Enhancing the Credibility of Data Trading Through Blockchain-Enforced Semantic Analysis

In the evolving landscape of semantic document management, particularly within the ambit of criminal case processing, the industry confronts significant challenges such as data breaches, risks of information manipulation, and the complexity of predicting judicial outcomes. Recent incidents underline the critical need for robust security measures and innovative approaches to manage and analyze semantic documents efficiently. This paper introduces the Blockchain-Enabled Semantic Analysis and Data Transaction Platform (BELTSDT), a pioneering framework that integrates the security and transparency of blockchain technology with the analytical prowess of artificial intelligence and machine learning. BELTSDT aims to address the aforementioned challenges by offering a decentralized solution for storing semantic documents, automating semantic processes through smart contracts, and enhancing the accuracy of case outcome predictions through advanced semantic analysis and predictive modeling. The core of our methodology involves a novel algorithmic approach, the Triplet Embedding Convolutional Recurrent Neural Network (TeCRNN), designed to process and analyze semantic texts effectively.