IEML: The Information Economy MetaLanguage
IEML is an acronym for Information Economy MetaLanguage. IEML is intended to become a standard for expressing semantic metadata and for modelling complex human systems. IEML is the result of many y…
A complete and up-to-date presentation of IEML can be found at: (peer reviewed scientific publication) Une présentation complète en français: (peer reviewed) The IEML website: The IEML Dictionary T…
IEML (Information Economy Meta-Language) is an open-source artificial method designed to represent the semantic content of linguistic signs in a computer-readable way. It was developed by Pierre Lévy as part of his work on collective intelligence, aiming to encode meaning in a way that is inspired by the structure of natural languages but also grounded in mathematics and logic abstractions.
The core goal of IEML is to make real-world data machine-readable by proposing a standard representation that enables the mapping of semantic representations with data in a computer-friendly manner. IEML’s design starts with a small set of primary concepts arranged in a matrix, which are then composed together to create more complex concepts. This process can be repeated, allowing for the creation of increasingly complex concepts through a matrix and fractal design. This design makes the representation easy to manipulate, quick to calculate distances between concepts, and simple to encode.
IEML is related to Semantic AI in that it serves as a foundation for semantic computation and semantic interoperability between systems. It is part of a neuro-symbolic architecture, specifically a neuro-semantic architecture, which emphasizes solving the problem of semantic computation. IEML facilitates the categorization of training data (text, image, sound, etc.) into various types of neural networks, which then input information into the system. This categorized data is transmitted to a semantic knowledge base, which is organized by a semantic network and supported by a graph database. The knowledge base can be presented as a hypertextual encyclopedia, allowing for the programming of simulations and various dashboards for monitoring and intelligence.
IEML is intended for use across multiple disciplines, including Artificial Intelligence, business intelligence, data science, heritage conservation, digital humanities, and digital communications. It addresses the problem of semantic interoperability among databases, languages, and disciplines, offering a powerful symbolic tool for these fields. IEML’s semantics are unambiguous and computable, aligning with its syntax, making it a well-formed symbolic system. This property allows IEML to serve as a concept coding system that lays the foundations for a new generation of artificial intelligence and enables collective intelligence to be reflexive. IEML provides a coordinate system for a common knowledge base that feeds both automatic reasoning and statistical calculations, fulfilling the promise of the Semantic Web through its computable meaning and interoperable ontologies.
In summary, IEML is a language with computable semantics that is designed to facilitate semantic computation and interoperability in artificial intelligence systems. It is a tool that bridges the gap between natural language and machine-readable data, enabling more sophisticated and coherent models of knowledge and enhancing the capabilities of AI systems in understanding and generating human language.