Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score
This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are gener...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/13/4590 |
| _version_ | 1827713754120323072 |
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| author | Hyun-Jin Kim Ji-Won Baek Kyungyong Chung |
| author_facet | Hyun-Jin Kim Ji-Won Baek Kyungyong Chung |
| author_sort | Hyun-Jin Kim |
| collection | DOAJ |
| description | This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms. |
| first_indexed | 2024-03-10T18:43:24Z |
| format | Article |
| id | doaj.art-40a144fa6ced4154bc9b4258a661b401 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:43:24Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-40a144fa6ced4154bc9b4258a661b4012023-11-20T05:38:15ZengMDPI AGApplied Sciences2076-34172020-07-011013459010.3390/app10134590Optimization of Associative Knowledge Graph using TF-IDF based Ranking ScoreHyun-Jin Kim0Ji-Won Baek1Kyungyong Chung2Division of Computer Science and Engineering, Kyonggi University, Suwon 16227, Gyeonggi, KoreaDepartment of Computer Science, Kyonggi University, Suwon 16227, Gyeonggi, KoreaDivision of Computer Science and Engineering, Kyonggi University, Suwon 16227, Gyeonggi, KoreaThis study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.https://www.mdpi.com/2076-3417/10/13/4590TF-IDFassociation ruleaprioriFP-treeassociative knowledge graph |
| spellingShingle | Hyun-Jin Kim Ji-Won Baek Kyungyong Chung Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score Applied Sciences TF-IDF association rule apriori FP-tree associative knowledge graph |
| title | Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score |
| title_full | Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score |
| title_fullStr | Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score |
| title_full_unstemmed | Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score |
| title_short | Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score |
| title_sort | optimization of associative knowledge graph using tf idf based ranking score |
| topic | TF-IDF association rule apriori FP-tree associative knowledge graph |
| url | https://www.mdpi.com/2076-3417/10/13/4590 |
| work_keys_str_mv | AT hyunjinkim optimizationofassociativeknowledgegraphusingtfidfbasedrankingscore AT jiwonbaek optimizationofassociativeknowledgegraphusingtfidfbasedrankingscore AT kyungyongchung optimizationofassociativeknowledgegraphusingtfidfbasedrankingscore |