A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

TL;DR

This survey comprehensively reviews methods for completing temporal knowledge graphs, discussing background, interpolation, extrapolation techniques, challenges, and future directions in the field.

Contribution

It provides a detailed taxonomy and analysis of existing TKGC methods, highlighting recent progress and identifying key challenges and future research prospects.

Findings

Categorized TKGC methods based on processing of temporal information.

Reviewed datasets, evaluation protocols, and loss functions used in TKGC.

Identified challenges and proposed future research directions in TKGC.

Abstract

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC.