The semantic web is now widely adopted
csvbase.com
csvbase.com
The lack of adoption has, imho, two components.
1. bad luck: the Web got worse, a lot worse. There hasn't been a Wikipedia-like event for many decades. This was not pre-ordained. Bad stuff happens to societies when they don't pay attention. In a parallel universe where the good Web won, the semantic path would have been much more traveled and developed.
2. incompleteness of vision: if you dig to their nuclear core, semantic apps offer things like SPARQL queries and reasoners. Great, these functionalities are both unique and have definite utility but there is a reason (pun) that the excellent Protege project [1] is not the new spreadsheet. The calculus of cognitive cost versus tangible benefit to the average user is not favorable. One thing that is missing are abstractions that will help bridge that divide.
Still, if we aspire to a better Web, the semantic web direction (if not current state) is our friend. The original visionaries of the semantic web where not out of their mind, they just did not account for the complex socio-economics of digital technology adoption.
Mapping the incredible success of The Web onto automated systems hasn't worked because the defining and unique characteristic of The Web is REST and, in particular, the uniform interface of REST. This uniform interface is wasted on non-intentional beings like software (that I'm aware of):
https://intercoolerjs.org/2016/05/08/hatoeas-is-for-humans.h...
Maybe this all changes when AI takes over, but AI seems to do fine without us defining ontologies, etc.
It just hasn't worked out the way that people expected, and that's OK.
If you say "AI" in 2024, you are probably talking about an LLM. An LLM is a program that pretends to solve semantics by actually entirely avoiding semantics. You feed an LLM a semantically meaningful input, and it will generate a statistically meaningful output that just so happens to look like a semantically meaningful transformation. Just to really sell this facade, we go around calling this program a "transformer" and a "language model", even though it truthfully does nothing of the sort.
The entire goal of the semantic web was to dodge the exact same problem: ambiguous semantics. By asking everyone to rewrite their content as an ontology, you compel the writer to transform the semantics of their content into explicit unambiguous logic.
That's where the category error comes in: the writer can't do it. Interesting content can't just be trivially rewritten as a simple universally-compatible ontology that is actually rooted in meaningfully unambiguous axioms. That's precisely the hard problem we were trying to dodge in the first place!
So the writer does the next best thing: they write an ontology that isn't rooted. There are no really useful axioms at the root of this tree, but it's a tree, and that's good enough. Right?
What use is an ontology when it isn't rooted in useful axioms? Instead of dodging the problem of ambiguous semantics, the "semantic web" moves that problem right in front of the user. That's probably useful for something, just not what the user is expecting it to be useful for.
---
I have this big abstract idea I've been working on that might actually solve the problem of ambiguous semantics. The trouble is, I've been having a really hard time tying the idea itself down to reality. It's a deceptively challenging problem space.
First of all, what's the problem? Computing human-written text.
What's the problem domain? Story. In other words: intentionally written text. By that, I mean text that was written to express some arbitrary meaning. This is smaller than the set of all possible written text, because no one intentionally writes anything that is exclusively nonsensical.
So what's my solution? I call it the Story Empathizer.
---
Every time someone writes text, they encode meaning into it. This even happens on accident: try to write something completely random, and there will always be a reason guiding your result. I call this the original Backstory. This original Backstory contains all of the information that is not written down. It's gone forever, lost to history. What if we could dig it up?
Backstory is a powerful tool. To see why, let's consider one of the most frustratingly powerful features of Story: ambiguity. In order to express a unique idea in Story, you don't need an equivalently unique expression! You can write a Story that literally already means some other specific thing, yet somehow your unique meaning still fits! Doesn't that break some mathematical law of compression? We do this all day every day, so there must be something that makes it possible. That thing is Backstory. We are full of them. In a sense, we are even made of them.
We can never get the original Backstory back, but we can do the next best thing: make a new one. How? By reading Story. When we successfully read a Story, we transform it into a new Backstory. That goes somewhere in the brain. We call it knowledge. We call it memory. We call it worldview. I call this process Empathy.
