Uncertainty
Bitemporal annotations and degradation are not enough, there is so much we can’t know for sure, intrinsically or practically.
The prototypical example is “person X is an expert in topic Y”. You see this in skills graphs, talent intelligence platforms, research networks, customer success graphs. This type of triple (or edge) when used transitively, with downstream reasoning, graph ML…leads to absolute conclusions while this is in fact a prior in need of a probability distribution. Enter Bayesian networks (belief network, probabilistic graphical models).
There are many reasons why we don’t use Bayesian techniques with KGs. Well, in general really. It’s mathematical, it’s difficult, it adds yet another layer of abstraction on top of ontologies, there is no database (storage) support, the query languages do not propagate uncertainty automatically, RDF at its deepest level should include a factor with every triple (confidence level) and more. Scalability is a big issue. Computing would involve Monte Carlo simulations and require GPU processing to scale.
There used to be a BayesDB database on Github, but it did not get the success it deserved.
There is a market gap because the knowledge graphs being built today are not hand-curated encyclopedias. They are extracted from documents by LLMs, assembled from web scrapes, merged across conflicting sources. The uncertainty is not incidental, it is structural. An LLM-extracted triple and a manually verified one sit side by side in your graph, indistinguishable, treated with identical epistemic weight.
Not really a knowledge graph, more like a confidence laundry machine. What is actually needed is a native probabilistic graph store where confidence is a first-class citizen of the data model, not a property you bolt onto an edge and manage yourself. Where query semantics propagate belief through traversal rather than silently dropping it. Where new evidence updates a distribution, not just a value. Where you get a posterior back alongside your results, not just a ranked list you are expected to trust blindly.
There is a huge amount of research on this, the academic foundations are solid but the business world is not there yet. The database does not exist yet. The query language does not exist yet. The market does not know it needs this yet.
Curious when/if it will happen.
On a philosophical level, how does a brain handle so well uncertainty, what would it take to implement probabilistic neural AI? Claude Fable, now with uncertainty included? GPT answering with “I am not sure, but it’s very likely that…”. It’s how we, humans, answer things.