Content to Graph (C2G): The Graph Engineering AI Hub

What is Content-to-Graph (C2G)? Transforming Unstructured Data into Knowledge Graphs

Unstructured text accounts for over 80% of enterprise information. Learn how modern AI parsers convert prose into interconnected, multi-relational semantic graphs.

GraphRAG vs. Vector RAG: Why Chunk Similarity Fails Multi-Hop Reasoning

Cosine similarity searches fail when queries demand multi-step reasoning across documents. Explore how Graph-Augmented Generation solves retrieval fragmentation.

Self-Bootstrapping Ontologies: Using LLMs to Discover Domain Schemas

Hand-crafted OWL and RDF schemas take months to design. Discover how recursive LLM prompting uncovers dynamic domain taxonomies directly from raw corpuses.

Hands-on Blueprint: Building an End-to-End C2G Pipeline with Neo4j and LangChain

A technical walk-through detailing ingestion, chunking, spaCy NER, LLM-driven relation extraction, and direct Cypher insertion into Neo4j Aura.

Zero-Shot Open Information Extraction: Precision Benchmarks Across Frontier Models

Comparing Claude 3.5, GPT-4o, and Gemini 1.5 Pro on complex OpenIE tasks without labeled fine-tuning datasets.

Beyond Text: Turning PDFs, Complex Tables, and Charts into Interconnected Nodes

Enterprise intelligence is locked inside diagrams, balance sheets, and scanned PDFs. Here is how Vision-Language Models convert visual structures into graph nodes.

Temporal Knowledge Graphs: Handling Changing Facts and Schema Drift in AI

Facts expire. A CEO changes, interest rates shift, and corporate partnerships dissolve. Discover how quad-stores model the dimension of time.

The GNN + LLM Symbiosis: Combining Graph Neural Networks with Autoregressive Models

Why graph neural networks (GNNs) and LLMs are complementary: using structural graph embeddings to condition transformer generation.

Graph-Backed Agentic Memory: How Autonomous Agents Maintain Long-Term World Models

Autonomous agents suffer catastrophic context drift when using flat chat histories. Learn how dynamic knowledge graphs provide persistent, indexed memory.

Production Text-to-Cypher: Schema-Aware Prompting and Validation Guards

Generating raw database queries with LLMs can result in syntax failures or destructive queries. Implement schema introspections and AST guardrails.

Disambiguation at Scale: Entity Deduplication in Noisy Multi-Source Graphs

Resolving "IBM", "International Business Machines", and "Big Blue" to a single canonical entity node using string metrics and vector clustering.

Graph-Native Foundation Models: What Comes After 1D Autoregressive Tokenization?

Natural language is a 1D projection of high-dimensional concepts. Explore emerging research into native graph tokenizers and non-Euclidean architectures.