We focus on materials ontology and data standardization technologies for the systematic sharing and utilization of materials data. We introduce trends in securing data interoperability between diverse research institutions and enterprises, and building AI-interpretable materials knowledge graphs.
To connect fragmented materials research data systematically and create value, an ontological approach is no longer a choice—it is a necessity. However, when faced with the vastness of the materials…
In this post, we will take a deep dive into Materialization techniques within the framework of Knowledge Graphs and Graph RAG, specifically focusing on how to systematically transform the complex…
In modern data engineering, Materialization has evolved beyond a mere technical means of enhancing query performance. It is now a core strategy for completing the structuralization of knowledge by breathing…
Based on the standardization required for data utilization, large-scale data must communicate with each other to provide materials researchers with new insights. At this juncture, we encounter the concept of…
As attempts to leverage AI and Big Data in materials science become increasingly prevalent, a critical prerequisite has emerged: Data Standardization. There is a common misconception that standardization is primarily…
📌 Why is Materials Ontology essential for innovation in advanced materials development? In this post, we explore how to overcome data silos, teach the "language of materials" to AI, and…