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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

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/ https://www.pr-owl.org/ [2]: https://www.w3.org/2021/12/rdf-star.html https://www.w3.org/2021/12/rdf-star.html [3]: https://w3c.github.io/rdf-star/UCR/rdf-star-ucr.html https://w3c.github.io/rdf-star/UCR/rdf-star-ucr.html

Delk 2y ago
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about formal logic and reasoning in non-absolute terms, but the idea has been around for a long time and AFAIK largely superseded by probability.)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.

Delk 2y ago

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.