@inproceedings{ad178e1957b7459abd5cd1a2f6c6288e,
title = "Summarizing an Ontology: A {"}Big Knowledge{"} Coverage Approach",
abstract = "Maintenance and use of a large ontology, consisting of thousands of knowledge assertions, are hampered by its scope and complexity. It is important to provide tools for summarization of ontology content in order to facilitate user {"}big picture{"} comprehension. We present a parameterized methodology for the semi-automatic summarization of major topics in an ontology, based on a compact summary of the ontology, called an {"}aggregate partial-area taxonomy{"}, followed by manual enhancement. An experiment is presented to test the effectiveness of such summarization measured by coverage of a given list of major topics of the corresponding application domain. SNOMED CT's Specimen hierarchy is the test-bed. A domain-expert provided a list of topics that serves as a gold standard. The enhanced results show that the aggregate taxonomy covers most of the domain's main topics.",
keywords = "Big knowledge, Ontology summarization, Topic coverage",
author = "Ling Zheng and Yehoshua Perl and Gai Elhanan and Christopher Ochs and James Geller and Michael Halper",
note = "Publisher Copyright: {\textcopyright} 2017 International Medical Informatics Association (IMIA) and IOS Press.; 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 ; Conference date: 21-08-2017 Through 25-08-2017",
year = "2017",
doi = "10.3233/978-1-61499-830-3-978",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "978--982",
editor = "Gundlapalli, {Adi V.} and Jaulent Marie-Christine and Zhao Dongsheng",
booktitle = "MEDINFO 2017",
address = "Netherlands",
}