Empathy is a two way street. We can use it to read, and we can use it to write. When two people communicate, they each create their own contextual Backstory. The goal is to make the two Backstories match.
---
So how do we do it with a computer? This is the tricky part. First, we need some fundamental Backstories to read with, and a program that uses Backstory to read. Then we should be able to put them to work, and recursively build something useful.
I envision a diverse library of Backstories. Once we have that, the hardest part will be choosing which Backstory to use, and why. Backstories provide utility, but they come with assumptions. Enough meta-reading, and we should be able to organize this library well enough. The simple ability to choose what assumptions we are computing with will be incredibly useful.
---
So that's all I've got so far. Every time I try to write a real program, my surroundings take over. Software engineering is fraught with assumptions. It's very difficult to set aside the canonical ways that software is made, and those are precisely what I'm trying to reinvent. I'm getting tripped up by the very problem I intend to solve, and the irony is not lost on me.
Any help or insight would be greatly appreciated. I know this idea is pretty out there, but if it works, it will solve NLP, and factor out all software incompatibility.
Hard agree.
> Maybe this all changes when AI takes over, but AI seems to do fine without us defining ontologies, etc.
I think about it as:
- Hypermedia controls were been deemphasized, leading to a ton of workarounds to REST
- REST is a perfectly suitable interface for AI Agents, especially to audit for governance
- AI is well suited to the task of mapping the web as it exists today to REST
- AI is well suited to mapping this layout ontologically
The semantic web is less interesting than what is traversable and actionable via REST, which may expose some higher level, reusable structures.
The first thing I can think of is `User` as a PKI type structure that allows us to build things that are more actionable for agents while still allowing humans to grok what they're authorized to.
Please tell me you're not an eliminativist. There is nothing respectable about eliminativism. Self-refuting, and Procrustean in its methodology, denying observation it cannot explain or reconcile. Eliminativism is what you get when a materialist refuses or is unable to revise his worldview despite the crushing weight of contradiction and incoherence. It is obstinate ideology.
https://en.wikipedia.org/wiki/Eliminative_materialism
> Eliminative materialism (also called eliminativism) is a materialist position in the philosophy of mind. It is the idea that the majority of mental states in folk psychology do not exist. Some supporters of eliminativism argue that no coherent neural basis will be found for many everyday psychological concepts such as belief or desire, since they are poorly defined. The argument is that psychological concepts of behavior and experience should be judged by how well they reduce to the biological level. Other versions entail the nonexistence of conscious mental states such as pain and visual perceptions.
Funny, because eliminativism to me is the inevitable conclusion that follows from the requirement of logical consistency + the crushing weight of objective evidence when pitted against my personal perceptions.
I believe in semantic web. The biggest problem is that, due to lack of tooling and ease of use, it take alot of effort and time to see value in building something like that across various parties etc. You dont see the value right away.
It starts with the lack of a common terminology. For tool A a "booking" might be a reservation e.g. of a dock at a warehouse. For tool B the same word means a movement of goods between two accounts.
In terms of data integration things have gotten A LOT worse since EDIFACT is de facto deprecated. Every carrier in the parcel business is cooking their own API, but with insufficient means. I've come across things like Polish endpoint names/error messages or country organisations of big Parcel couriers using different APIs.
IMHO the EU has to step in here because integration costs skyrocket. They forced cellphone manufacturers to use USB-Cs for charging, why can't they force carriers to use a common API?
The community has its head in the sands about... just about everything.
Document databases and SQL are popular because all of the affordances around "records". That is, instead of deleting, inserting, and updating facts you get primitives that let you update records in a transaction even if you don't explicitly use transactions.
It's very possible to define rules that will cut out a small piece of a graph that defines an individual "record" pertaining to some "subject" in the world even when blank nodes are in use. I've done it. You would go 3-4 years into your PhD and probably not find it in the literature, not get told about it by your prof, or your other grad students. (boy I went through the phase where I discovered most semantic web academics couldn't write hard SPARQL queries or do anything interesting with OWL)
Meanwhile people who take a bootcamp can be productive with SQL in just a few days because SQL was developed long ago to give the run-of-the-mill developer superpowers. (imagine how lost people were trying to develop airline reservation systems in the 1960s!)
