Matchmaking with Knowledge Graphs
Most companies today are in the business of matchmaking. Not finding the significant other of your dreams, something just as important but very different.
But everywhere we go they do a God-awful job of it.
Matchmaking is the engine of a company's survival and growth – an essential function of the enterprise. When you can't match effectively, your company is hemorrhaging costs or missing opportunities for growth. Or both. Every. Single. Day.
But when we wander into the dark, grimy boiler rooms where search engines and ads targeting algorithms lurk – and where execs never tread – we see bleary-eyed teams struggling to keep clunky technologies from the last millennium up and running. Rusty, off-the-shelf, easy-to-deploy open source software. Leaky approaches long since abandoned by industry leaders.
This is why so many people are talking about knowledge graphs.
Small, agile, reliable graphs. Later on, broad and deep graphs. They make matching work in a scalable, intuitive way. Powered by facts. Built on understandable principles that we can troubleshoot and improve. Targeting not only scale and speed, but accuracy and transparency.
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To drive predictable business growth.
We're going to the Knowledge Graph Conference at Cornell Tech in New York City next week. You should, too.
Think of it as (almost) free consulting. A herd of experts who have been developing this technology for decades. Companies already in the middle of deploying it. Ask all the questions you want. You'll probably never see as much expertise and experience in one place ever again. Unless, of course, you come back next year.
Look for us there. We are hypergraf.ai
Mike Dillinger ( mike@hypergraf.ai ) -- speaking on Thursday morning
Thomas Mansur ( thomas@hypergraf.ai )
enterprise IA | knowledge graphs | practice democratization | community building | structured content and data | podcasting
2wSee you there, Mike!
Strategic Data Leader | Ontology-Based Governance & AI/ML Readiness | MBA | PhD Researcher
2wknowledge graphs without semantics aren't very useful.
Conscious AI l Prompt Engineer l AI Trend Watcher
2wKnowledge exists in different forms: – Fragmented knowledge stored in databases or texts – Organized knowledge using folders, tags, or search engines – Structured knowledge as knowledge graphs – And finally, knowledge embedded in LLMs (large language models) To overcome the limitations of each approach, we need to combine them. One of the most advanced setups today is LLMs integrated with knowledge graphs. Ideally, an LLM alone can satisfy many queries that don't require high precision. But in critical scenarios, knowledge graphs with predefined solutions are essential. However, as system complexity increases, even such combinations can fail—especially if the LLM identifies an alternative scenario the graph was not designed for. right?
Director of Industry Research @ Industry Knowledge Graph LLC | MBA Visit IndustryKG.com
2wSounds great, Mike. On a related topic that I incorrectly assumed by the title... maybe a future post (or KGC 2026 presentation) ==> Knowledge Graph Matchmaking. Theoretically RDF KGs should be combining all over the place, correct? Mike the Matchmaker has a nice ring to it, eh? And in the KG world, the more matchmaking the better.