Xin Huang, Byron Choi, Jianliang Xu, W. K. Cheung, Yanchun Zhang, Jiming Liu
{"title":"Ontology-based Graph Visualization for Summarized View","authors":"Xin Huang, Byron Choi, Jianliang Xu, W. K. Cheung, Yanchun Zhang, Jiming Liu","doi":"10.1145/3132847.3133113","DOIUrl":null,"url":null,"abstract":"Data summarization that presents a small subset of a dataset to users has been widely applied in numerous applications and systems. Many datasets are coded with hierarchical terminologies, e.g., the international classification of Diseases-9, Medical Subject Heading, and Gene Ontology, to name a few. In this paper, we study the problem of selecting a diverse set of k elements to summarize an input dataset with hierarchical terminologies, and visualize the summary in an ontology structure. We propose an efficient greedy algorithm to solve the problem with (1-1/e)≈ 62%-approximation guarantee. Preliminary experimental results on real-world datasets show the effectiveness and efficiency of the proposed algorithm for data summarization.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"59 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3132847.3133113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Data summarization that presents a small subset of a dataset to users has been widely applied in numerous applications and systems. Many datasets are coded with hierarchical terminologies, e.g., the international classification of Diseases-9, Medical Subject Heading, and Gene Ontology, to name a few. In this paper, we study the problem of selecting a diverse set of k elements to summarize an input dataset with hierarchical terminologies, and visualize the summary in an ontology structure. We propose an efficient greedy algorithm to solve the problem with (1-1/e)≈ 62%-approximation guarantee. Preliminary experimental results on real-world datasets show the effectiveness and efficiency of the proposed algorithm for data summarization.