# Optimization of Associative Knowledge Graph Using TF-IDF Based Ranking Score
## 2 Related Work
### Word Classification Using Term Frequency-Inverse Document Frequency
In Figure 3, the term 'Traffic' has the highest TF-IDF weight score, 35 in terms of all documents. The term 'time' has the lowest weight score, which is about 10. Based on the TF-IDF Rank Table, transactions are redesigned in order to find efficient association rules. The words with TF-IDF Rank scores, which are not in the top 20% scores of the words extracted from traffic accident topic text, were deleted. [PERSON] et al. [27] proposed a method and algorithm for evaluating the semantic similarity of text. The proposed method was further analyzed through a vector-based knowledge search approach. This takes into account the mathematical background of the potential meaning analysis by using the weight and similarity calculation of TF-IDF to derive the meaning of words from the text. Accordingly, it is an algorithm that not only calculates similarity but also obtains a vector representation and potential meaning of words in a reduced multidimensional space. Therefore, it can be used to evaluate text at the syntax and semantic level, reduce the dimension of the document vector space, and acquire more meaning of the text. Optimization of Associative Knowledge Graph using the Term Frequency-Inverse Document Frequency based Ranking Score  
In order to find significant information in massive data generated in real-time, it is necessary to improve the speed and usefulness of the association rule algorithm. This study proposes the method of optimizing the associated-knowledge graph using TF-IDF based ranking scores. The knowledge graphs made with conventional association rules include information of words with low importance, so that information offering efficiency is low. To solve the problem, the proposed method removes words with low importance and creates a knowledge graph by using TF-IDF based ranking scores. It consists of data collection and preprocessing step, mining-based associated-words knowledge graph extraction step, and TF-IDF based association-knowledge graph optimization step. Figure 4 shows the optimization process of association graph using TF-IDF based ranking scores. Figure 3: Weights of the words calculated with the use of TF-IDF in News Stream Data. In the first preprocessing step, news about traffic accident topics and traffic safety topics are collected in real-time through crawling and then are converted into a corpus. Unnecessary data are removed from the news corpus, words are extracted in morphological analysis, and transactions are designed. In the second step, an associated-words knowledge graph is extracted with the use of mining. With transactions, a frequent item header table is generated on the basis of support and confidence. In the frequent item header table, association rules are discovered, knowledge is extracted, and a graph is generated. In the last step, the ranking of the words extracted in the first step is determined with the use of TF-IDF. Words with low ranking scores are judged to be less important and thereby are removed, and transactions are redesigned. Through association rules, significant knowledge is extracted, and a graph is generated. With the generated knowledge graph, it is possible to make the knowledge base of the traffic accident and safety and to predict an emerging risk.