Importance of Knowledge Graphs for Enterprises

One year ago today, Dean Allemang Bryon Jacob and I released our paper "A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases" and WOW! Early 2023, everyone was experimenting with LLMs to do text to sql. Examples were "cute" questions on "cute" data. Our work provided the first piece of evidence (to the best of our knowledge) that investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems on SQL databases. The result was that by using a knowledge graph representations of SQL databases achieves 3X the accuracy for question-answering tasks compared to using LLMs directly on SQL databases. The release of our work sparked industry-wide follow-up: - The folks at dbt, led by Jason Ganz, replicated our findings, generating excitement across the semantic layer space - Semantic layer companies began citing our research, using it to advocate for the role of semantics - We continuously get folks thanking us for the work because they have been using it as supporting evidence for why their organizations should invest in knowledge graphs - RAG got extended with knowledge graphs: GraphRAG - This research has also driven internal innovation at data.world forming the foundation of our AI Context Engine where you can build AI apps to chat with data and metadata. Over the past year, I've observed two trends: 1) Semantics is moving from "nice-to-have" towards foundational: Organizations are realizing that semantics are fundamental for effective enterprise AI. Major cloud data vendors are incorporating these principles, broadening the adoption of semantics. While approaches vary (not always strictly using ontologies and knowledge graphs), the message is clear: semantics provides your unique business context that LLMs don't necessarily have. Heck, Ontology isn't a frowned upon word anymore 😀   2) Knowledge Graphs as the ‘Enterprise Brain’: Our work pushed to combine Knowledge Graphs with RAG, GraphRAG, in order to have semantically structured data that represents the enterprise brain of your organization. Incredibly honored to see Neo4j Graph RAG Manifesto citing our research as critical evidence for why knowledge graphs drive improved LLM accuracy. It's really exciting that the one year anniversary of our work is while Dean and I are at the International Semantic Web Conference. We are sharing our work on how ontologies come to the rescue to further increase the accuracy to 4x (we released that paper in May). This image is an overview of how it's achieved. It's pretty simple, and that is a good thing! I've dedicated my entire career (close to 2 decades) to figure out how to manage data and knowledge at scale and this GenAI boom has been the catalyst we needed in order to incentivize organizations to invest in foundations in order to truly speed up an innovate. There are so many people to thank! Here’s to more innovation and impact!

TL;DR: There has been a dramatic uptick in interest in Knowledge Graphs (KGs). Combined with LLMs, KGs can provide better insights into organizational data while reducing or even eliminating hallucinations just like some ideas in 𝗡𝗲𝘂𝗿𝗼-𝗦𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗔𝗜. A long time ago I wrote about how Symbolic AI and Neural AI will come together to unlock new value while lowering enterprise risk. (https://bit.ly/3WZQ11q). We are definitely headed down that path with some interesting startups like Elemental Cognition (https://lnkd.in/eFUhFYEZ) and Amazon Web Services (AWS) using symbolic techniques for security scanning of LLM generated code in Q Developer (https://lnkd.in/ecJTSSaS). Another variant albeit not Neuro-Symbolic AI is the 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗞𝗚𝘀 𝗮𝗻𝗱 𝗟𝗟𝗠𝘀. KGs are inherently symbolic and integrating with LLMs is a no-brainer for specific use cases. A great writeup of the 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 by the excellent Neo4j team (Philip Rathle, Emil Eifrem): https://lnkd.in/ebR6tMD8 which itself builds on some great work by the Microsoft GraphRAG team (https://lnkd.in/enRpA6Y7). Benefits summary: 1. 𝗛𝗶𝗴𝗵𝗲𝗿 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 & More Useful Answers  • A KG combined with an LLM improved accuracy by 3x • LinkedIn showed that KG integrated LLMs outperforms the baseline by 77.6% (https://lnkd.in/eNvvQaeq) 2. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗗𝗮𝘁𝗮 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗙𝗮𝘀𝘁𝗲𝗿 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, and More 𝗔𝗻𝗱 𝗵𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝘄𝗶𝘀𝘁: KGs and ontologies have historically been hard to create and maintain. Turns out you can use LLMs+ to simplify that process!! Great research work here: https://lnkd.in/eTyGjSe5 and actual implementation by the Neo4J team (https://bit.ly/3WIJxmd). If you want to try this using AWS services give it a whirl here: https://go.aws/3T8FK0L 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝘅𝗢𝘀: Consider adding Knowledge Graphs to your enterprise Data and GenAI strategy.

