Knowledge Graph Construction Guide

Overview

Knowledge graphs (KGs) organize information as networks of entities and relationships, making them powerful tools for research synthesis, literature exploration, and AI-augmented retrieval. In academic contexts, knowledge graphs can represent relationships between papers, authors, methods, datasets, findings, and concepts -- enabling queries like "Which methods have been applied to dataset X?" or "What are the common limitations reported across studies of Y?"

This guide covers building knowledge graphs for research applications: defining schemas (ontologies), extracting entities and relations from text, storing and querying graph data, and integrating knowledge graphs with Retrieval Augmented Generation (RAG) systems for AI-powered research assistants.

Whether you are building a personal research knowledge base, constructing a domain-specific literature graph, or developing a RAG system for an academic chatbot, these patterns provide a solid foundation.

Knowledge Graph Fundamentals

Core Components