The Knowledge Graph Talent Shortage: Why Companies Can't Find the Skills They Desperately Need In my previous posts, I showed how Google's Knowledge Graph gives them a major AI advantage (https://lnkd.in/d5ZpMYut), and how enterprises from IKEA to Siemens to AstraZeneca have been using knowledge graphs and now leverage them for GenAI applications (https://lnkd.in/dPhuUhFJ). But here's the problem: we don't have enough people who know how to build them. 📊 The numbers tell the story. Job boards show thousands of open positions globally for ontology engineers, semantic web developers, and knowledge graph specialists. Yet these positions remain unfilled for months. Salaries for this expertise are rising, and technology vendors report inbound client calls instead of chasing business. 🤔 Why the shortage? The semantic web emerged in the early 2000s with technologies like RDF, OWL, and SPARQL. A small group of pioneers built this expertise. I was part of that early wave. I contributed to the POSC Caesar Association oil and gas ontology, certified as ontology modeller and participated in the W3C workshop hosted by Chevron in Houston in 2008. Later I led the Integrated Operations in the High North (IOHN) program with 23 companies like ABB, Siemens, and Cisco to increase semantic web knowledge within Equinor's vendor ecosystem. After IOHN, I stepped away for over a decade. The Knowledge Graph Alliance (KGA) drew me back. Companies need people who can design ontologies, write SPARQL queries, map enterprise data to semantic standards, and integrate knowledge graphs with LLMs. These aren't skills you pick up in a weekend bootcamp. 🔄 What needs to change? Universities must integrate semantic knowledge graphs into core curriculum alongside AI and machine learning as requirements, not electives. Here's something many don't realize: philosophy matters. Some of the best ontologists have philosophy degrees. Understanding how to represent knowledge requires training in logic and formal reasoning. DAMA International®'s Data Management Body of Knowledge covers 11 knowledge areas, but knowledge graphs remain absent. This would legitimize the discipline. Industry-academia bridges are critical. Organizations like the KGA bring together industry leaders with research organizations and academia. We need more such collaborations. 💡 The opportunity: If you're a data engineer or data scientist looking for a career differentiator, semantic web skills are your ticket. 🎯 The bottom line: Knowledge graphs aren't optional for industrial-scale GenAI. But you need the people who understand them. While reports document tech talent shortages, the semantic web skills gap remains largely undocumented as companies struggle to fill thousands of positions. What's your experience with the shortage? Are you hiring? Upskilling? Teaching this? #KnowledgeGraphs #SemanticWeb #AI #GenAI #TalentShortage #SkillsGap #Ontology #DataScience #Philosophy #DigitalTransformation | 29 comments on LinkedIn

💡 Graph of Ideas -- LLMs paired with knowledge graphs can be great partners for ideation, exploration, and research. We've all seen the classic detective corkboard, with pinned notes and pictures, all strung together with red twine. 🕵️  The digital version could be a mind-map, but you still have to draw everything by hand. What if you could just build one from a giant pile of documents? Enter GoAI - a fascinating approach that just dropped on arXiv combining knowledge graphs with LLMs for AI research idea generation. While the paper focuses on a graph of research papers, the approach is generalizable. Here's what caught my attention: 🔗 It builds knowledge graphs from AI papers where nodes are papers/concepts and edges capture semantic citation relationships - basically mapping how ideas actually connect and build on each other 🎯 The "Idea Studio" feature gives you feedback on innovation, clarity, and feasibility of your research ideas - like having a research mentor in your pocket 📈 Experiments show it helps produce clearer, more novel, and more impactful research ideas compared to traditional LLM approaches The key insight? Current LLMs miss the semantic structure and prerequisite relationships in academic knowledge. This framework bridges that gap by making the connections explicit. As AI research accelerates, this approach can be be used for any situation where you're looking for what's missing, rather than answering a question about what exists. Read all the details in the paper... https://lnkd.in/ekGtCx9T