博文

"Standardization of DIKWP Semantic Mathematics " Released

已有 707 次阅读 2024-10-7 15:03 |系统分类:博客资讯

The International Standard for "Standardization of DIKWP Semantic Mathematics " Officially Released

Recently, the international standard for "Standardization of DIKWP Semantic Mathematics" was officially released by International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC), World Association of Artificial Consciousness (WAC), and World Conference on Artificial Consciousness (WCAC). This marks another major milestone in the DIKWP standards system following the release of the "DIKWP Conceptual Semantic Standardization", signifying a significant breakthrough in the standardization of the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model.

With the rapid development of AI technologies, the DIKWP model has been increasingly applied across various fields. To ensure the reliability and effectiveness of these applications, the DIKWP-SC, in collaboration with WAC and WCAC, jointly developed the "DIKWP Semantic Mathematical Standardization" international standard. This standard aims to provide a unified specification for the semantic mathematical representation of the DIKWP model, promoting interoperability between different systems and standardized evaluation.

The "Standardization of DIKWP Semantic Mathematics" international standard covers the following key sections:

1. Overview of the DIKWP Model: A detailed introduction to the fundamental concepts and framework of the DIKWP model, including definitions and interrelations of Data, Information, Knowledge, Wisdom, and Purpose.

2. Semantic Mathematical Representation of the DIKWP: Provides mathematical representation methods for data, information, knowledge, wisdom, and purpose, including Conceptual Space (ConC), Cognitive Space (ConN), and mathematical models of graph structures. These mathematical models offer precise, consistent, and adaptive methods for understanding and manipulating the interactions between DIKWP components.

3. Practical Case Studies: Demonstrates the application of the DIKWP model in fields such as medical decision support systems, environmental management systems, and financial trading systems. Examples include:

l Medical Decision Support System: Using patient data to generate personalized treatment plans that consider patient preferences and ethical guidelines.

l Environmental Management System: Monitoring environmental quality through sensor data and formulating policies that balance economic development with environmental protection.

l Financial Trading System: Analyzing market data to develop strategies that optimize long-term returns while managing risks.

The development of this standard has been strongly supported by 60 institutions from various enterprises, research institutes, and universities in the AI field. Experts and scholars from these institutions actively participated in the drafting and review process of the standard, ensuring its scientific rigor and practicality. The participating units include the Korea Advanced Information Technology Research Center (CAlTech), Asian Institute of Technology, Peking University, The Fifth Research Institute of Electronics (Ministry of Industry and Information Technology), Hainan Meteorological Bureau, Harbin Institute of Technology, and others.

Both " DIKWP Conceptualization Semantics Standards " and "Standardization of DIKWP Semantic Mathematics" have been publicly released on internationally renowned research platforms, ResearchGate and ScienceNet. The detailed clauses of the standards, as well as their theoretical foundations, are now open to all interested individuals and organizations for reference.

We warmly invite experts from all fields to join our committee and participate in the continuous improvement of this standards system, with the goal of making it a globally recognized data governance norm. Whether through technical suggestions or the exploration of application scenarios, we welcome your contributions. Your participation will inject new momentum into the development of DIKWP standards, promoting the healthy growth of AI technology.