There’s been a lot of talk about making LLM outputs more deterministic – especially surrounding agents. What’s often overlooked in the push for deterministic outputs is the input itself: context. In most enterprise AI systems, “context” is still treated as raw data. But to answer complex, multi-hop questions like “How is engineering project Y tracking against its OKRs?”, agents need a deeper understanding of cross-system relationships, enterprise-specific language, and how work actually gets done. LLMs aren’t built to infer this on their own. They need a machine-readable map of enterprise knowledge – something consumer search systems have long relied on: the knowledge graph. But applying that in the enterprise brings a new set of challenges: the graph must enforce data privacy, reason over small or fragmented datasets without manual review, and do so using scalable algorithms. At Glean, we’ve built a knowledge graph with thousands of edges, recently expanded into a personal graph that captures not just enterprise data, but how individuals work. This foundation sets the stage for personalized, context-aware agents that can anticipate needs, adapt to organizational norms, and guide employees toward their goals, far beyond the limits of chat session history. We break this down in more detail in our latest engineering blog on how knowledge graphs ground enterprise AI and why they’re foundational to the future of agentic reasoning. https://lnkd.in/g-rVJPri

We’re continuing the conversation with clients about knowledge graphs—and I suspect we’ll be talking about them a lot more in the coming years. While they’re not exactly new, they are becoming increasingly relevant – and appearing on corporate wishlists – as organizations struggle with the limitations of traditional databases and the unpredictability of AI-generated content. Databases store data, but knowledge graphs store relationships—a crucial difference when dealing with meaning-rich information. They make it possible to structure knowledge in a way that reflects how humans actually think and enable sophisticated reasoning, rather than forcing everything into rigid tables and keyword searches. One of the most interesting aspects is how they ground AI systems in factual knowledge, reducing the hallucination problem that plagues large language models. The question is: If AI is built on unreliable or incomplete data, how do we expect it to produce meaningful results? Mike Dillinger, PhD and I explored these issues in our latest research, Taming Global Content with Knowledge Graphs (https://lnkd.in/ge5SwYtF). A few key questions that keep coming up: * How do we ensure AI tools work with accurate, contextual knowledge rather than probabilistic guesswork? * How do we build knowledge graphs that actually work across multilingual and multicultural contexts? * What is the role of language professionals in working with what seems like an engineering resource? For those working with AI, content management, or multilingual systems, this isn’t just a technical issue—it’s a strategic one. If we don’t structure knowledge in ways that make sense for both machines and humans, we’ll end up with AI that can generate words but doesn’t understand them. If the goal of language services has always been to transmit knowledge across boundaries of language and place, knowledge graphs should be a core task for LSPs and enterprise language teams. Would be curious to hear how others are approaching this challenge. #KnowledgeGraphs #AI #LLMs #Localization #DataStrategy

Enterprises will need knowledge graphs for agentic AI quality and autonomy, no matter how good models get at context window processing. To be clear — it’s fall-off-chair exciting watching our latest (shhh) Palmyra models with NO RAG beat previous generations of models WITH RAG for use cases where there’s a lot of data / context to sift through (millions of words) to get an accurate and high quality response. Anyone who’s followed Writer for a while knows we’re all about that graph-based RAG, but even better models doesn’t mean that “RAG is dead,” as I have been seeing banded about a bit online by generative AI companies waking up to the limitations of vector-based RAG. Why? Because we’ve seen that a graph-based RAG approach is really efficient at helping self-orchestrating agentic AI get good at making the same kind of decisions a human would. Enterprise knowledge is embedded in the complex, dynamic network of people, processes, and tools that make organizations function. It’s not just Sharepoint pages and data warehouses where “knowledge” sits — it’s everywhere. Agentic AI must learn, reason, and operate within this complexity. For truly successful and self-orchestrating agentic AI in the enterprise, we need: -Continuous and autonomous knowledge integration, where relationships are understood, and knowledge here is data +++ -Process blueprints -LLMs with continuous learning + memory (large context window + self-evolving)  -Supervision and control What’s the best way to build knowledge graphs that you can use with agentic AI? There’s a shortcut — we’ve trained a specialized LLM that can build graphs (ie the topic nodes and relationship edges) in real time on customer data, and it can be enriched with a customer’s own ontologies.