Learning from ChatGPT

#4467 Chemistry and other Domains for KG Discovery Projects #4467 Describe how to apply a knowledge graph to chemistry to discover Applying a Knowledge Graph (KG) to chemistry is like giving an AI a highly interconnected, multi-dimensional map of the molecular world. Instead of viewing chemicals as isolated data points in a spreadsheet, a knowledge graph treats them as a web of relationships, allowing researchers to discover hidden connections that standard databases miss. Here is a breakdown of how a knowledge graph is built and applied to chemistry to drive groundbreaking discoveries. 1. The Structure: Entities and Relations In a chemical knowledge graph, data is structured as a network of nodes (entities) and edges (the relationships between them). Node Type (Entities) Edge Type (Relationships) Target Node (Entities) Molecule A inhibits Protein X Protein X is associated with Disease Y Reaction B requires catalyst Element Z Molecule A is a substructure of Molecule C By connec...

#4466 Systems Engineering KG Methods for Discovery #4466  I have a systems engineering knowledge graph with nodes->relationships Domain-->System --> Subsystem --> Component --> Part. I want to look for opportunities to fix and/or improve systems and their elements by performing cross-domain analysis. Describe 15 good methods to apply Cypher Queries along with LLMs applying innovative thinking methods to provide generative-AI proposed solutions. To uncover hidden opportunities, resolve engineering contradictions, and drive systemic innovation across your Domain --> System --> Subsystem --> Component --> Part hierarchy, you can pair Cypher queries with LLM-driven innovative thinking methodologies (such as TRIZ, First Principles, and Systems Dynamics). By using Cypher to extract structural topology, dependencies, and functional mappings, you give the LLM the exact contextual "ground truth" it needs to generate highly targeted, feasible engineering ...