A Simple and Efficient Framework for Identifying Relation-gaps in Ontologies

TL;DR

This paper introduces a simple, efficient machine learning framework to identify relation gaps in ontologies, enhancing their richness by discovering potential object properties between class pairs.

Contribution

It presents a novel low-complexity machine learning approach for discovering object properties in ontologies, addressing the challenge of relation gap identification.

Findings

Framework achieves high precision in identifying class pairs with potential object properties

Method demonstrates low computational complexity and efficiency

Results show promising retrieval of relevant class pairs for ontology enrichment

Abstract

Though many ontologies have huge number of classes, one cannot find a good number of object properties connecting the classes in most of the cases. Adding object properties makes an ontology richer and more applicable for tasks such as Question Answering. In this context, the question of which two classes should be considered for discovering object properties becomes very important. We address the above question in this paper. We propose a simple machine learning framework which exhibits low time complexity and yet gives promising results with respect to both precision as well as number of class-pairs retrieved.