# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 4 Results and Discussion
### Journal Co-Citation Network Analysis
The betweenness centrality scores of the journals including \"Communications of the ACM\", \"IEEE Transactions on Knowledge and Data Engineering\", and \"IEEE Transactions on Pattern Analysis and Machine Intelligence\" are all around 0.20, and they play important intermediary roles connecting different journals that have a co-citation relationship in the event knowledge graph field before 2015. In the most recent years, the journal \"IEEE Transactions on Systems, Man, and Cybernetics: Systems\", with a betweenness centrality of 0.14, stands out as the core journal related to event knowledge graphs, promoting interdisciplinary collaboration and knowledge exchange. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Abbreviation** & **Full Name** & **Impact Factor** & \
\begin{tabular}{c} **Betweenness** \\ **Centrality** \\ \end{tabular} & **Average Year** \\ \hline COMMUN ACM &
\begin{tabular}{c} Communications of the \\ ACM \\ IEEE Transactions on \\ Knowledge and Data \\ Engineering \\ IEEE Transactions on Pattern \\ Analysis and Machine \\ Intelligence \\ IEEE Transactions on \\ Systems, Man, and \\ Cybernetics: Systems \\ Journal of Machine Learning \\ Research \\ \end{tabular} & 22.7 & 0.23 & 2012 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The top five journals based on betweenness centrality for the years 2012 to 2022. Figure 7: The visualization of journal co-citation network for the years 2012–2022. As the number of citations of a journal increases rapidly in a certain period of time, the journal is identified as owning a strong citation burst. As shown in Table 8, the journal titled \"PLOS ONE\" has the strongest citation burst, with a score of 5.38, which lasted two years from 2018 to 2020. There were also some strong citation bursts in the most well-known and highly impacted journals, including \"Science\" and \"Nature\" during 2015 and 2019, proving that the top-level research regarding event knowledge graphs had been widely acknowledged and increasingly cited by the academic community during those periods. The journal \"Artificial Intelligence\" constitutes the longest citation burst lasting four years (from 2013 to 2017), during which the event-knowledge-graph-related publications in \"Artificial Intelligence\" were cited more frequently than expected by peers. In order to identify the distinguished groups of journals with high homogeneity, we further conducted a cluster analysis of the journal co-citation network. As shown in Figure 8, the network was grouped into six clusters, and they are rendered in different colors. The modularity indicated by the Q value equals 0.4731 and the weighted mean Silhouette S value equals 0.8173, both of which fall in the desirable ranges. As such, the cluster analysis results prove to be reliable and meet our expectations, supporting scholars to better understand the academic structure of the event-knowledge-graph-related research. Figure 8: The clusters of the journal co-citation network for the years 2012 to 2022. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Abbreviation** & **Full Name** & \
\begin{tabular}{c} **Impact** \\ **Factor** \\ \end{tabular} & **Burst** & **Begin (Year)** & **End (Year)** \\ \hline PLOS ONE & Plos One & 3.7 & 5.38 & 2018 & 2020 \\ SCIENCE & Science & 56.9 & 5.24 & 2018 & 2019 \\ ARTIF & Artificial & 14.4 & 5.22 & 2013 & 2017 \\ INTELL & Intelligence & & & & \\ PROC VLDB &
\begin{tabular}{c} Proceedings \\ of the VLDB \\ Endowment \\ \end{tabular} & 2.5 & 5.16 & 2016 & 2019 \\ NATURE & Nature & 64.8 & 4.34 & 2015 & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The top five journals sorted by citation burst for the years 2012 to 2022. The details concerning the six clusters are presented in Table 9, including the number of journals included, S value, mean year, and the cluster labels generated by the LLR method. Cluster #0 with the largest size took place in 2020 and was labelled as \"attention mechanism\", \"semantics\", \"feature extraction\", \"knowledge engineering\", and \"event extraction\".