{"title":"链接发现:一个全面的分析","authors":"Nicolai Erbs, Torsten Zesch, Iryna Gurevych","doi":"10.1109/ICSC.2011.63","DOIUrl":null,"url":null,"abstract":"We present a comprehensive analysis of link discovery approaches. We classify them with regard to the type of knowledge being used, and identify three commonly used sources of knowledge: The text of a document, the document title, and already existing links. We analyze the influence of the knowledge source as well as of the amount of training data used. Results show that the link-based approach performs best if the amount of training data is huge. In a more realistic setting with fewer training data, the text-based approach yields better results.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Link Discovery: A Comprehensive Analysis\",\"authors\":\"Nicolai Erbs, Torsten Zesch, Iryna Gurevych\",\"doi\":\"10.1109/ICSC.2011.63\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a comprehensive analysis of link discovery approaches. We classify them with regard to the type of knowledge being used, and identify three commonly used sources of knowledge: The text of a document, the document title, and already existing links. We analyze the influence of the knowledge source as well as of the amount of training data used. Results show that the link-based approach performs best if the amount of training data is huge. In a more realistic setting with fewer training data, the text-based approach yields better results.\",\"PeriodicalId\":408382,\"journal\":{\"name\":\"2011 IEEE Fifth International Conference on Semantic Computing\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Fifth International Conference on Semantic Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSC.2011.63\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Fifth International Conference on Semantic Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSC.2011.63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a comprehensive analysis of link discovery approaches. We classify them with regard to the type of knowledge being used, and identify three commonly used sources of knowledge: The text of a document, the document title, and already existing links. We analyze the influence of the knowledge source as well as of the amount of training data used. Results show that the link-based approach performs best if the amount of training data is huge. In a more realistic setting with fewer training data, the text-based approach yields better results.