Ontology-based graph visualization for summarized view

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


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 11?e 62%-approximation guarantee. Preliminary experimental results on real-world datasets show the effectiveness and efficiency of the proposed algorithm for data summarization.

Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781450349185
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841


Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017

Scopus Subject Areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)


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