#Knowledge Graphs

From LLM Agents to Scientific Knowledge Graphs

Agents-K1 revolutionizes LLM research agents by creating agent-native scientific knowledge graphs from full papers, enabling deeper scientific reasoning.

Neo4j: Context Graphs for AI Agents

Neo4j experts Andreas Kollegger and Zaid Zaim discuss how context graphs enhance AI agents for explainable and decision-aware operations.

The 4 Types of AI Agent Memory Explained

IBM Master Inventor Martin Keen details the four essential memory types AI agents need: working, semantic, procedural, and episodic.

Neo4j's Stephen Chin on Context Graphs for AI

Stephen Chin from Neo4j discusses how context graphs, built on knowledge graph technology, are essential for creating explainable and context-aware AI agents.

Beyond Text: Rethinking Docs for AI Agents

The OBJECTGRAPH file format redefines documents as traversable knowledge graphs, slashing token usage for AI agents while maintaining accuracy.

Neo4j CEO Emil Eifrem on Graph Databases and AI

Neo4j CEO Emil Eifrem discusses the symbiotic relationship between graph databases and AI, highlighting how relational context is crucial for modern AI applications.

Potpie AI Secures $2.2M for Engineering Agents

Potpie AI secured $2.2 million in pre-seed funding to integrate AI agents into complex engineering systems by unifying context across codebases.

AI Agent Ontologies Bridge Business-Data Divide

Chemstack AI Powers Mstack's 10x Growth, Reshaping Chemical R&D

Beyond Retrieval: Patho AI's Wisdom-Driven Knowledge Graphs Unlock True AI Understanding

The Quest for AI Engineering's Standard Model

Aeneas: Unlocking New Eras in Historical Discovery

Google DeepMind's Aeneas utilizes sophisticated machine learning to identify subtle patterns and semantic relationships across vast historical document datasets. Unlike traditional keyword-based methods, it discerns complex connections that often elude human researchers.