Semantic networks for data representation, merging and analysis: 'literary calculus'?

摘要

Database merging and interoperability is a prerequisite for intelligent data exploitation, and thus represents a major challenge for many companies. Working from the observation that man excels in the use of natural language for processing information, we propose a paradoxical approach that involves transforming structured information into textual information. By translating databases into semantic networks, we obtain informational material of remarkable fluidity and miscibility, greatly expanding query and analysis capabilities, and opening the way toward what we might call "literary calculus".