RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion
Youri Xu, E Haihong, Meina Song, Wenyu Song, Xiaodong Lv, Wang, Haotian, Yang Jinrui

TL;DR
RTFE is a recursive framework that enhances temporal knowledge graph completion by modeling state transitions as a Markov chain, effectively leveraging static embeddings for dynamic graph evolution.
Contribution
It introduces a recursive method to adapt static knowledge graph embeddings for temporal graphs, improving TKG completion performance.
Findings
Effective on five TKG datasets
Outperforms existing TKGE models
Models state transitions as gradient updates
Abstract
Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion. Different from previous work which ignores the continuity of states of TKG in time evolution, we treat the sequence of graphs as a Markov chain, which transitions from the previous state to the next state. RTFE takes the SKGE to initialize the embeddings of TKG. Then it recursively tracks the state transition of TKG by passing updated parameters/features between timestamps. Specifically, at each timestamp, we approximate the state transition as the gradient update process. Since RTFE learns each timestamp recursively, it can naturally…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Machine Learning in Healthcare
