TY - GEN
T1 - Multi-layer Big Knowledge Visualization Scheme for Comprehending Neoplasm Ontology Content
AU - Zheng, Ling
AU - Ochs, Christopher
AU - Geller, James
AU - Liu, Hao
AU - Perl, Yehoshua
AU - Coronado, Sherri De
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/30
Y1 - 2017/8/30
N2 - 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.
AB - 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.
KW - Abstraction network
KW - Big Knowledge
KW - Knowledge Visualization
KW - Ontology Summarization
UR - http://www.scopus.com/inward/record.url?scp=85031760073&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031760073&partnerID=8YFLogxK
U2 - 10.1109/ICBK.2017.40
DO - 10.1109/ICBK.2017.40
M3 - Conference contribution
AN - SCOPUS:85031760073
T3 - Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017
SP - 127
EP - 134
BT - Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017
A2 - Lu, Ruqian
A2 - Wu, Xindong
A2 - Ozsu, Tamer
A2 - Wu, Xindong
A2 - Hendler, Jim
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Big Knowledge, ICBK 2017
Y2 - 9 August 2017 through 10 August 2017
ER -