# Optimization of Associative Knowledge Graph Using TF-IDF Based Ranking Score
## 5 Discussion and Conclusions  
In the lift-based evaluation, the proposed algorithm shows two times better performance than other association rule algorithms. In particular, with the increase in the number of rules, the algorithm with the use of TF-IDF Ranking had far better performance. Therefore, no matter how much the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Support** & **500 Rules** & **1000 Rules** & **1500 Rules** & **2000 Rules** & **2500 Rules** \\ \hline TF-IDF top 0 FP-Tree & 0.09979 & 0.08379 & 0.07304 & 0.06389 & 0.06087 \\ TF-IDF top 20 FP-Tree & 0.05093 & 0.04188 & 0.03635 & 0.03278 & 0.03036 \\ TF-IDF top 0 Apriori & 0.1404 & 0.11343 & 0.09818 & 0.09097 & 0.08495 \\ TF-IDF top 20 Apriori & 0.06794 & 0.05186 & 0.04522 & 0.04133 & 0.03903 \\ \hline
**Confidence** & **500 Rules** & **1000 Rules** & **1500 Rules** & **2000 Rules** & **2500 Rules** \\ \hline TF-IDF top 0 FP-Tree & 0.17034 & 0.17132 & 0.17251 & 0.17189 & 0.17189 \\ TF-IDF top 20 FP-Tree & 0.16144 & 0.16273 & 0.16123 & 0.16078 & 0.16114 \\ TF-IDF top 0 Apriori & 0.4745 & 0.5077 & 0.4941 & 0.4877 & 0.4902 \\ TF-IDF top 20 Apriori & 0.3793 & 0.4138 & 0.4398 & 0.4559 & 0.4653 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average Support, Confidence Value of Association Algorithms. Figure 14: Average Lift Score of Association Algorithms by Number of Rules. number of association rules increases in the stream news corpus collected in real-time, the proposed method improves the rule generation speed and usefulness. In addition, through the optimization of the knowledge graph, it is possible to extract significant information in real-time. This study proposed the method of optimizing the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates the TF-IDF weights for words in the news corpus related to traffic accident topics to make the ranking scores of all words. Word ranking is applied to remove the words which are not in the top 20% scores of all words extracted from the news corpus. Word data of the news corpus are optimized and are converted into transactions. A TID is set by news and item sets are generated. With the generated transactions, association rules of words are generated. According to the association rules, the edge based on confidence weight and the vertex based on word importance are generated and visualized in the knowledge graph. In the evaluation of performance (a degree of optimization), the graph was compared with the associated-words knowledge graph with no use of TF-IDF ranking. An association rule algorithm with the use of TF-IDF was compared with an association rule algorithm with no use of TF-IDF in terms of rule generation speed. As a result, in the condition that the support value is 0.01 or more and in all values of confidence, the association rule algorithm with the use of TF-IDF generated association rules about 22 seconds (44 times or more) faster than the association rule algorithm with no use of TF-IDF. In addition, the average lift value of the proposed TF-IDF based association rule algorithm was two times (up to 2.51) higher than those of Apriori and FP-Tree algorithms, so that the proposed one generated more useful association rules. Therefore, when an association rule knowledge graph is generated with the use of TF-IDF, it is possible to quickly make association rules for massive data collected in real-time. Given the two-times increase in the lift value, the usefulness of association rules is better. The contributions of the methods proposed in this paper are as follows:  
1. The method proposed in this paper has a problem in that it does not count the number of word duplicates when it is composed of transactions. Therefore, in order to solve the problem of the existing association rule algorithm, the transaction was optimized by using the ranking based on the TF-IDF weight. 2. By removing unnecessary keywords and considering the characteristics of stream data generated in real-time, the speed of generation is improved. 3.