W. Kang, A. Tung, Wei Chen, Xinyu Li, Qiyue Song, Chao Zhang, Feng Zhao, Xiajuan Zhou
{"title":"Trendspedia:一个分析和可视化网络发展的互联网观察站","authors":"W. Kang, A. Tung, Wei Chen, Xinyu Li, Qiyue Song, Chao Zhang, Feng Zhao, Xiajuan Zhou","doi":"10.1109/ICDE.2014.6816742","DOIUrl":null,"url":null,"abstract":"The popularity of social media services has been innovating the way of information acquisition in modern society. Meanwhile, mass information is generated in every single day. To extract useful knowledge, much effort has been invested in analyzing social media contents, e.g., (emerging) topic discovery. With these findings, however, users may still find it hard to obtain knowledge of great interest in conformity with their preference. In this paper, we present a novel system which brings proper context to continuously incoming social media contents, such that mass information can be indexed, organized and analyzed around Wikipedia entities. Four data analytics tools are employed in the system. Three of them aim to enrich each Wikipedia entity by analyzing the relevant contents while the other one builds an information network among the most relevant Wikipedia entities. With our system, users can easily pinpoint valuable information and knowledge they are interested in, as well as navigate to other closely related entities through the information network for further exploration.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Trendspedia: An Internet observatory for analyzing and visualizing the evolving web\",\"authors\":\"W. Kang, A. Tung, Wei Chen, Xinyu Li, Qiyue Song, Chao Zhang, Feng Zhao, Xiajuan Zhou\",\"doi\":\"10.1109/ICDE.2014.6816742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The popularity of social media services has been innovating the way of information acquisition in modern society. Meanwhile, mass information is generated in every single day. To extract useful knowledge, much effort has been invested in analyzing social media contents, e.g., (emerging) topic discovery. With these findings, however, users may still find it hard to obtain knowledge of great interest in conformity with their preference. In this paper, we present a novel system which brings proper context to continuously incoming social media contents, such that mass information can be indexed, organized and analyzed around Wikipedia entities. Four data analytics tools are employed in the system. Three of them aim to enrich each Wikipedia entity by analyzing the relevant contents while the other one builds an information network among the most relevant Wikipedia entities. With our system, users can easily pinpoint valuable information and knowledge they are interested in, as well as navigate to other closely related entities through the information network for further exploration.\",\"PeriodicalId\":159130,\"journal\":{\"name\":\"2014 IEEE 30th International Conference on Data Engineering\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 30th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2014.6816742\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 30th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2014.6816742","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Trendspedia: An Internet observatory for analyzing and visualizing the evolving web
The popularity of social media services has been innovating the way of information acquisition in modern society. Meanwhile, mass information is generated in every single day. To extract useful knowledge, much effort has been invested in analyzing social media contents, e.g., (emerging) topic discovery. With these findings, however, users may still find it hard to obtain knowledge of great interest in conformity with their preference. In this paper, we present a novel system which brings proper context to continuously incoming social media contents, such that mass information can be indexed, organized and analyzed around Wikipedia entities. Four data analytics tools are employed in the system. Three of them aim to enrich each Wikipedia entity by analyzing the relevant contents while the other one builds an information network among the most relevant Wikipedia entities. With our system, users can easily pinpoint valuable information and knowledge they are interested in, as well as navigate to other closely related entities through the information network for further exploration.