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2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (2018)
Laguna Hills, CA, USA
Sep 26, 2018 to Sep 28, 2018
ISBN: 978-1-5386-9555-5
pp: 85-92
ABSTRACT
Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.
INDEX TERMS
graph theory, ontologies (artificial intelligence)
CITATION

S. Pouriyeh et al., "Graph-Based Methods for Ontology Summarization: A Survey," 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Laguna Hills, CA, USA, 2018, pp. 85-92.
doi:10.1109/AIKE.2018.00020
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