Summarizing an Ontology:A Big Knowledge Coverage Approach
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 CTs 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 domains main topics.
Big Knowledge Topic Coverage Ontology Summarization
Ling Zheng Yehoshua Perl Gai Elhanan Christopher Ochs James Geller Michael Halper
College of Computing,New Jersey Institute of Technology,Newark,NJ 07102-1982,USA
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
978-982
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）