, which has not been explored by existing models. |
Critic Rule Induction: Improving Temporal Knowledge Graph Forecasting with Generator-Critic Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for predicting future facts from time-evolving graphs rely on statistical co-occurrences and extensive path enumeration. |
| Approach: | They propose a Critic-Guided Rule Induction method which treats temporal rules as rule hypotheses to be examined and adopts a decoupled Generation-Discrimination pipeline to induce rules that are high-coverage and high-precision. |
| Outcome: | The proposed method outperforms strong baselines on three benchmarks and achieves state-of-the-art performance. |
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for static knowledge graphs do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. |
| Approach: | They propose a framework to leverage time-dependent temporal information to infer missing facts in temporal knowledge graphs. |
| Outcome: | The proposed framework achieves 10.7% improvement in Hits@10 across three standard benchmarks. |
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
| Approach: | They propose a retrieval-augmented generation framework that uses temporal logical rule-based retrieval and parameter-efficient instruction tuning to solve temporal knowledge forecasting challenges. |
| Outcome: | The proposed framework outperforms conventional methods in the temporal knowledge graph domain with low computation resources. |
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Temporal knowledge graphs (TKGs) are used to represent real-world facts in a structured way. |
| Approach: | They propose to use in-context learning with large language models for TKG forecasting . they compare naive LLMs to state-of-the-art (SOTA) supervised models . |
| Outcome: | The proposed approach performs well against pre-trained large language models . the proposed approach is based on simple heuristics and state-of-the-art models compared with pre-trainers . |
Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for lifelong TKG reasoning only address part of the challenges. |
| Approach: | They propose a temporal-path-based reinforcement learning framework for lifelong TKG reasoning . they add temporal displacement into the action space of RL to extrapolate for the future . |
| Outcome: | The proposed model outperforms existing models against well-adapted baselines on three lifelong TKG reasoning benchmarks. |
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts. |
| Approach: | They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data. |
| Outcome: | The proposed model outperforms SOTA methods on the link prediction task. |
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. |
| Approach: | They propose a time-shaped reward method that captures historical knowledge graph snapshots and a new representation method for unseen entities to improve the inductive inference ability of the model. |
| Outcome: | The proposed method improves on four benchmark datasets with higher explainability, less calculation, and fewer parameters when compared with existing state-of-the-art methods. |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts. |
| Approach: | They propose a Temporal Meta-learning framework to learn evolutionary meta-knowledge from TKGs. |
| Outcome: | The proposed method improves on four widely-used datasets and three backbones on a wide range of scenarios on tKGs. |