Paco Nathan



Graph RAG is all the rage right now in the AI world. Paco Nathan is uniquely positioned to help the industry understand and contextualize this new technology.



Paco currently leads a knowledge graph practice at an AI startup, and he has been immersed in the AI community for more than 40 years.



His broad and deep understanding of the tech and business terrain, along with his "graph thinking" approach, provides executives and other decision makers a clear view of terrain that is often obfuscated by less experienced and knowledgeable advisors.



We talked about:



his work building out the knowledge graph practice at Senzing, and their focus on entity resolution

the importance of entity resolution in knowledge graph use cases like fraud detection

the high percentage of knowledge graph projects that we never hear about because of their sensitive or proprietary nature

his take on the concept of "graph thinking" and how he and colleagues illustrate it with a simple graph model of a medieval village

how graphs add structure and context to our understanding of the world

the importance of embracing complexity and the Cynefin framework in which he grounds various types of business challenges: simple, complicated, complex, and chaotic

how to apply insights discerned from a Cynefin framing in management

how knowledge graphs can help oranizations understand the complex environments in which they operate

the wide range of industries and government entities that are applying knowledge graphs to concerns like supply chains, ESG, etc.

his overview of RAG - retrieval augmented generation and graph RAG

the wide variety of uses of the term "graph" in the current technology landscape



Microsoft's graph RAG which uses NetworkX inside their graph RAG library, not a graph database

Neo4j's approach which creates a "lexical graph" based an an NLP analysis of text

"embedding graphs"

ontology-based graphs

Google's approach to RAG, using graph neural networks

graphs that do reasoning over LLM-created facts assertions

"graph of thought" graphs based on chain-of-prompt thinking

"causal graphs" that permit causal reasoning

"graph analytics" graphs that re-rank possible answers





the evolution of graph RAG libraries and the variety of design patterns they employ

the shift in discovery dominance from search to recommender systems, most of which use knowledge graphs

examples of graph RAG from LlamaIndex and LangChain, in addition to Microsoft's graph RAG

his prediction that we'll see more reinforcement learning, graph tech, and advanced math capabilities like causality in addition to LLMs in AI systems

his reflection on his efforts to advance graph thinking over the past 4 years and the current state of LLMs, graphs, graph RAG, and the open-source software community

the need for a shift in thinking in the industry, in particular the need for cross-pollination across tech proficiencies and enterprise teams

the "10:1 ratio for the number of graph RAG experts versus the number of people we've actually worked with a library"



Paco's bio

Paco Nathan leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and is a computer scientist with +40 years of tech industry experience and core expertise in data science, natural language, graph technologies, and cloud computing. He's the author of numerous books, videos, and tutorials about these topics.



Paco advises Kurve.ai, EmergentMethods.ai, KungFu.ai, DataSpartan, and Argilla.io (acq. Hugging Face), and is lead committer for the pytextrank and kglab open source projects. Formerly: Director of Learning Group at O'Reilly Media;...