{"title":"基于神经网络的深度网络实体识别","authors":"Baohua Qiang, Chunming Wu, Long Zhang","doi":"10.1109/CYBERC.2010.93","DOIUrl":null,"url":null,"abstract":"With the rapid developments and extensive applications of internet, a large number of duplicated entities on the Web, especially on the Deep Web, require to be eliminated and integrated effectively. So identifying the corresponding entities on the Deep Web is critical. Due to the query interface on the HTML page represents the schema of the Web database, we firstly try to obtain the schema of the entities on the Deep Web by extracting the schema of the query interface in order to improve the accuracy for entities matching. Then an entities identification approach on the Deep Web using neural network is proposed. The experimental results show the effectiveness of our proposed algorithm.","PeriodicalId":315132,"journal":{"name":"2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Entities Identification on the Deep Web Using Neural Network\",\"authors\":\"Baohua Qiang, Chunming Wu, Long Zhang\",\"doi\":\"10.1109/CYBERC.2010.93\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid developments and extensive applications of internet, a large number of duplicated entities on the Web, especially on the Deep Web, require to be eliminated and integrated effectively. So identifying the corresponding entities on the Deep Web is critical. Due to the query interface on the HTML page represents the schema of the Web database, we firstly try to obtain the schema of the entities on the Deep Web by extracting the schema of the query interface in order to improve the accuracy for entities matching. Then an entities identification approach on the Deep Web using neural network is proposed. The experimental results show the effectiveness of our proposed algorithm.\",\"PeriodicalId\":315132,\"journal\":{\"name\":\"2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBERC.2010.93\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBERC.2010.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Entities Identification on the Deep Web Using Neural Network
With the rapid developments and extensive applications of internet, a large number of duplicated entities on the Web, especially on the Deep Web, require to be eliminated and integrated effectively. So identifying the corresponding entities on the Deep Web is critical. Due to the query interface on the HTML page represents the schema of the Web database, we firstly try to obtain the schema of the entities on the Deep Web by extracting the schema of the query interface in order to improve the accuracy for entities matching. Then an entities identification approach on the Deep Web using neural network is proposed. The experimental results show the effectiveness of our proposed algorithm.