# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 2 Background
### Benchmark Datasets  
There are eight commonly used datasets for TKGC, and the main statistics for these datasets are summarized in Table 1.  
ICEWS14, ICEWS05-15, and ICEWS18. These three datasets are subsets of Integrated Crisis Early Warning Systems (ICEWS). ICEWS processes vast amounts of data from digitized news, social media, and other sources to predict, track, and respond to global events with time points, primarily for early warning purposes. These three datasets contain events in 2014, 2005-2015, and 2018, respectively.  
GDELT. This dataset is a subset of the larger Global Database of Events, Language, and Tone (GDELT) [16]. GDELT is a comprehensive global database that includes broadcast, print, and web news from every country starting in 1979. The GDELT subset contains facts with daily timestamps in the form of time points between 1 April 2015 and 31 March 2016. It should be emphasized that GDELT exhibits a complex geometry characterized by a small number of nodes and a substantial volume of training data.  
YAGO11k, YAGO15k, WIKI, and Wikidata12k. These datasets are subsets of YAGO3 [7] and Wikidata [6]. YAGO3 and Wikidata are two large-scale TKGs where a portion of the facts involves temporal information. Specifically, YAGO3 is a linked database developed by the Max Planck Institute in Germany, which integrates data from Wikipedia, WordNet, GeoNames, and other data sources [7]. Wikidata is a Wikimedia hosted and maintained  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **\#Entities** & **\#Relations** & **\#Timestamps** & **\#Train** & **\#Validation** & **\#Test** & **Timestamps** \\ \hline ICEWS14 & 6869 & 230 & 365 & 72,826 & 8941 & 8963 & time point \\ ICEWS05-15 & 10,094 & 251 & 4017 & 368,962 & 46,275 & 46,092 & time point \\ ICEWS18 & 23,033 & 256 & 304 & 373,018 & 45,995 & 49,545 & time point \\ GDELT & 500 & 20 & 366 & 2,735,685 & 341,961 & 341,961 & time point \\ YAGO11k & 10,623 & 10 & 70 & 16,406 & 2050 & 2051 & time interval \\ YAGO15k & 15,403 & 34 & 198 & 29,381 & 3635 & 3685 & time interval \\ Wikidata12k & 12,554 & 24 & 81 & 32,497 & 4062 & 4062 & time interval \\ WIKI & 12,554 & 24 & 232 & 539,286 & 67,538 & 63,110 & time interval \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics for TKGC benchmark datasets. #Entities, #Relations, #Timestamps, #Train, #Validation, and #Test denote the number of entities, relations, timestamps, training set, validation set, and test set, respectively.  
project launched in 2012 that aims to create a free knowledge graph about the world that can be read and edited by both humans and machines. Notably, different from ICEWS dataset that only involves time points, temporal information in YAGO3 and Wikidata is expressed in different forms, including time points such as 1 January 2000, time intervals containing begin or end time with shapes such as 'occursSince 2003' or 'occursUntil 2005', and time intervals containing both begin and end time, such as [2000, 2010]. In YAGO11k and Wikidata12k, all facts have temporal information in the form of time intervals with begin and end times. Among these two subsets, the frequency distribution of timestamps in YAGO11k exhibits the long-tail property, which makes it difficult to predict. In YAGO15k, temporal information is represented in the form of time intervals containing begin or end time, and some facts do not even include timestamps. Therefore, this subset is temporally incomplete and more challenging.