Understanding Graph Technologies

For years, as a knowledge graph practitioner, I kept hearing the same refrain: you don't need an ontology to do knowledge graphs. Too complicated. Unnecessary overhead. Just connect the data and move on. Now, mildly amusing, I'm encountering the reverse. An organisation realises it needs an ontology, and gets told by some: yes, you need an ontology - but not a knowledge graph. That part is too complicated. At the same time, Context Graph is now gaining traction as a term. It’s often positioned as a fresh idea, when in reality it rebrands knowledge graph principles. We’ve been here before - first with the term Semantic Web, then with Linked Data. Let me cut through all of this. 🔵 The Truth Is Simple To solve the data integration problem - to make your organisation's data AI-ready - you need two things. First you need to share meaning clearly: the abstract concepts, the definitions, the metadata that describes your world. That's an ontology. Second, you need to connect your data into a rich network of relationships. No fact lives in splendid isolation. Its value comes from how it relates to other facts. In any organisation of scale, this means a decentralised way of identifying and linking facts together. That's a graph - a vast, distributed graph. 🔵 These Are Not Separate Things They are one thing. You need to move seamlessly from individual facts up into the conceptual realm - to reason at the level of abstractions. Then you need to come back down from concepts into the world of facts - to ground that reasoning in reality. Put those together and you have a knowledge graph. The ontology without the graph is a map with no territory. The graph without the ontology is territory with no map. Neither works alone. 🔵 The Final Piece: Open Standards It's not enough to get your data AI-ready for today's task - enabling agents to work with your internal knowledge. You also need to prepare for what comes next. For organisations that successfully navigate this phase, the future is interoperability: AI marketplaces where agents, data, and meaning flow across boundaries. That future only works if what you build today is based on open standards. True open standards - from recognised bodies like the W3C, with wide adoption. Not proprietary formats dressed up as "open." Not vendor-specific schemas that lock you in. Only then can your AI-ready data seamlessly plug into the ecosystems of tomorrow. 🔵 The Bottom Line Don't let anyone split what should be whole. Ontology and graph are two aspects of the same solution. Meaning and connection. Abstraction and grounding. You need both. And you need them built on standards that will outlast any single vendor's roadmap. That's not complexity. That's clarity. ⭕ What is a Knowledge Graph: https://lnkd.in/eFgDfjRQ

Vector search gave LLMs memory. Graph databases gave LLMs relationships. But neither could give LLMs real-time reasoning. That’s the next frontier. Because agents don't just need content — they need connected knowledge that they can reason over, instantly. And here’s where the traditional stack fails: Most graph databases still “walk” through data — one node, one edge, one hop at a time. Exactly like humans flipping pages in a directory. That works for analytics. It collapses for AI agents. The core idea: What if graphs stopped behaving like “maps”… and started behaving like “math”? That’s the FalkorDB breakthrough. Instead of hopping from node to node — FalkorDB converts the entire graph into a sparse matrix. Your data becomes a mathematical object. And once your graph is math — queries become math too. Not traversal. Not step-by-step. Just matrix computation using linear algebra. And math doesn’t walk. It computes. Which means: Real-time graph reasoning for agents. At scale. Why this changes the game for LLMs: Vector search tells you what is similar. Graphs tell you what is connected. But sparse matrix graphs tell you what is structurally meaningful — instantly. It’s the difference between finding a document… …and finding the truth inside a network of relationships. That's how agents will think. FalkorDB brings this into the real world: 🔹 Graphs as sparse matrices — zero traversal overhead 🔹 Linear algebra-powered queries — orders-of-magnitude faster 🔹 Redis-native, open-source, lightweight deployment 🔹 OpenCypher compatible — no need to learn a new language 🔹 Built specifically for LLM context, agent memory, and reasoning I tested it — queries that took seconds now feel like function calls. Agents that relied on retrieval now reason in real-time. The future isn't LLMs with bigger context windows. It’s LLMs with smarter knowledge structures. And frameworks like FalkorDB will power that shift. I’ve shared their GitHub link in the comments — explore it, run it, stress it. It feels like where Agent Memory is heading.

