Why is modern AI so "unruly"? According to information architect Jessica Talisman, it’s because we’ve over-indexed on data storage and ignored the art of description.

In this Graph Chat, Bryce Merkel Sasaki sits down with Jessica (Founder of The Ontology Pipeline) to discuss the bridge between Labeled Property Graphs (LPG) and RDF, the rise of neuro-symbolic AI, and why every tech company needs the perspective of a "Chief Librarian."

• Substack: Intentional Arrangement
• Framework: The Ontology Pipeline

Join David Hughes (GraphGeeks community) for a Graph Chat with Chang She, CEO and Co-founder of LanceDB, filmed at the ODSC conference.

Chang shares groundbreaking insights on how agentic retrieval systems are challenging traditional RAG approaches, requiring much higher throughput and iterative search. The conversation highlights the new Lance Format as the multimodal Lakehouse standard optimized for AI data operations.

Most excitingly for the graph community, Chang provides a first introduction on the new open-source project, Lance Graph, which enables storing graph schemas and executing Cypher-like queries directly on Lance tables, integrating vector, tabular, and graph data into a unified format.

Learn why data differentiation is the key to winning in the age of AI agents.

https://lancedb.com/

Join Amy Hodler of GraphGeeks and Henry Gabb, Chair of the newly renamed Graph Data Council (GDC) (formerly the Linked Data Benchmark Council - LDBC), for a deep dive into the world of vendor-neutral graph benchmarks, standards, and innovation.

Hear how the GDC is expanding its focus beyond traditional benchmarks like FinBench and Graphalytics to embrace microbenchmarks, synthetic data generation, and the exciting work being done on LEX schema to potentially unify property graphs and RDF. Learn why many members, including major vendors and researchers, value having a "seat at the table" to shape the future direction of the graph community.

Explore the intersection of data performance, complexity, and the drive for standard graph query languages and schemas essential for emerging AI applications like text-to-graph query.

https://ldbcouncil.org/

Amy Hodler and Dave Bechberger dive into the crucial role of memory in advanced AI systems, especially at the intersection of graphs, knowledge graphs, and generative AI.

Dave Bechberger, currently focusing on MCP servers, agentic memory, and semantic data layers, explains that memory is fundamental because standard LLM calls are atomic and lack recollection of prior interactions. An agent without memory lacks continuity for complex user interactions.

The discussion breaks down three key types of memory and how graphs apply:

• Episodic Memory: Transactional details are directly integrated into the context.
• Short-Term Memory: Session-based interactions that require compaction or summarization.
• Long-Term Memory: For extracting and storing patterns, trends, and preferences across multiple interactions.

Join Amy Hodler and Ricky Sun, CEO and founder of the high-performance graph platform Ultipa, as they explore the current state and future direction of graph technology. The discussion highlights the significance of the new ISO standard GQL (Graph Query Language), which Ricky believes will rapidly accelerate market adoption by providing a common language and preventing vendor lock-in. He also offers a practical view on Graph and AI, arguing that while AI is great for the "first and last mile", high-performance graph computing must handle the critical middle—providing real-time, white-box explainable reasoning for deep, trustworthy insights.

GQL Book - https://www.packtpub.com/en-in/product/getting-started-with-the-graph-query-language-gql-9781836204008

Free GQL Playground https://www.ultipa.com/gql-playground

In this episode of the Graph Geeks in Discussion podcast, join host Amy Hodler and special guest Paco Nathan as they unpack the latest trends in AI and graph technology. They dive into insights from recent conferences, highlighting a shift toward practical, programmatic uses of generative AI within the software development lifecycle. Paco explains how companies are moving beyond simple code generation to focus on real-world applications like fault detection and root cause analysis. The conversation also explores the rise of hybrid AI—combining neural networks with symbolic systems like knowledge graphs—to create more efficient and explainable models. Whether you're a developer, data scientist, or just curious about the future of AI, this episode offers a deep dive into the innovations driving the industry forward, from the promise of neurosymbolic AI to the practical use cases of Graph RAG.

In this discussion with Dr. Denise Gosnell, an entrepreneur, business strategist, and author. Denise shares insights from her impressive career—from a college athlete to a PhD, to leading graph teams at AWS and Datastax—and discusses the inspiration behind her new book, Tech Confidential: An Insider's Playbook for Daring Entrepreneurs. She reveals how the book, structured like an onion with layers on ego, team dynamics, product-market fit, and exit strategies, provides a no-nonsense guide to navigating the tech industry. We also dive into why graph technology hasn't yet gone mainstream and discuss the importance of embracing chaos and having a coach, reminding listeners that you should never try to succeed alone.

Playbook and resources: https://www.techconfidential.ai/

Join host Amy Hodler and guest Sumit Pal, a former Gartner research VP and current Strategic Technology Director at Graphwise, as they dive into the power of graph technology and its impact on AI. They discuss how graph-based data gives AI the crucial context it needs to deliver better results.

In this podcast:

• How graphs help make data "AI-ready" by resolving quality issues and providing critical context.
• The difference between what a traditional AI model can do with flat data (what happened) versus what a graph-enhanced model can do (why it happened).
• Why the future of AI might be in smarter, more domain-specific Small Language Models (SLMs) rather than just massive LLMs.
• How Graph RAG can dramatically improve AI outputs.

Follow up with Graphwise on https://graphwise.ai/.

Join us for an insightful discussion with Tomaz Bratanic, Graph ML and GenAI Research expert at Neo4j, as we dive deep into the world of GraphRAG and his newly released book "Essential GraphRAG – a practical guide to combining Knowledge Graphs with Retrieval-Augmented Generation (RAG)" (Manning Publications, co-authored with Oskar Hane).

We explore how GraphRAG enhances LLM accuracy by combining the power of knowledge graphs with retrieval-augmented generation, covering everything from vector similarity search to agentic RAG systems. Tomaz also shares his unconventional journey from professional poker player to graph researcher.

Key Topics Covered:

• The importance of incorporating more than just unstructured text
• Advanced retrieval strategies and Text2Cypher generation
• Building knowledge graphs with LLMs and Microsoft's GraphRAG approach

Resources:

📘 Free eBook from Neo4j: https://neo4j.com/essential-graphrag/

Join Amy Hodler, David Haglin (Rocketgraph), and David Hughes (Enterprise Knowledge) as they explore graph-wide scanning and its role in revolutionizing cybersecurity. Discover how graphs are eliminating blind spots, detecting advanced persistent threats, and transforming how analysts find crucial insights in massive datasets. Learn why traditional methods fall short and how AI integration is key to navigating the colossal scale of cyber data for unprecedented clarity and threat detection.

Want to take Rocketgraph for a spin? https://rocketgraph.com/free-trial/