This text discusses the process of generating a Knowledge Graph using a Mermaid Entity Relationship Model Diagram. It outlines the steps for creating the model and deploying it using Linked Data Principles. The text also provides guidance on generating ontology using RDF-Turtle and incorporating schema.org terms. It further explores the use of reasoning and inference to enhance the ontology. The text concludes with examples of SPARQL queries and the visualization of the Knowledge Graph using OpenLink Structured Data Sniffer Browser Extension.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Using CHAT-GPT to generate a Knowledge Graph from a Mermaid Entity Relationship Model | Adoption of CHAT-GPT for Knowledge Graph generation | Improved and more efficient Knowledge Graph generation | Need for automated and streamlined Knowledge Graph creation |
| Entity Relationship Model created using Mermaid’s Visual Notation | Use of Mermaid’s visual notation for creating Entity Relationship Models | More widespread use of visual notations for data modeling | Simplifying the process of data modeling and improving collaboration |
| Immediate viewability and browsing of Knowledge Graphs from any web browser | Accessibility of Knowledge Graphs from web browsers | Knowledge Graphs becoming more widely available and easier to access | Increased demand for easily accessible and user-friendly data visualization tools |
| Use of Linked Data Principles for deploying Knowledge Graphs | Adoption of Linked Data Principles for Knowledge Graph deployment | Greater interoperability and standardization of Knowledge Graphs | Facilitating data integration and exchange between different systems and organizations |
| Generating RDF-Turtle ontology from text using CHAT-GPT | Automation of ontology generation from text | Streamlined ontology development process | Improving efficiency and accuracy of ontology creation |
| Cross-referencing ontology classes with schema.org terms | Integration of schema.org vocabulary into ontologies | Improved interoperability and integration with schema.org-based systems | Aligning ontologies with widely used schemas for better data integration |
| Reasoning and inference in Knowledge Graphs | Application of reasoning and inference to Knowledge Graphs | Smarter and more intelligent Knowledge Graphs | Enabling advanced data analysis and decision-making capabilities |
| Focus on business-centric ontology design | Emphasis on business-oriented ontologies | More industry-specific ontologies and applications | Addressing specific business needs and requirements |
| Enhanced visualization and export options for Knowledge Graphs | Improved visualization and export capabilities for Knowledge Graphs | More interactive and customizable data visualization options | Empowering users to explore and share Knowledge Graphs more effectively |