A Conceptual Framework for Semantic Indexing of Data Sources Based on Structured Peer-to-Peer Model, Hilbert Curve, Hypercube and Data Analysis

Abstract

Semantic indexing ensures better organization and optimized searching of heterogeneous, autonomous, and distributed data sources. This approach leverages meaning and context rather than just keywords to better manage the increasing volume, complexity, and heterogeneity of modern data, enabling precise searching, optimized integration, and improved interoperability between domains. Several approaches to semantic indexing are available: ontology-based indexing, machine learning and automated semantic annotation of data sources. However, the main challenge remains scaling up. This article focuses on a conceptual framework designed for scalable semantic indexing of data sources based on a structured peer-to-peer architecture adapted for managing a very large number of nodes, Hilbert curve renowned for its preservation of semantic affinity while scaling, hypercube structure with its efficient diffusion algorithm, semantic annotation of data sources based on keywords, as well as machine learning techniques, in particular, multidimensional data analysis. An illustrative exploratory example of the Meta Skills semantic class is presented to outline the proposed architecture. This study proposes a conceptual and exploratory framework for large-scale semantic indexing of data sources. The proposed approach has not yet been implemented or validated on a large scale; its objective is to provide an initial structured model to serve as a basis for future empirical research.