Graph-based Ontology Summarization: A Survey
It addresses the problem of understanding large ontologies for users in fields like data modeling and knowledge management, but is incremental as it surveys existing methods without introducing new techniques.
This survey paper reviews existing ontology summarization techniques, focusing on graph-based methods that represent ontologies as graphs and use centrality measures to identify key elements, and highlights potential future research directions.
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.