# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 1 Introduction  
Knowledge graphs (KGs) represent real-world information as structured facts in the form of triples, i.e., (subject entity, relation, object entity), abbreviated as \((s,r,o)\), where \(s\) and \(o\) represent entities, and \(r\) represents a relation that holds between the two entities. For example, given a triple ([PERSON], PresidentOf, USA), it indicates that there is a relation \"PresidentOf\" between the entities \"[PERSON]\" and \"USA\". The rich information carried by KGs makes them popular in various artificial intelligence and data mining applications, such as question answering [1], recommender [2], and dialogue systems [3], etc. Although current KGs consist of billions of facts, they are still far from complete. For instance, in Freebase [4], more than 75% of person entities have no known nationality [5], which hampers their usefulness in the aforementioned applications.  
Knowledge graph completion (KGC) tries to solve this incompleteness problem by inferring new triples based on the existing ones. It operates under the assumption that KGs are static, i.e., the ground truth of triples is independent of time. However, this assumption is not always realistic, e.g., as shown in Figure 1, the triple ([PERSON], PresidentOf, USA) is only valid during the period [January 2009, January 2017]. Indeed, many real-world KGs such as Wikidata [6], YAGO3 [7], and ICEWS include temporal information for their facts. Specifically, temporal facts are facts of the form of quadruple \((s,r,o,t)\), where \(s\) and \(o\) are entities, \(r\) is a relation, and \(t\) is a timestamp. It indicates that, at a time specified by \(t\), there exists a relation \(r\) between entities \(s\) and \(o\). Consequently, temporal knowledge graph completion (TKGC) has become an important research topic, which aims to automatically predict missing facts by making inferences from the existing temporal facts.  
As TKGC has attracted increased attention from the research community, various TKGC methods have been proposed, among which, deep learning (DL)-based methods have achieved state-of-the-art performance. Therefore, in order to promote further progress in this field, it is necessary to comprehensively summarize and compare the existing DL-based TKGC methods. We note that: (1) Ref. [8] provides a review of temporal knowledgegraphs, which includes a section on TKGC. However, this section only covers five early methods related to TKGC. (2) Ref. [9] provides a review of temporal knowledge graphs, focusing on representation learning-based TKGC methods. (3) There are three review papers [10; 11; 12] on TKGC, but they either mainly focus on the interpolation-based methods or, perhaps due to space limitations, introduce TKGC methods from a macro perspective. Most importantly, the above reviews do not include research work from the last one to two years.  
This paper reviews TKGC methods from the perspective of deep learning for the first time, and the proportions of the latest research after 2023 and the literature not included in the previous reviews reach 50%, respectively.  
Our main contributions are summarized as follows:  
* For the first time, we propose a survey that analyzes the existing DL-based TKGC methods to date and further subdivide them into eight categories according to their core techniques.
* We summarize eight common benchmark datasets and the general evaluation protocol.
* We point out three future directions for TKGC, providing new ideas for the work of relevant researchers.  
The remainder of this paper is organized as follows. Section 2 introduces the background of TKGC, including task definition, benchmark datasets, and evaluation protocol. Section 3 summarizes the DL-based TKGC methods and Section 4 summarizes the experimental results of these methods. Section 5 concludes this paper and highlights the future directions of TKGC.