What’s the point of a massive context window if using over 5% of it causes the model to melt down? Bigger windows are great for demos. They crumble in production. When we stuff prompts with pages of maybe-relevant text and hope for the best, we pay in three ways: 1️⃣ Quality: attention gets diluted, and the model hedges, contradicts, or hallucinates. 2️⃣ Latency & cost: every extra token slows you down, and costs rise rapidly. 3️⃣ Governance: no provenance, no trust, no way to debug and resolve issues. A better approach is a knowledge graph + GraphRAG pipeline that feeds the model the most relevant data with context instead of all the things it might need with no top-level organization. ✅ How it works at a high level: Model your world: extract entities (people, products, accounts, APIs) and typed relationships (owns, depends on, complies with) from docs, code, tickets, CRM, and wikis. GraphRAG retrieval: traverse the graph to pull a minimal subgraph with facts, paths, and citations, directly tied to the question. Compact context, rich signal: summarize those nodes and edges with provenance, then prompt. The model reasons over structure instead of slogging through sludge. Closed loop: capture new facts from interactions and update the graph so the system gets sharper over time. ✅ A 30-day path to validate it for your use cases: Week 1: define a lightweight ontology for 10–15 core entities/relations built around a high-value workflow. Week 2: build extractors (rules + LLMs) and load into a graph store. Week 3: wire GraphRAG (graph traversal → summarization → prompt). Week 4: run head-to-head tasks against your current RAG; compare accuracy, tokens, latency, and provenance coverage. Large context windows drive cool headlines and demos. Knowledge graphs + GraphRAG work in production, even for customer-facing use cases.

Rethinking Vector Search: Beyond Nearest Neighbors with Semantic Compression and Graph-Augmented Retrieval Traditional vector databases rely on approximate nearest neighbor (ANN) search to retrieve the top-k closest vectors to a query. While effective for local relevance, this approach often yields semantically redundant results-missing the diversity and contextual richness required by modern AI applications like RAG systems and multi-hop QA. The Problem with Proximity-Based Retrieval: Current ANN methods prioritize geometric distance but don't explicitly account for semantic diversity or coverage. This leads to retrieval results clustered in a single dense region, often missing semantically related but spatially distant content. Enter Semantic Compression: Researchers from Carnegie Mellon University, Stanford University, Boston University, and LinkedIn have introduced a new retrieval paradigm that selects compact, representative vector sets capturing broader semantic structure. The approach formalizes retrieval as a submodular optimization problem, balancing coverage (how well selected vectors represent the semantic space) with diversity (promoting selection of semantically distinct items). Graph-Augmented Vector Retrieval: The paper proposes overlaying semantic graphs atop vector spaces using kNN connections, clustering relationships, or knowledge-based links. This enables multi-hop, context-aware search through techniques like Personalized PageRank, allowing discovery of semantically diverse but non-local results. How It Works Under the Hood: The system operates in two stages: first, standard ANN retrieval generates candidates, then a greedy optimization algorithm selects the final subset. For graph-augmented retrieval, relevance scores propagate through both vector similarity and graph connectivity using hybrid scoring that combines geometric proximity with graph-based influence. Real Impact: Experiments show graph-based methods with dense symbolic connections significantly outperform pure ANN retrieval in semantic diversity while maintaining high relevance. This addresses critical limitations in applications requiring broad semantic coverage rather than just local similarity. This work represents a fundamental shift toward meaning-centric vector search systems, emphasizing hybrid indexing and structured semantic retrieval for next-generation AI applications.

