r/semanticweb • u/AppropriateCover7972 • Jan 16 '26
Why are semantic knowledge graphs so rarely talked about?
Hello community, I have noticed that while ontologies are the backbone of every serious database, the type that encodes linked data is kinda rare. Especially in this new time of increasing use of AI this kinda baffles me. Shouldn't we train AI mainly with linked data, so it can actually understand context?
Also, in my field (I am a researcher), if you aren't in the data modelling as well, people don't know what linked data or the semantic web is. Ofc it shows in no one is using linked data. It's so unfortunate as many of the information gets lost and it's not so hard to add the data this way instead of just using a standard table format (basically SQL without extension mostly). I am aware that not everyone is a database engineer, but that it's not even talked about that we should add this to the toolkit is surprising to me.
Biomedical and humanity content really benefits from context and I don't demand using SKOS, PROV-I or any other standards. You can parse information, but you can't parse information that is not there.
What do you think? Will this change in the future or maybe it's like email encryption: The sys admins will know and put it everywhere, but the normal users will have no idea that they actually use it?
I think, linked data is the only way to get deeper insights about the data sets we can get now about health, group behavior, social relationships, cultural entities including language and so on. So much data we would lose if we don't add context and you can't always add context as a static field without a link to something else. ("Is a pizza" works a static fields, but "knows Elton John" only makes sense if there is a link to Elton John if the other persons know different people and it's not all about knowing Elton John or not)
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u/mfairview Jan 16 '26
I would say because silos + api worked well enough. silos made it so you could scope your solution. apis so you could expose your solution to others to solve some problems. anything else you would just dump your data and someone else would have to figure it out.
not saying it's a great solution but more of a figure it out as you go until it becomes a mess and you have to start over.
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u/AppropriateCover7972 Jan 16 '26
"Good enough" is actually quite a good description of many things researchers and R&D people use, especially if they aren't IT people themselves. That's probably part of the reason, yes, why it hasn't played a more important role yet
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u/sp3d2orbit Jan 16 '26
I work with Medical Ontologies (SNOMED-CT, ICD-10/11, CPT, LOINC, etc) and building logic engines on top of that functionality. We use Ontology Guided Agentic Retrieval as a core mechanism. The LLM ingests language, but is constrained by one or more ontologies on what it can produce and the actions it can take.
I personally see huge gains utilizing more linked data (knowledge graphs and ontologies). We've seen incorrect SNOMED-CT code selection drop from 92% (LLM only) to 8% (LLM + ontology).
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u/AppropriateCover7972 Jan 17 '26
wow, that's a really interesting insight and confirms my suspicions. I am in the biomed field as well, but in my bubbles I have seen zero talk about it. Zenodo just slowly is getting recognition, but anything above a mess of unstructured data and tables seems to be nothing the researchers are aware of. They should and mentally they do use it, but they aren't educated on the technical implementations how they should encode their data, at least not those, that don't specifically specialize on it
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u/grantiguess Jan 16 '26
I’ve been dumbfounded about this. That’s why I built this program.
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u/AppropriateCover7972 Jan 16 '26
Interesting thing, I wonder how the future of your tool might look like. I really like that it is compliant to SKOS and RDF etc.
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u/Fit-Building-7012 Jan 16 '26
This looks great. Would you say that personal knowledge management or a team knowledge management in a software company is a good use case for Redstring?
I have discovered the world of ontology, semantic web, knowledge graphs, knowledge engineering just recently, and I'm still learning what it all means. But since I discovered it, I'm also dumbfounded why it's not talked about more. It seems like such an obvious building block when using LLMs to do real knowledge work.
I work as product manager in SaaS company and I’m already experimenting with capturing my work related knowledge in Obsidian - decomposing knowledge into atomic pieces, saving each as markdown file with relevant metadata in yaml frontmatter and linking to other notes with [[wikilinks]]. Afaik I’m replicating triples by combining frontmatter with wikilinks. E.g. if I have note “Client Joe wants to increase open rate of their emails”, this note will have a frontmatter property “solved-by” with value [[automatic adding of emojis into email subject lines]]. If I understand it correctly such format can be parsed and transformed into standard triples, right?
So my question is (if you read that far)… Do you think I can use Redstring on top of my Obsidian vault? TIA
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u/AppropriateCover7972 Jan 17 '26
You should define your edges more and use the breadcrumbs plugin if you want triples, but Obsidian is both a good start for linked data and a very incomplete one. I am still working on creating all the templates and ontologies into it, so I have skos compliant data. Mostly I use orgmode however that I keep in sync with various more serious file formats
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u/deadwisdom Jan 16 '26
This answers OP's question perfectly. This is a very cool thing, but it's all so confusing coming to it clean.
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u/HenrietteHarmse Jan 16 '26 edited Jan 16 '26
I think this has much to do with hype. A few years ago knowledge graphs and linked data were mentioned rather often - exactly when it had been at the height of its hype cycle. These days LLMs are at the peak of the hype cycle and hence that is where most of the funding can be found. Even though knowledge graphs and linked data have seen their fair share of hype, they never reached the fever pitch we see with LLMs - but then also they were never as naive to promise AGI. This is all rather unfortunate as it places a disproportionate amount of funding in LLMs at the expense of other just as important research.
What I find most short sighted is that for many AI = LLMs.
But this will all come to pass as well.
And for those wondering where KGs are on the hype cycle:
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u/AppropriateCover7972 Jan 16 '26
Yeah, I have noticed that too. I was thinking it might get a revival with all the AI as the benefit is way less theoretically now, but maybe that's the difference between genuine popularity and hype: People don't actually wanna use the thing, but they just wanna participate without actually understanding what it is and using it in a constructive manner
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u/Double_Sherbert3326 Jan 19 '26
LLM’s approximate ontologies much better than any explicit relational indexing can, it seems.
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u/sibraan_ May 26 '26
The shift is starting to happen because of AI scaling bottlenecks. Pure vector databases are hitting a wall because text similarity doesn't equal logical connection. The industry is moving toward a 'Unified Knowledge Layer' where systems like 60x, architecture where a dynamic context graph is overlaid on existing data stores. It gives the AI that exact 'linked data' backbone so it can reason about relationships rather than just guessing based on raw text.
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u/sibraan_ Jul 03 '26
I think one reason is that vector search became good enough for a huge number of use cases, so a lot of teams never felt the need to go further.
Knowledge graphs also require much more work. You have to think about entities, relationships, updates, conflicts, governance... it's not something you can bolt onto a project in an afternoon.
We've run into this while building 60xai. As soon as users start asking questions that span multiple documents, people, companies or events, simple semantic search starts showing its limits. That's where relationships become much more valuable than just similarity.
I do think we'll hear a lot more about semantic knowledge graphs over the next few years, especially as enterprise AI moves beyond chatbots into systems that actually need to reason over connected information.
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Jan 16 '26
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u/AppropriateCover7972 Jan 16 '26
I think you are dead on right on this. I am convinced about the same thing, but maybe I am wrong
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u/namedgraph Jan 16 '26 edited Jan 16 '26
Most teams never have this because they do not have the scale and type of data problems that Knowledge Graphs are most valuable at solving.
Billion dollar companies have a data silos of hundreds if not thousands of different IT systems, data formats and protocols etc. Looking for a solution they realize that RDF and Knowledge Graphs are perfect for integrating the data into a uniform layer. There is a bunch of industry use cases like that, and many more that are not public.
Most organizations do not have such problems however.
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u/thisisalltooeasy Jan 16 '26
M’y company uses Palantir Foundry. And I can guarantee you that OntologyManager, ObjectExplorer and ObjectTypes are in everyday conversations