Sigh When the phrase "The King is dead, long life the King" is used, the two kings are different people; the one that just passed and the one that replaced him. If the King is replaced by a Queen then the phrase is "The King is dead, long live the Queen". This is not some life after death thing. You aren't saying the King will live on in the hearts and minds of the people, you're stating your support for the successo…
That original use of the phrasal template is still valid, but today’s common use is when X goes though some sort of transition, is “reborn” in a new form, or when people realize that something which has generally been presumed dead is still around.
If you think of “King” as a role, this modern use is not that different than the original use. The capital-K role of King didn’t die, but continues in another form.
The web is already semantic and machine-readable. The machine reads and interprets the HTML code and displays the semantic meaning of the page to the user.
If you want the machine to read the same meaning out that the human does, you need a smarter machine, not a different format.
The author of this post mentions the Humanities at the end of their post and TerminusDB. I work on a Humanities based project which uses the Semantic Web ( https://github.com/cyocum/irish-gen ) and I have looked at TerminusDB a couple of times. The main factor in my choice of technologies for my project was the ability to reason data from other data. OWL was the defining solution for my project. This is mainly becaus…
"Reasoning" capability can be added to any conventional database via the use of views, and sometimes custom indexes. The real problem is that it's computationally expensive for non-trivial cases.
As you put the word Reasoning in quotation marks, I might misunderstand your bottom line here (I am Autistic, so please do not get quirky on natural language semantics), but the bare statement: "Reasoning can be added to any conventional database" is just not right. Reasoning is a well-defined notion from logic, that is based on formal languages, semantics and a relation called entailment (inference in proof theory) respectively. None of that does natively exist in a database. In the literature, there are two well-known ways for integrating a notion of reasoning into a database. Firstly, Datalogic was invented to create recursive queries. Datalogic's relation to reasoning was a side-effect, and it only covers a fragment based on horn clauses. On the other hand there's OWL-DL a (limited) fragment of OWL, that encodes some kind of reasoning via query expansion on vanilla SQL-Queries. So maybe you can elaborate on the notion of "using views, and sometimes custom indices to add reasoning to a conventional database".
Semantic Web lost itself in fine details of machine-readable formats, but never solved the problem of getting correctly marked up data from humans. In the current web and apps people mostly produce information for other people, and this can work even with plain text. Documents may lack semantic markup, or may even have invalid markup, and have totally incorrect invisible metadata, and still be perfectly usable for hu…
Comparing "rigid formal grammar-based models" (whatever that might actually mean for now) to machine learning is like comparing apples to bananas. The former one is a rigorous syntactical formalization, aimed at being readable by machine and humans alike. The latter one is a learned interpolation of a probability distribution function. I do not see a single way to compare these two "things". Nevertheless, I may guess, what you actually are trying to say: Annotating data by hand (the syntax is completely irrelevant) is inferior to annotating data by machine learning. And this claim is at least debatable and domain-dependent. There are domains where even a 3% false-positive rate translates to "death of a human being in 3 out of 100 identified cases", and there are domains where it's to much work to formalize every bits and pieces of the domain and extracting (i.e. learning) knowledge is a feasible endeavor. I have experience in both fields, and I dare to say, that extracting concepts and relations out of text in a way that it can be further processed and used for some kind of decision process is way more complicated than you might imagine, and GPT-3 et al. do not achieve that.
I am surprised no one has mentioned schema.org. It is a much simpler standard and more widely used than RDF/OWL.
Another point I think is that it is not in any publishers interest to publish structured data, as it easily copy-able. For example, neither Amazon nor Wikipedia publishes using schema.org. It would make their data susceptible to 3rd party aggregators.
I love that a post on why we need the semantic web has a subheading titled “Key Innvoations”, because really the reason the semantic web died is because we need automated agents capable of dealing with the web as it is, not a web designed for automated agents.