Exploring Weighted Property Approaches for RDF Graph Similarity Measure

TL;DR

This paper introduces a weighted property approach for RDF graph similarity measurement, emphasizing the importance of property weighting to improve accuracy and context-awareness in similarity assessments.

Contribution

The paper proposes a novel weighted property method for RDF graph similarity that incorporates property importance, enhancing traditional equal-weight approaches.

Findings

Improved similarity accuracy in RDF graphs.

Effective reflection of perceived similarity.

Promising results in vehicle domain dataset.

Abstract

Measuring similarity between RDF graphs is essential for various applications, including knowledge discovery, semantic web analysis, and recommender systems. However, traditional similarity measures often treat all properties equally, potentially overlooking the varying importance of different properties in different contexts. Consequently, exploring weighted property approaches for RDF graph similarity measure presents an intriguing avenue for investigation. Therefore, in this paper, we propose a weighted property approach for RDF graph similarity measure to address this limitation. Our approach incorporates the relative importance of properties into the similarity calculation, enabling a more nuanced and context-aware measures of similarity. We evaluate our approach through a comprehensive experimental study on an RDF graph dataset in the vehicle domain. Our results demonstrate that the proposed approach achieves promising accuracy and effectively reflects the perceived similarity between RDF graphs.