Enterprises today are drowning in multimodal data - text, images, audio, video, time-series, and more. Large multimodal LLMs promise to make sense of this, but in practice, embeddings alone often collapse nuance and context. You get fluency without grounding, answers without reasoning, “black boxes” where transparency matters most. That’s why the new IEEE paper “Building Multimodal Knowledge Graphs: Automation for Enterprise Integration” by Ritvik G, Joey Yip, Revathy Venkataramanan, and Dr. Amit Sheth really resonates with me. Instead of forcing LLMs to carry the entire cognitive burden, their framework shows how automated Multi Modal Knowledge Graphs (MMKGs) can bring structure, semantics, and provenance into the picture. What excites me most is the way the authors combine two forces that usually live apart. On one side, bottom-up context extraction - pulling meaning directly from raw multimodal data like text, images, and audio. On the other, top-down schema refinement - bringing in structure, rules, and enterprise-specific ontologies. Together, this creates a feedback loop between emergence and design: the graph learns from the data but also stays grounded in organizational needs. And this isn’t just theoretical elegance. In their Nourich case study, the framework shows how a food image, ingredient list, and dietary guidelines can be linked into a multimodal knowledge graph that actually reasons about whether a recipe is suitable for a diabetic vegetarian diet - and then suggests structured modifications. That’s enterprise relevance in action. To me, this signals a bigger shift: LLMs alone won’t carry enterprise AI into the future. The future is neurosymbolic, multimodal, and automated. Enterprises that invest in these hybrid architectures will unlock explainability, scale, and trust in ways current “all-LLM” strategies simply cannot. Link to the paper -> https://lnkd.in/gv93znbQ #KnowledgeGraphs #MultimodalAI #NeurosymbolicAI #EnterpriseAI #KnowledgeGraphLifecycle #MMKG #AIResearch #Automation #EnterpriseIntegration

This company is set to redefine the Agentic AI era and the insights will change how you think about relationships. AI only truly understands behavior when it understands how facts are connected and what happened before. That was one of the most important ideas in my conversation with Philip Rathle, CTO of Neo4j, on the #GenAITalks Podcast. Most data systems describe entities in isolation: A customer bought three products. An employee missed a deadline. A machine failed. A transaction was flagged. A knowledge graph adds the part that often determines the outcome: The relationships. Who influenced the customer? Which systems did the employee depend on? What supplier, component and maintenance event preceded the failure? Which accounts, devices and transactions belong to the same network? The individual node rarely tells the whole story. Behavior often emerges from the structure around it. And this becomes even more powerful when we add time. A graph can connect an outcome to the chain of antecedent events that came before it: A happened before B. B influenced C. C increased the probability of D. That does not automatically prove causality. As correlation can still hide inside a beautifully connected graph. But graphs give us something flat tables and similarity RAG searches struggle to provide: A visible, queryable model of the paths through which an outcome may have emerged. That changes what AI can do. Instead of only asking: “What information looks similar to this situation?” We can begin asking: “What network of relationships produced this situation and what sequence of events made it more likely?” That is the deeper promise of knowledge graphs. Philip described knowledge graphs as the missing layer between powerful language models and systems that can deliver more deterministic, explainable answers. For those who weren't able to attend the Agentic AI Summit at UC Berkeley, tune in here to get a flavor for what you missed. His broader work on GraphRAG similarly argues that graph structure can complement the statistical nature of LLM retrieval. Watch the full conversation with Philip Rathle on the podcast hosted by GenAI Works below: https://lnkd.in/gwxnk_VB #KnowledgeGraphs #GraphRAG #CausalAI #GenerativeAI #Neo4j

