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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

Building a Knowledge Graph Is Hard.

You may have come across many #GraphRAG articles on how to go about building knowledge graphs — the patterns, the frameworks, the debates…

GLiNER2 extracts structured data without the LLM tax.

Was recently notified of MD-LD https://mdld.js.org/, a Markdown linked data parser in Javascript, that enables to create structured data while authoring MD. Same idea as RDFa for HTML, but in Markdown. It builds on my original blog post "semantic markdown" from 2020 : https://lnkd.in/ejwRqa5x So you write thinks like "## Meeting notes {=ex:Meeting_1 .schema:Event}" The very cool (and impressive) thing is the roundtrip : after the parser has extracted triples, and you modify the triples, you can regenerate the markdown file with updated structured annotations. Imagine : a lightweight solution to mix content-written-for-the-web with structured data. And could ask your AI to enrich your markdown with this syntax. And the structured data lives inside the content. And you can query your journal / notes / task lists in SPARQL. A pretty cool solution for personal knowledge graphs, or larger ones. This opens a whole new world of possibilities.

💻 Your website might be clear to you, but search engines and AI systems form their own interpretation. In the course AI Search and LLMs Entity SEO and Knowledge Graph Strategies for Brands for MLforSEO , I walk through how to verify what Google and AI models actually understand about your content. The analysis is based on Google Natural Language API and Knowledge Graph Search API, focusing on entity recognition and salience signals that influence AI citations and semantic visibility. If your brand or product is not clearly identified as a primary entity, your content is harder to surface in AI driven search experiences. This course explains how to test, diagnose, and correct that. 👉 The course is published at the following link and goes into the methodology step by step: https://lnkd.in/dvFiM8ct

What the heck does an ontology look like? How is it structured?

Discover how 2025 marked Neo4j's evolution from a graph database and analytics leader into the world’s premier graph intelligence platform.

Gaming giant teams with Deloitte to deploy a natural language query platform that uses LLM-powered agents and knowledge graphs to transform traditional user input-driven insight discovery.

A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j, TigerGraph). Features automatic PostgreSQL schema inference.

Implementing, Comparing, and Leveraging Neo4j, Zep, Cognee, Mem0, and Other Systems for Persistent, Explainable Intelligence

The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices

In defining what an information taxonomy is, it's useful to understand what a taxonomy is not: classification, navigation, glossary, or ontology.

“We know that we need a KG. We just don’t know why — and how.”

In this post I want to bring some clarity on the different ways in which "semantics" contributes to AI. By "semantics" I mean here the understanding and modeling of data, which in turn leads to the use of supporting artefacts such as ontologies and knowledge grapIn this post I want to bring some cla

Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a...

Let me start with a definition that differs from popular ones (e.g.

Lesson 7: You Need a Methodology

If you lead data or technology for a large enterprise, you have probably lived through the same sequence of data migrations. Data warehouses gave way to

A Review of Standard Ontologies That Exist for Decades

When building graphs, it’s tempting to reuse the same relationship type names across different parts of the model. But in both Neo4j and…