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Comment by PeterStuer | Hacker News Reader

OpenCyc is the remaining evolution of Cyc, which was based on the idea that symbolic AI (knowledge graphs/Semanic Networks) would lead to AGI through scaling the knowledge base. You formalize the world so you can use logic to reason about it.

Later approaches of the same idea coning out of the academic Databases community reinvented this particular wheel with far better PR and branded it the Semantic Web with 'Ontologies' and RDF, OWL and its ilk.

While reasonable (pun intended) for small vertical domains, the approach has never made inroads in more broad general intelligence as IMHO it does not deal well with ambiguities, contradictions, multi level or perspective modeling and circular referential meaning in 'real world' reasoning and also tends to ignore agentive, transformative and temporal situatedness.

Its ideal seems to be a single thruth never changing model of the universe that is simply accepted by all.

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All true in general, but allow me to add that there has been work on incorporating probabilistic / "soft" reasoning into the Semantic Web / RDF / OWL world. For some time now there has been PR-OWL (Probabilistic OWL)[1], and the recent work on RDF* (RDF-STAR)[2][3] emphasizes its application in terms of being able to - among other things - do stuff like adding weights (confidence scores, fuzzy probabilities, what-have-you) to RDF assertions. So the need to pursue these paths is understood, although I suppose one can argue that progress has been slow and painstaking.

[1]: https://www.pr-owl.org/

[2]: https://www.w3.org/2021/12/rdf-star.html

[3]: https://w3c.github.io/rdf-star/UCR/rdf-star-ucr.html

One of the issues is probably that the weights in fuzzy or probabilistic relationships or properties are rather context-dependent, so they probably still more or less have the same problems as all general-purpose knowledge graphs: it's exceedingly difficult to explicitly model relationships so that they'd be both broadly generalizable and detailed enough to be useful for non-trivial reasoning.

I'm also not sure that fuzzy or probabilistic properties automatically translate into reasonable transitive properties or reasoning even if the individual weights are reasonable.

(Fuzzy logic is of course exactly about formal logic and reasoning in non-absolute terms, but the idea has been around for a long time and AFAIK largely superseded by probability.)

Not that I have any deeper idea about recent work in that area. I did a couple of years' stint in semantic web stuff back in the day, and weighted relationships were one of the obvious ideas for dealing with the rigidity of explicit relationships. They also came with obvious problems and at least back then my impression was that the idea wasn't actually as useful as it initially sounded.

But as I said, I haven't really been following the field in years, so there might have been some useful developments.

Cyc apparently addresses this issue with what are termed "microtheories" - in one theory something can be so, and in a different theory it can be not so: https://cyc.com/archives/glossary/microtheory/

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