# Optimization of Associative Knowledge Graph Using TF-IDF Based Ranking Score
## 4 Experimental Results  
The hardware and operating system for implementing the optimized associative knowledge graph proposed in this study are MacOS Catalina 10.15.3 (O/S), i7-7820 HQ 2.9 GHz(3.9 GHz) CPU, and 16 GB LPDDR3 RAM. Performance is evaluated in two ways. In the optimized transactions, the generation speed of association rules is evaluated. The rule generation speed and objective usefulness of the generated association rules are evaluated according to association rule algorithms. In the first performance evaluation, the proposed method is compared with a conventional method in terms of the generation speed of association rules in the transactions related to traffic accident topics. From news stream data, 1700 traffic accident data are collected through crawling and are converted into a corpus. The collected corpus is cleaned for the improved quality of the analysis. In the data cleaning process, missing values and outliers are processed, and unnecessary data are removed, so that data dimensionality is reduced. In the morphological analysis, traffic accident topics are extracted, and transactions are designed with the use of TF-IDF weights. For the comparison of the performance of the association rule algorithm, the data as the results of FP-Tree and Apriori algorithms are generated. In the first evaluation, the generation speed of association rules is compared according to the independent changes in the minimum support and confidence. In order to evaluate  
Figure 10: Non-Optimized Associative Knowledge Graph. the performance of the associated-words graph generation using TF-IDF weight-based ranking and the performance of the associated-words graph generation with no use of TF-IDF, this study compared the generation speed of association rules [41]. In other words, the generation speed and count are compared according to the top 5%, 10%, 15%, and 20% of TF-IDF. In consideration of the characteristics of the stream data created in real-time, the support and confidence that fit knowledge generation is judged. When it comes to the comparison of generation speed according to a change in support, the value of confidence that best expresses generation speed is 0.1. Accordingly, in the condition that the confidence value is set to 0.1, the rule generation speed is compared according to a change in the minimum support. Figure 11 presents word count and generation speed according to TF-IDF and min support. The results from the comparison of the association rule generation speed according to the changes in TF-IDF ranking ratio and the minimum support. As shown in Figure 11, in the condition that the confidence value is set to 0.1 and the support value is 0.005, the model with the use of TF-IDF 15%, 20% are compared to the model with no use of TF-IDF, spends about 16.7 seconds and 9 seconds more in generating association rules. In the condition that the support value is over 0.01, the difference was 22 seconds or more and thus the association rule generation speed value is greatly reduced. Additionally, in the condition when support value is over 0.01, the model in which words in top TF-IDF ranking are applied shortens the time of generating association rules. In the condition that the support value is fixed to 0.01 and a confidence value varies, the association rule generation speed is compared. Figure 12 shows word count and generation speed according to TF-IDF and confidence. The results from the comparison of the association rule generation speed according to the changes in TF-IDF and confidence. As shown in Figure 12, the difference in the association rule generation speed depending on whether there is TF-IDF ranking applied is 12 seconds (about 44 times or more) in all measured confidence values. In particular, in the model with the use of TF-IDF, its rule generation speed in all confidence values does not exceed one second. That is because the number of unnecessary words (in transactions) is reduced.