Strategic models for heritage artifact recommendation that integrates generative AI and ontologies is the need of the hour. This paper proposes a knowledge-centric semantic model for heritage artifact recommendation which outgrows knowledge from the user perspective and the perspective of the dataset. The user query is enhanced with generated metadata from the Web 3.0 and classified using a strong Bi-LSTM model, and the artifacts are outgrown through generative AI using Gemini Pro as well as ontologies through standard knowledge repositories. This framework computes enhancing differential semantics through Second Order Co-occurrence Pointwise Mutual Information and CoSimRank used at several stages in the pipeline. The Dolphins Echolocation Algorithm is encompassed for optimization of the initial solution set to transform it into a more optimal solution set, using CoSimRank as an objective function. The overlap of generation of tags and the populated knowledge graphs in the dataset perspective to yield a matching instance reprioritization vector is the underlying strategy of the model, which uses both knowledge which is generated from the user perspective through preprocessed user queries, as well as the dataset perspective. An overall precision of 96.07%, recall of 97.07%, and F-measure of 96.57%, with a low False Discovery Rate of 0.04 makes the proposed SFARDE framework a best-in-class model for heritage artifact recommendation.