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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Summarizing an Ontology: A "Big Knowledge" Coverage Approach.

Ling Zheng¹, Yehoshua Perl¹, Gai Elhanan¹

¹College of Computing, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA.

Studies in Health Technology and Informatics

|January 4, 2018

Summary

Summarizing large ontologies is crucial for understanding complex information. A new semi-automatic method using aggregate partial-area taxonomy effectively captures major topics, aiding comprehension.

Keywords:

Big Knowledge Ontology Summarization Topic Coverage

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Area of Science:

Biomedical Informatics
Knowledge Representation

Background:

Large ontologies, like SNOMED CT, are complex and difficult to maintain.
Effective summarization tools are needed for user comprehension of ontology content.

Purpose of the Study:

To present a parameterized methodology for semi-automatic ontology summarization.
To evaluate the effectiveness of this summarization technique using SNOMED CT's Specimen hierarchy.

Main Methods:

Developed an 'aggregate partial-area taxonomy' for compact ontology summarization.
Employed manual enhancement of the summarized taxonomy.
Used a domain expert-provided list of major topics as a gold standard for evaluation.

Main Results:

The aggregate taxonomy, after manual enhancement, effectively covers most of the major topics in the domain.
The methodology demonstrated a high degree of coverage for key concepts within the SNOMED CT Specimen hierarchy.

Conclusions:

Semi-automatic summarization using aggregate partial-area taxonomy is an effective approach for managing and understanding large ontologies.
This method facilitates better "big picture" comprehension of complex biomedical terminologies.