## 3 Results
### Result Analysis  
The experimental results are illustrated in Tables 2 and 3. ERGCN outperforms the benchmarks on WIKI and ICEWS. Especially, the performances on WIKI rise significantly. The experimental results show that it is helpful to make full use of semantic information in entity prediction tasks. Obviously, ERGCN works better than static models because ERGCN captures the evolutionary pattern of facts. Thus, it can achieve higher performance when testing on unseen temporal knowledge graphs. Compared with recent dynamic models, such as REGCN and RENET, our ERGCN overtakes the others in most tasks. Although ERGCN does not have the best performance on YAGO and GDELT, its performance is very close to the best results. Therefore, ERGCN's overall performance is better. The results verify the importance of differential treatment for various relation types, which contains much useful semantic information about the temporal dependencies of facts. As mentioned above, we only use a one-layer ERGCN block in the tasks. The reason is that the performance on the high-accuracy metrics, such as Hit@1 and Hit@3, drops significantly when the layers are more than one. This phenomenon may indicate that ERGCN focuses on 1-hop neighborhoods, while long-distance relationships may interfere with the entity reasoning process. However, ERGCN still outperforms the other dynamic models and these results suggest that information in the 1-hop neighborhood is underutilized in previous approaches, and that ERGCN can extract these information more effectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **N\_Train** & **N\_Valid** & **N\_Test** & **Entities** & **Relations** & **Time Gap** \\ \hline YAGO & 161,540 & 19,523 & 20,026 & 10,623 & 10 & 1 year \\ WIKI & 539,286 & 67,538 & 63,110 & 12,554 & 24 & 1 year \\ GDELT & 1,734,399 & 238,765 & 305,241 & 7,691 & 240 & 15 min \\ ICEWS14 & 74,845 & 8,514 & 7,371 & 6,869 & 230 & 24 h \\ ICEWS15 & 368,868 & 46,302 & 46,159 & 10,094 & 251 & 24 h \\ ICEWS18 & 373,018 & 45,995 & 45,995 & 23,033 & 256 & 24 h \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset statistics. ERGCN is similar to RENET, but we pay more attention to applying full semantic information of facts. By capturing more precise temporal representation of sequential knowledge, ERGCN overtakes RENET in the majority of the datasets and our results are close to those of RENET on GDELT. Different from REGCN, which includes a new recurrent block to learn sequential histories of entities, the structure of ERGCN is simple, but the performance is good. Compared with the previous best results on WIKI, ERGCN has improved 11.44% in MRR metric, 11.35% in Hit@3 metric and 8.40% in Hit@10 metric, respectively. In this dataset, temporal facts are widely collected from the open-source dataset, Wikipedia. The informative interactions between different entities are discrete, temporal dependencies around facts are often limited in small areas, and then 1-hop neighborhoods contain the most important structural information of dependencies. Different from concentrating attention on neighbors' entities, ERGCN concerns the interaction between entities and their linked relation types, which provides more relevant structural dependencies between facts. The results on ICEWS show that, compared with other methods, using more semantic information of facts provides more accurate temporal characteristics for the reasoning process. In terms of the MRR metric, the performance of ERGCN is 0.17/5.38/0.78% higher than the previous best on ICEWS14/ICEWS15/ICEWS18, respectively. Moreover, ERGCN improves Hit@1,3,10 in each dataset as well. The ICEWS series dataset is event-based and facts here often change frequently. Therefore, learning the relation type becomes a key point, which is able to provide basic information indicating the trend of the facts. Different from previous studies, ERGCN focuses on learning the interaction between entities and relation types, which reserves various temporal dependencies.