A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies
In large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/14/3154 |
| _version_ | 1797588450064465920 |
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| author | Runsheng Miao Yuchen Huang Zhenyu Zhang |
| author_facet | Runsheng Miao Yuchen Huang Zhenyu Zhang |
| author_sort | Runsheng Miao |
| collection | DOAJ |
| description | In large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable of returning hierarchical, systematized knowledge results. The method can match the knowledge demands according to the keyword input by users and then present the knowledge supplies corresponding to the knowledge demands as results to the users. Firstly, a knowledge structure named Knowledge Demand is designed to represent the genuine needs of social media users. This knowledge structure measures the popularity of topic combinations in the Topic Map, so the topic combinations with high popularity are regarded as the main content of the Knowledge Demands. Secondly, the proposed method designs a hierarchical and systematic knowledge structure, named Knowledge Supply, which provides Knowledge Solutions matched with the Knowledge Demands. The Knowledge Supply is generated based on the Knowledge Element Repository, using the BLEU similarity matrix to retrieve Knowledge Elements with high similarity, and then clustering these Knowledge Elements into several knowledge schemes to extract the Knowledge Solutions. The organized Knowledge Elements and Knowledge Solutions are the presentation of each Knowledge Supply. Finally, this research crawls posts in the “Autohome Forum” and conducts an experiment by simulating the user’s actual knowledge search process. The experiment shows that the proposed method is an effective knowledge retrieval method, which can provide users with hierarchical and systematized knowledge. |
| first_indexed | 2024-03-11T00:52:10Z |
| format | Article |
| id | doaj.art-d4f78013ab244fd1a8ad6415a7f00e18 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:52:10Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d4f78013ab244fd1a8ad6415a7f00e182023-11-18T20:21:22ZengMDPI AGMathematics2227-73902023-07-011114315410.3390/math11143154A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge SuppliesRunsheng Miao0Yuchen Huang1Zhenyu Zhang2College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, ChinaCollege of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaIn large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable of returning hierarchical, systematized knowledge results. The method can match the knowledge demands according to the keyword input by users and then present the knowledge supplies corresponding to the knowledge demands as results to the users. Firstly, a knowledge structure named Knowledge Demand is designed to represent the genuine needs of social media users. This knowledge structure measures the popularity of topic combinations in the Topic Map, so the topic combinations with high popularity are regarded as the main content of the Knowledge Demands. Secondly, the proposed method designs a hierarchical and systematic knowledge structure, named Knowledge Supply, which provides Knowledge Solutions matched with the Knowledge Demands. The Knowledge Supply is generated based on the Knowledge Element Repository, using the BLEU similarity matrix to retrieve Knowledge Elements with high similarity, and then clustering these Knowledge Elements into several knowledge schemes to extract the Knowledge Solutions. The organized Knowledge Elements and Knowledge Solutions are the presentation of each Knowledge Supply. Finally, this research crawls posts in the “Autohome Forum” and conducts an experiment by simulating the user’s actual knowledge search process. The experiment shows that the proposed method is an effective knowledge retrieval method, which can provide users with hierarchical and systematized knowledge.https://www.mdpi.com/2227-7390/11/14/3154knowledge retrievalTopic MapKnowledge Elementssocial media |
| spellingShingle | Runsheng Miao Yuchen Huang Zhenyu Zhang A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies Mathematics knowledge retrieval Topic Map Knowledge Elements social media |
| title | A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies |
| title_full | A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies |
| title_fullStr | A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies |
| title_full_unstemmed | A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies |
| title_short | A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies |
| title_sort | social media knowledge retrieval method based on knowledge demands and knowledge supplies |
| topic | knowledge retrieval Topic Map Knowledge Elements social media |
| url | https://www.mdpi.com/2227-7390/11/14/3154 |
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