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Found 5 papers, 3 papers with code
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
Exploring Link Prediction over Hyper-Relational Temporal Knowledge Graphs Enhanced with Time-Invariant Relational Knowledge
We develop two new benchmark hyper-relational TKG (HTKG) datasets, i. e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models both temporal facts and qualifiers.
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
