{"title":"小搜索游戏:通过人工计算获取网络词汇的游戏","authors":"Jakub Simko, M. Tvarozek, M. Bieliková","doi":"10.1145/1995966.1995977","DOIUrl":null,"url":null,"abstract":"Semantic structures, ranging from ontologies to flat folksonomies, are widely used on the Web despite the fact that their creation in sufficient quality is often a costly task. We propose a new approach for acquiring a lightweight network of related terms via the Little Search Game - a competitive browser game in search query formulation. The format of game queries forces players to express their perception of term relatedness. The term network is aggregated using \"votes\" from multiple players playing the same problem instance. We show that nearly 91% of the relationships produced by Little Search Game are correct and also elaborate on the game's unique ability to discover term relations, that are otherwise hidden to typical corpora mining methods.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"12 1","pages":"57-62"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Little search game: term network acquisition via a human computation game\",\"authors\":\"Jakub Simko, M. Tvarozek, M. Bieliková\",\"doi\":\"10.1145/1995966.1995977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Semantic structures, ranging from ontologies to flat folksonomies, are widely used on the Web despite the fact that their creation in sufficient quality is often a costly task. We propose a new approach for acquiring a lightweight network of related terms via the Little Search Game - a competitive browser game in search query formulation. The format of game queries forces players to express their perception of term relatedness. The term network is aggregated using \\\"votes\\\" from multiple players playing the same problem instance. We show that nearly 91% of the relationships produced by Little Search Game are correct and also elaborate on the game's unique ability to discover term relations, that are otherwise hidden to typical corpora mining methods.\",\"PeriodicalId\":91270,\"journal\":{\"name\":\"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media\",\"volume\":\"12 1\",\"pages\":\"57-62\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1995966.1995977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1995966.1995977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Little search game: term network acquisition via a human computation game
Semantic structures, ranging from ontologies to flat folksonomies, are widely used on the Web despite the fact that their creation in sufficient quality is often a costly task. We propose a new approach for acquiring a lightweight network of related terms via the Little Search Game - a competitive browser game in search query formulation. The format of game queries forces players to express their perception of term relatedness. The term network is aggregated using "votes" from multiple players playing the same problem instance. We show that nearly 91% of the relationships produced by Little Search Game are correct and also elaborate on the game's unique ability to discover term relations, that are otherwise hidden to typical corpora mining methods.