Analysis of Network Information Retrieval Method Based on Metadata Ontology
In order to solve the problem that people can accurately search for the network information they need, the research on network information retrieval methods becomes more important. This article is mainly about the research of network information retrieval methods based on metadata ontology calculati...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2022/2664639 |
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| author | Chunping Wang Keming Chen |
| author_facet | Chunping Wang Keming Chen |
| author_sort | Chunping Wang |
| collection | DOAJ |
| description | In order to solve the problem that people can accurately search for the network information they need, the research on network information retrieval methods becomes more important. This article is mainly about the research of network information retrieval methods based on metadata ontology calculations. This article constructs an LDA three-layer Bayesian model with a three-layer structure of document, topic, and single order. The three-layer structure obeys a random polynomial distribution and can be calculated the joint distribution probability of all variables in the LDA model greatly increases the calculation efficiency. Using a cross-modal information retrieval method, it can mine the common data features between different modal data and analyze the semantic correlation between different modal data, improve the accuracy of search, and solve the existence of different modal data. There is a gap in the expression semantics between heterogeneous and different modal data. The experimental results in this paper show that the text feature extraction of the network information retrieval method based on the metadata ontology calculation has a good performance in terms of accuracy, and the accuracy of the extraction and clustering results is as high as about 90%. The improved CCA algorithm used is better than the traditional CCA and the accuracy is improved by 23%, which is 12% higher than the LDA-CCA algorithm. |
| format | Article |
| id | doaj-art-be057091714c48e08db1eb82bcb1cda4 |
| institution | Kabale University |
| issn | 1687-5877 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-be057091714c48e08db1eb82bcb1cda42025-02-03T05:50:35ZengWileyInternational Journal of Antennas and Propagation1687-58772022-01-01202210.1155/2022/2664639Analysis of Network Information Retrieval Method Based on Metadata OntologyChunping Wang0Keming Chen1School of Communication and Information EngineeringDean’s OfficeIn order to solve the problem that people can accurately search for the network information they need, the research on network information retrieval methods becomes more important. This article is mainly about the research of network information retrieval methods based on metadata ontology calculations. This article constructs an LDA three-layer Bayesian model with a three-layer structure of document, topic, and single order. The three-layer structure obeys a random polynomial distribution and can be calculated the joint distribution probability of all variables in the LDA model greatly increases the calculation efficiency. Using a cross-modal information retrieval method, it can mine the common data features between different modal data and analyze the semantic correlation between different modal data, improve the accuracy of search, and solve the existence of different modal data. There is a gap in the expression semantics between heterogeneous and different modal data. The experimental results in this paper show that the text feature extraction of the network information retrieval method based on the metadata ontology calculation has a good performance in terms of accuracy, and the accuracy of the extraction and clustering results is as high as about 90%. The improved CCA algorithm used is better than the traditional CCA and the accuracy is improved by 23%, which is 12% higher than the LDA-CCA algorithm.http://dx.doi.org/10.1155/2022/2664639 |
| spellingShingle | Chunping Wang Keming Chen Analysis of Network Information Retrieval Method Based on Metadata Ontology International Journal of Antennas and Propagation |
| title | Analysis of Network Information Retrieval Method Based on Metadata Ontology |
| title_full | Analysis of Network Information Retrieval Method Based on Metadata Ontology |
| title_fullStr | Analysis of Network Information Retrieval Method Based on Metadata Ontology |
| title_full_unstemmed | Analysis of Network Information Retrieval Method Based on Metadata Ontology |
| title_short | Analysis of Network Information Retrieval Method Based on Metadata Ontology |
| title_sort | analysis of network information retrieval method based on metadata ontology |
| url | http://dx.doi.org/10.1155/2022/2664639 |
| work_keys_str_mv | AT chunpingwang analysisofnetworkinformationretrievalmethodbasedonmetadataontology AT kemingchen analysisofnetworkinformationretrievalmethodbasedonmetadataontology |