Often that may require some web scale data, like Pagerank but also any other authority/trust metric where you can say "this data is probably quality data".
A rather basic example, published/last modified dates. It's well known in SEO circles at least in the recent past that changing them is useful to rank in Google, because Google prefers fresh content. Unless you're Google or have a less than trivial way of measuring page changes, the data may be less than trustworthy.
You don't see many geocities style sites nowadays, even though there's many older sites with quality (and original) content. Maybe mobile friendliness plays into that though.
I think that Web Browsers need to change what they are. They need to be able to understand content, correlate it, and distribute it. If a Browser sees itself not as a consuming app, but as a _contributing_ and _seeding_ app, it could influence the semantic web pretty quickly, and make it much more awesome.
Beaker Browser came pretty close to that idea (but it was abandoned, too).
Humans won't give a damn about hand-written semantic code, so you need to make the tools better that produce that code.
Off the top of head...
OpenStreetMap was in 2004. Mastodon and the associated spec-thingy was around 2016. One/two decades is not the same as many decades.
Oh, and what about asm.js? Sure, archive.org is many decades old. But suddenly I'm using it to play every retro game under the sun on my browser. And we can try out a lot of FOSS software in the browser without installing things. Didn't someone post a blog to explain X11 where the examples were running a javascript implementation of the X window system?
Seems to me the entire web-o-sphere leveled up over the past decade. I mean, it's so good in fact that I can run an LLM clientside in the browser. (Granted, it's probably trained in part on your public musing that the web is worse.)
And all this while still rendering Berkshire Hathaway website correctly for many decades. How many times would the Gnome devs have broken it by now? How many upgrades would Apple have forced an "iweb" upgrade in that time?
Edit: typo
The technical capability of the browser to be an OS within an OS is more than proven by now, but not sure I am impressed with the utility thus far.
At the same time even basic features in the "right direction", empowering the users information processing ability (bookmarks, rss, etc) have stagnated or regressed.
An interesting read in itself, and also points to Cory Doctorow giving seven reasons why the Semantic Web will never work: https://people.well.com/user/doctorow/metacrap.htm. They are all good reasons and are unfortunately still valid (although one of his observations towards the end of the text has turned out to be comically wrong, I'll let you read what it is)
Your comment and the two above links point to the same conclusion: again and again, Worse is Better (https://en.wikipedia.org/wiki/Worse_is_better)
Indeed a good read, thanks for the link!
> [Cory Doctorow's] seven insurmountable obstacles
I think his context is the narrower "Web of individuals" where many of his seven challenges are real (and ongoing).
The elephant in the digital room is the "Web of organizations", whether that is companies, the public sector, civil society etc. If you revisit his objections in that light they are less true or even relevant. E.g.,
> People lie
Yes. But public companies are increasingly reporting online their audited financials via standards like iXBRL and prescribed taxonomies. Increasingly they need to report environmental impact etc. I mentioned in another comment common EU public procurement ontologies. Think also the millions of education and medical institutions and their online content. In institutional context lies do happen, but at a slightly deeper level :-)
> People are lazy
This only raises the stakes. As somebody mentioned already, the cost of navigating random API's is high. The reason we still talk about the semantic web despite decades of no-show is precisely the persistent need to overcome this friction.
> People are stupid
We are who we are individually, but again this ignores the collective intelligence of groups. Besides the hordes of helpless individuals and a handful of "big techs"(=the random entities that figured out digital technology ahead of others) there is a vast universe of interests. They are not stupid but there is a learning curve. For the vast part of society the so-called digital transformation is only at its beginning.