🚀 Why RAG alone won’t get us there—and how Agentic RAG helps I've used RAG systems in multiple products—especially in knowledge-heavy contexts. They help LLMs stay grounded by retrieving supporting documents. But there’s a point where they stop being useful. Let me give you a simple example. Let’s say you ask: 👉 “Which medical researchers have published on long COVID, what clinical trials they were part of, and what other conditions those trials studied?” A classical RAG system would: 1️⃣ Look for text chunks that match “long COVID” 2️⃣ Return some papers or abstracts 3️⃣ And leave the LLM to guess or hallucinate the rest And here is the problem? You're not just looking for one passage. You're asking for a chain of connected facts: 🔹 Authors → 🔹 Publications → 🔹 Clinical trials → 🔹 Other conditions RAG systems were never built to follow that trail. They do top-k lookup and feed static chunks to the LLM. No planning. No reasoning. No ability to explore relationships between entities. That’s where Agentic RAG with Knowledge Graphs comes in. Instead of dumping search results, the system: ✅ Breaks the question into steps ✅ Uses structured data to navigate relationships (e.g., author–trial–condition) ✅ Assembles the answer using small, verifiable hops ✅ Uses tools for hybrid search, graph queries, and concept mapping You can think of it like this: A classical RAG is like searching through a pile of papers with a highlighter and Agentic RAG is like giving the job to a smart analyst who understands the question, walks through your research database, and explains how each part connects. I am attaching a paper I read recently that demonstrated this well—they used a mix of Neo4j for knowledge graphs, vector stores for retrieval, and a lightweight LLM to orchestrate the steps. The key wasn’t the model size—it was the structure and reasoning behind it. I believe that this approach is far more suitable for domains where: 💠 Information lives across connected sources 💠 You need traceability 💠 And you can’t afford vague or partial answers I see this as a practical next step for research, healthcare, compliance, and enterprise decision-support. #AI #LLM #AgenticRAG #KnowledgeGraph #productthinking #structureddata I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

Agentic systems don't just benefit from Small Language Models. They architecturally require them, paired with knowledge graphs. Here's the technical reality most teams miss. 🎯 The Workload Mismatch Agents execute 60-80% repetitive tasks: intent classification, parameter extraction, tool coordination. These need <100ms latency at millions of daily requests. Physics doesn't negotiate. Model size determines speed. But agents still need complex reasoning capability. 🧠 The Graph Solution The breakthrough: separate knowledge storage from reasoning capability. LLMs store facts in parameters. Inefficient. Graph-augmented SLMs externalize knowledge to structured triples (entity-relationship-entity), use 3-7B parameters purely for reasoning. Knowledge Graph of Thoughts: Same SLM solves 2x more tasks when querying graphs vs. processing raw text. Cost drops from $187 to $5 per task. Multi-hop reasoning becomes graph traversal, not token generation. Token consumption drops 18-30%. Hallucination reduces through fact grounding. 💰 The Economics At 1B requests/year: GPT-5 approach: $190K+ 7B SLM + graph infrastructure: $1.5-19K One production system: $13M annual savings, 80%→94% coverage by caching knowledge as graph operations. ⚡ The Threshold Below 3B parameters: Models can't formulate effective graph queries Above 3B: Models excel at coordinating retrieval and synthesis over structured knowledge Modern 7B models (Qwen2.5, DeepSeek-R1-Distill, Phi-3) now outperform 30-70B models from 2023 on graph-based reasoning benchmarks. 🏗️ The Correct Architecture Production agents converge on this pattern: Query → Classifier SLM → Graph construction/update → Specialist SLMs query graph → Multi-hop traversal → Response synthesis → (5% escalate to LLM) The graph provides: External memory across reasoning steps Fact grounding to prevent hallucination Reasoning scaffold for complex inference 🔐 Why This Matters Edge deployment: 5GB graph + 7B model runs locally on laptops Privacy: Medical/financial data never leaves premises Latency: Graph queries are deterministic <50ms operations Updates: Modify graph triples without model retraining Real case: Clinical diagnostic agent on physician laptop. Patient symptoms → graph traversal → diagnosis in 80ms. Zero external transmission. 🎓 The Separation of Concerns Graphs handle: relationship queries, continuous updates, auditability SLMs handle: query formulation, reasoning coordination, synthesis LLMs conflate both functions in one monolith. This drives their size and cost. Agent tasks follow this pattern: understand intent → retrieve structured knowledge → reason over relationships → execute action → update knowledge state. Graphs make each step explicit. SLMs provide coordination intelligence. Together, they outperform larger models on unstructured data at 10-36x lower cost. Are you still processing agent tasks with 70B+ models on raw text, or have you separated knowledge (graphs) from reasoning (SLMs)?

