# Diachronic Embedding for Temporal Knowledge Graph Completion

## TL;DR

This paper introduces a novel, model-agnostic diachronic embedding approach for temporal knowledge graph completion, enhancing static models with temporal entity characteristics to improve inference accuracy.

## Contribution

It proposes a new diachronic embedding function that can be integrated with any static KG model, demonstrated with SimplE, to effectively handle temporal information.

## Key findings

- The proposed model outperforms existing baselines in experiments.
- The embedding function is fully expressive when combined with SimplE.
- The approach is model-agnostic and adaptable to various static KG models.

## Abstract

Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.