In the Four-Dimensional World of EMMO, Materials Exist Together with Other Entities
Studying the vast classes and IRIs of EMMO is already a difficult task when we first encounter it. The inconvenient news is that we are soon asked to face an…
How to Use EMMO to Build a Materials Domain Ontology
“Anyone building a materials ontology will eventually encounter EMMO.” Researchers who set out to build an ontology in their own field will eventually arrive at the idea of a top-level…
Mediafication of Materials Research Data: A Topic Clusters-Based Knowledge Distribution System
As the media landscape grows more intelligent, specialized media platforms are expected to transcend the delivery of fragmented news and serve as knowledge guides that communicate the hierarchies and correlations…
SKOS: The Fast Track to Building Scalable Materials Science Ontologies
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…
The Beginning of Media-Lab: A Letter to You in the Future Where AI Media Becomes a Part of Everyday Life
On a future day when AI media has become a natural part of everyday life, I send this record to you — the one who continues to devote day and…
Graph RAG-based Materialization for Material Data Utilization
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…
Data Integrity and Consistency in Materials Research: From Fundamental Concepts to Innovative Strategies
As the paradigm of modern materials research shifts beyond model optimization toward Data-centric AI, which prioritizes data quality above all else, establishing reliable data assets has become the cornerstone of…
Formula Discovery vs. Physics Compliance: How SISSO and PINNs Differ
Moving beyond traditional black-box AI methodologies, what scientists today require is not just simple result prediction, but physics-based AI that can explain "why" a particular result was produced. While this…
Materialization: Pre-extracting All Discovery Scenarios for Advanced Materials Development
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…
A Comprehensive Guide to Metadata in Materials Science Research
When reflecting on the specialized nature and complexity of materials science data, a critical question often arises: "Is our data truly 'AI-friendly' enough for effective learning within this domain?" While…