  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.
