Graph-based Ontology Summarization: A Survey

TL;DR

This survey reviews graph-based ontology summarization techniques, highlighting their strengths and weaknesses, and discusses future research directions to improve understanding of large ontologies.

Contribution

It provides a comprehensive overview of graph-based ontology summarization methods and analyzes their effectiveness and limitations.

Findings

Graph-based methods effectively identify key ontology elements.

Centrality measures are commonly used for summarization.

Future research should address current limitations and explore new approaches.

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.