{"title":"Unsupervised real-time induction and interactive visualization of taxonomies over domain-specific concepts","authors":"M. Kejriwal, Ke Shen","doi":"10.1145/3487351.3489481","DOIUrl":null,"url":null,"abstract":"Given a domain-specific set of concept labels, taxonomy induction is the problem of inducing a taxonomy over the concept labels. Despite its importance in problems such as e-commerce, and some algorithmic research as a consequence, practical tools for taxonomy induction and interactive visualization do not currently exist. To be truly useful, such a tool must permit a reasonable solution in a relatively unsupervised setting, and be applicable to general subsets of concept labels. In this paper, we present an unsupervised, end-to-end taxonomy induction system for arbitrary concept-labels from the e-commerce domain. Our system only takes a simple text file as input and yields a tree-like taxonomy that can be rendered on a browser, and that a non-technical user can interact with. Important components of the system can also be customized by a technically experienced user.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3487351.3489481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Given a domain-specific set of concept labels, taxonomy induction is the problem of inducing a taxonomy over the concept labels. Despite its importance in problems such as e-commerce, and some algorithmic research as a consequence, practical tools for taxonomy induction and interactive visualization do not currently exist. To be truly useful, such a tool must permit a reasonable solution in a relatively unsupervised setting, and be applicable to general subsets of concept labels. In this paper, we present an unsupervised, end-to-end taxonomy induction system for arbitrary concept-labels from the e-commerce domain. Our system only takes a simple text file as input and yields a tree-like taxonomy that can be rendered on a browser, and that a non-technical user can interact with. Important components of the system can also be customized by a technically experienced user.