Temporal Knowledge Graph Completion with Quaternion Embeddings in Hypercomplex Space

Conceptos Básicos

The author introduces a novel approach using quaternion embeddings in hypercomplex space for temporal knowledge graph completion, focusing on capturing dynamic relations over time. The proposed method outperforms existing models by effectively modeling complex temporal variability.

Resumen

Temporal Knowledge Graph Completion (TKGC) aims to fill missing facts within a given temporal knowledge graph at specific times. Existing methods operate in real or complex spaces but the proposed approach introduces quaternion representations in hypercomplex space to capture time-sensitive relations and achieve state-of-the-art performance. The model effectively captures symmetric, asymmetric, inverse, compositional, and evolutionary relation patterns through theoretical evidence and comprehensive experiments on public datasets validate its performance.

Estadísticas

TQuatE achieves SOTA performance on ICEWS14, ICEWS05-15, and GDELT datasets.
Compared to TLT-KGE(Q), TQuatE shows improvements of 0.79%, 0.58%, and 8.38% in MRR.
TQuatE outperforms TeAST modeled in complex space.
Ablation study confirms the effectiveness of modeling periodic time in TQuatE.

Citas

"Our proposed model TQuatE represents the KGs in hypercomplex space, offering more degrees of freedom."
"TQuatE significantly outperforms the existing SOTA model on GDELT."
"The impact of temporal regularization during training is greater than that of embedding regularization."

Ideas clave extraídas de

Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space

by Li Cai,Xin M... a las arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02355.pdf

Consultas más profundas

How does the complexity of quaternion multiplication affect the efficiency of TQuatE compared to TeAST

The complexity of quaternion multiplication in TQuatE affects its efficiency compared to TeAST due to the increased computational requirements. Quaternion multiplication involves more intricate calculations than complex multiplication, as quaternions have four components (one real part and three imaginary parts) compared to two components in complex numbers. This complexity leads to longer computation times for TQuatE compared to TeAST, which operates in a simpler complex space. As a result, TQuatE may require more time during training and inference phases, impacting its overall efficiency.

What are the implications of the ablation study results showing the importance of modeling periodic time

The ablation study results highlighting the importance of modeling periodic time in TQuatE have significant implications for temporal knowledge graph completion tasks. The findings suggest that incorporating periodic time representations enhances the model's performance by capturing both temporal and periodic features of relations effectively. By including periodicity in modeling relations over time, TQuatE can better capture complex temporal variability within knowledge graphs. This emphasizes the critical role of considering not just linear temporal changes but also cyclic patterns when addressing dynamic data evolution.

How can the findings from this research be applied to other domains beyond knowledge graph completion

The findings from this research on Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space can be applied beyond knowledge graph completion tasks:

Time Series Analysis: The concept of modeling periodic time variations can be applied to time series analysis tasks where cyclic patterns exist.
Financial Forecasting: In financial forecasting models, understanding both linear trends and cyclical fluctuations is crucial for accurate predictions.
Healthcare Data Analysis: Applying similar techniques could help analyze patient health data over different periods while considering recurring patterns.
Supply Chain Management: Understanding seasonal demand variations or cyclic supply chain disruptions can benefit from models that incorporate periodic features.
Natural Language Processing: Incorporating periodic elements into language models could enhance their ability to capture evolving linguistic trends over specific intervals.

By leveraging the insights gained from this research across various domains, practitioners can develop more robust models capable of capturing both linear and cyclical dynamics present in diverse datasets beyond knowledge graphs.