Multi-layer Big Knowledge Visualization Scheme for Comprehending Neoplasm Ontology Content

Ling Zheng, Christopher Ochs, James Geller, Hao Liu, Yehoshua Perl, Sherri De Coronado

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Big Knowledge repositories, in the form of large ontologies, typically consist of many thousands of knowledge assertions. They have complex network structures consisting of nodes and links. Without some form of comprehension, humans cannot make correct, innovative and creative use of Big Knowledge. Visualization is an important tool for knowledge comprehension, however, the node-link diagrams become overwhelming for Big Knowledge. In order to support comprehension, we have developed methods for algorithmically summarizing ontology content and visualizing the summaries. These methods facilitate gaining an understanding of the 'big picture' of an ontology, which is essential for maintenance and integration into applications. Such a summary is called an abstraction network. Similar to the theory of limited working memory in humans, we assume that there is a limited human comprehension capacity for node-link ontology diagrams. In this paper, we present a visualization scheme that is based on multi-layer, multi-granularity abstraction networks of ontology content, each of which stays below a maximum number of nodes. We demonstrate this visualization scheme on the National Cancer Institute Thesaurus's Neoplasm subhierarchy.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017
EditorsRuqian Lu, Xindong Wu, Tamer Ozsu, Xindong Wu, Jim Hendler
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages127-134
Number of pages8
ISBN (Electronic)9781538631195
DOIs
StatePublished - Aug 30 2017
Event2017 IEEE International Conference on Big Knowledge, ICBK 2017 - Hefei, China
Duration: Aug 9 2017Aug 10 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017

Other

Other2017 IEEE International Conference on Big Knowledge, ICBK 2017
Country/TerritoryChina
CityHefei
Period8/9/178/10/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty

Keywords

  • Abstraction network
  • Big Knowledge
  • Knowledge Visualization
  • Ontology Summarization

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