Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations

Ioannis Dikeoulias, Saadullah Amin, Günter Neumann


Abstract
Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information. In this context, tensor decomposition has successfully modeled interactions between entities and relations. Their effectiveness in static knowledge graph completion motivates us to introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER. Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features, which is model-agnostic and offers a more generalized representation of time. We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing. The experiments show that our proposed methods perform on par or better than the state-of-the-art semantic matching models on two benchmarks.
Anthology ID:
2022.repl4nlp-1.12
Volume:
Proceedings of the 7th Workshop on Representation Learning for NLP
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | RepL4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
111–120
Language:
URL:
https://aclanthology.org/2022.repl4nlp-1.12
DOI:
10.18653/v1/2022.repl4nlp-1.12
Bibkey:
Cite (ACL):
Ioannis Dikeoulias, Saadullah Amin, and Günter Neumann. 2022. Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 111–120, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations (Dikeoulias et al., RepL4NLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.repl4nlp-1.12.pdf
Code
 iodike/chronokge
Data
ICEWS