Most enterprises have databases full of facts. But AI cannot create value from facts alone. It needs context, relationships, and meaning. That is where the difference between a data model and an ontology becomes important. A data model tells your systems how data should be structured, stored, validated, and queried. An ontology tells your systems what the data means, how concepts relate to each other, and what can be inferred from those relationships. Think of it like a library. A data model is the card catalog. It tells you where every book is, how it is classified, and how to retrieve it quickly. An ontology is the collective intelligence of the library. It understands topics, authors, themes, relationships, context, and how one idea connects to another. This distinction matters even more in the AI era. Traditional applications can work well with data models alone. They need structure, performance, rules, and integrity. But AI systems need more than clean tables. They need to reason. They need to connect concepts across systems. They need to answer semantic questions. They need to infer knowledge that was not explicitly stored in one row, column, or document. That is where ontology becomes powerful. A data model answers: “What data do we have and where is it stored?” An ontology helps answer: “What does this data mean and how is it connected?” This is why enterprises building serious AI systems cannot stop at schemas, pipelines, and dashboards. They need a semantic layer that connects business concepts, rules, relationships, and context. The best architecture is not data model vs ontology. It is data model plus ontology. Data models give you structure. Ontologies give you meaning. Together, they help break data silos, improve interoperability, support reasoning, and turn enterprise data into a knowledge engine for AI. In the age of AI, data without meaning is just expensive storage. Meaning is becoming the new infrastructure. Where do you think most enterprises are stuck today: managing data or understanding it? Follow Prashant R. for more such insights!! #EnterpriseAI #DataArchitecture #Ontology #KnowledgeGraph #GenAI #AIArchitecture

Introducing Microsoft Graph RAG: Enhancing AI's Ability to Summarize Large Text Corpora ... 👉A New Approach to Query-Focused Summarization based on Knowledge Graph Microsoft Researchers have introduced a novel approach called Graph RAG (Retrieval-Augmented Generation) that enhances the capabilities of large language models (LLMs) to answer complex questions over extensive text corpora. This innovative method combines the strengths of RAG systems and query-focused summarization (QFS) to handle "global questions" that require understanding entire datasets, such as identifying main themes within a large collection of documents. 👉 Overcoming Limitations of Traditional Methods Graph RAG addresses the limitations of traditional RAG systems, which struggle with questions that are not about retrieving specific information but rather summarizing broad concepts. It does so by creating a graph-based text index using an LLM, which then allows for the generation of comprehensive and diverse answers by summarizing information from closely-related entities within the graph. Here's a step-by-step example of how Graph RAG works: 1. The LLM builds a graph-based text index in two stages:   - Derive an entity knowledge graph from the source documents   - Pre-generate community summaries for groups of closely-related entities 2. Given a question, each community summary is used to generate a partial response 3. All partial responses are then summarized into a final response to the user 👉 Practical Applications Across Industries The implications of this research are significant for industries that rely on the analysis of large volumes of text data, such as: - Legal: Summarizing case law and identifying relevant precedents - Academic research: Synthesizing scientific papers and identifying research trends - Intelligence analysis: Extracting insights from diverse sources of intelligence data The ability to quickly summarize and extract themes from large datasets can greatly enhance decision-making processes and strategic planning in these fields and beyond. 👉 Benefits of Graph RAG Graph RAG offers several key benefits compared to traditional methods: - Scalability: Can handle much larger datasets than current LLMs - Efficiency: Generates comprehensive answers faster by leveraging graph structure - Diversity: Produces more diverse answers by considering multiple perspectives from related entities 👉 Shaping the Future of AI in Text Analysis As AI continues to advance, approaches like Graph RAG will play an increasingly important role in transforming data analysis across various industries. By enabling more efficient and effective summarization of large text corpora, Graph RAG and similar systems have the potential to unlock new insights and drive innovation in fields ranging from business to healthcare to scientific research.