Literal2Feature: An Automatic Scalable RDF Graph Feature Extractor

The last decades have witnessed significant advancements in terms of data generation, management, and maintenance. This has resulted vast amounts becoming available a variety forms formats including RDF. As RDF is represented as graph structure, applying machine learning algorithms to extract valuable knowledge insights from them not straightforward, especially when the size enormous. Although Knowledge Graph Embedding models (KGEs) convert graphs low-dimensional vector spaces, these vectors often lack explainability. On contrary, this paper, we introduce generic, distributed, scalable software framework that capable transforming large into an explainable feature matrix. matrix can be exploited many standard algorithms. Our approach, by exploiting semantic web big technologies, able existing features deep traversing given graph. proposed open-source, well-documented, fully integrated active community project Semantic Analytics Stack (SANSA). experiments on real-world use cases disclose extracted successfully used tasks like classification clustering.