GraphNews

An Introduction to Semantic Model-Driven AI

Knwler — turn documents into a structured knowledge graph, in minutes. We built Knwler to solve a problem every compliance team, legal department, and research analyst knows too well: extracting structured insight from dense, complex documents takes a lot of manual work.

Geospatial data analysis systems are currently very relevant. Most such systems use either relational databases or graph databases. This paper presents the idea of using both approaches, taking into account the main features and advantages of each. A concrete example of a city transport network is used to experimentally examine the use of this hybrid approach. A special ETL procedure was developed to transform data from the corresponding graph database to a relational one, as well as the reverse process from the relational to the graph database. The results show which type of queries are better suited for relational databases, and which for graph databases. Additionally, for certain specific queries and applications, neither database type is capable of providing any results. Although this kind of hybrid architecture has issues with data duplication, the performance gains achieved are significant, making this approach highly efficient.

I am an AI realist, not a skeptic.

Bridging Semantics and Syntax with Annotation-Driven Generation

How an ontology-based knowledge graph boosts the multi-hop Q&A accuracy of one of the leading schemaless GraphRAG systems

Trust has stood out more than ever in the light of recent innovations. Some examples are advances in artificial intelligence that make machines more and more humanlike, and the introduction of...

I ran 30 context experiments to understand which piece of context has the most impact on analytics agents performance

A Graph Practitioner’s Perspective

From Property Graphs to OWL Inference Using a Hybrid Pipeline

Here is a very simple example of graph with AGE PostgreSQL extension. In this example, we will assume we are trying to match a donor who can potentially donate to a patient based on the matching blood group.

Why copying data for graph analytics doesn’t scale, and what you can do instead

2025 was a big year for Spanner on Google Cloud, with new capabilities to help it evolve from a passive data repository to an intelligent context hub.

The integration patterns that make LLMs more reliable and controllable

AI agents act under your authority. If you can’t trace their reasoning or enforce boundaries, you don’t have governance. You have risk. Here’s the evidence.

Misinformation spreads quickly—but fact-checking can happen just as fast.

High-performance GraphRAG inspired from LightRag written in Rust - raphaelmansuy/edgequake

Part 2 — Generative Knowledge Operators

How Terminology Pollution Is Killing Graph Architecture And Where GraphQL Actually Belongs

Thursday was eventful - I had 37 phone calls from recruiters looking for ontologists. One of them sent a questionnaire that I thought was brilliant, and I am reproducing here, because if you are an ontologist, you should probably be able to address these issues: 1. Have you designed or implemented a Knowledge Graph in an enterprise environment? (Yes + can describe use case = proceed) 2. Which semantic technologies have you worked with hands-on (RDF, OWL, SPARQL, SHACL)? (Should name at least 2 confidently) 3. Was your work more architecture-focused, hands-on, or hybrid? (Looking for senior ownership) Semantic Depth 4. Have you designed or managed ontologies or semantic models tied to business domains? 5. How did your Knowledge Graph integrate with relational or warehouse data? AI / RAG / KAG Exposure 6. Have you worked on platforms supporting LLMs, RAG, or AI systems using enterprise data? 7. Do you understand the difference between RAG and Knowledge-Augmented Generation (KAG)? (Conceptual understanding is acceptable) 8. Have you integrated Knowledge Graphs with embeddings or vector search? Enterprise & Governance 9. How did governance, lineage, or trust factor into your semantic or AI architecture? 10. Have you worked in regulated or large enterprise environments? Platform Alignment 11. What cloud platforms have you worked on? Any hands-on GCP data services? 12. Have you embedded semantic logic into data pipelines or ingestion flows? Seniority & Communication 13. Have you defined standards or reference architectures used by multiple teams? 14. How do you explain Knowledge Graphs to business stakeholders? | 42 comments on LinkedIn

Discover some of the best technical talks and videos from the NODES 2025 online conference organized by Neo4j and curated by Christian Miles from G.V().

The context graph will be the single most valuable asset for companies building and deploying AI systems.

Decision traces aren't enough for agent memory. Learn how world models and behavioral validation create AI that predicts outcomes. Build smarter agents.

Watching the Grok situation unfold has felt strangely familiar.

Coming Soon: [KNOWLEDGE GRAPH] 1 Industry, 5 Million+ Queries Problem 1: How do I learn ALL the topics in my industry? Solution: Structure 5.3M raw queries into a 5-level taxonomy → Categories → Subcategories → Intents → Topics → Keywords Problem 2: How do I use this data? Solution:  → Visualize & filter topics at scale → Track rankings vs competitors → Calculate market share % by topic Problem 3: How does this drive business impact? [Coming Soon] Solution: → Increase topical coverage with intent-based content → 3-4 year content & optimization roadmap → Internal linking recommendation engine Current Scale: • 5,331,768 keywords indexed • 913 L1 categories • 746 L2 subcategories   • 882 L3 intents • 1ms classification speed Productivity Impact: Manual: 1,000 keywords = 2 hours This system: 5.3M keywords = instant Select the topics YOU care about → Find opportunities → Start IMPLEMENTING #KnowledgeGraph #SEO #DataEngineering #Telecom #AI | 83 comments on LinkedIn