It's also very important to think in macro systems and societies, as you point out, rather than at the individual level
Another problem is that it's always ignored the basic requirements of most applications like:
1. Getting the list of authors in a publication as refernces to authority records in the right order (Dublin Core makes the 1970 MARC standard look like something from the Starship Enterprise)
2. Updating a data record reliably and transactionally
3. Efficiently unioning graphs for inference so you can combine a domain database with a few database records relevant to a problem + a schema easily
4. Inference involving arithemtic (Godel warned you about first-order logic plus arithmetic but for boring fields like finance, business, logistics that is the lingua franca, OWL comes across as too heavyweight but completely deficient at the same time and nobody wants to talk about it)
things like that. Try to build an application and you have to invent a lot of that stuff. You have the tools to do it and it's not that hard if you understand the math inside and out but if you don't oh boy.
If RDF got a few more features it would catch up with where JSON-based tools like
https://www.couchbase.com/products/n1ql/
were 10 years ago.
People can’t get HTML right for basic accessibility, so something like the semantic web would be super science that people will out of their way to intentionally ignore any profit upon so long as they can raise their laziness and class-action lawsuit liability.
Not only has this gotten much worse; even when you put in the stop gaps for developers such as linters or other plugins, they willfully ignore them and will actually implement code they know is determinantal to accessibility.
If it really works for my company and it is a competitive advantage I would keep quiet about it and I know of more than one company that's done exactly that. The standards process is so exhausting and you have to fight with so many systems programmers who never wrote an application that it's just suicide to go down that road.
BTW, RSS is an RDF application that nobody knows about
https://web.resource.org/rss/1.0/spec
you can totally parse RSS feeds with a RDF-XML parser and do SPARQL and other things with them.
I'll give you two examples: Internet Archive. Let's Encrypt.
There's also OpenStreetMap, exactly two decades old and thus four years younger than Wikipedia.
The world wide web (but not the internet) is only 3 decades old!
RSS and Atom were semantic web formats. They had a ton of applications built to publish and consume them, and people found the formats incredibly useful.
The idea was that if you ran into ingestible semantic content, your browser, a plugin, or another application could use that data in a specialized way. It worked because it was a standardized and portable data layer as opposed to a soup of meaningless HTML tags.
There were ideas for a distributed P2P social network built on the semantic web, standardized ways to write articles and blog posts, and much more.
If that had caught on, we might have saved ourselves a lot of trouble continually reinventing the wheel. And perhaps we would be in a world without walled gardens.
As what you have done is spend many years generating a shared understanding of what that ontology means between the experts. Once that's done you have the much harder task for pushing that shared understanding to the rest of the world.
ie the problem isn't defining a tag for a cat - it's having a global share vision of what a cat is.
I mean we can't even agree on what is a man or a women.
Developing such linking tools between ontologies would be worthwhile if there are multiple ontologies covering the same domain, provided they are actually used (i.e., there are large datasets for each). Alas, instead of a bottom-up, organic approach people try to solve this with top-down, formal (upper-level) ontologies [1] and Leibnizian dreams of an underlying universality [2], which only adds to the cognitive load.
[1] https://en.wikipedia.org/wiki/Formal_ontology
[2] https://en.wikipedia.org/wiki/Characteristica_universalis
In our spoken language the agents doing the parsing are human AI's (actual intelligences) able to deal with most of the finer nuances in semantics, and still making numerous errors in many contexts that lead to misunderstanding, i.e. parse errors.
There was this hand-waving promise in semantic web movement of "if only we make everything machine-readable, then .." magic would happen. Undoubtedly unlocking numerous killer apps, if only we had these (increasingly complex) linked data standards and related tools to define and parse 'universal meaning'.
An overreach, imho. Semantic web was always overpromising yet underdelivering. There may be new use cases in combinations of SM with ML/LLM but I don't think they'll be a vNext of the web anytime soon.
People sort of try. A concrete example are the Activitypub/Fediverse standards which dared to use json-ld. To my knowledge so far the social media experience of mastodon and friends is not qualitatively different from the old web stuff.
Just make LLMs more ubiquitous and train them on the Web. Rather than crawling or something. The LLMs are a lot more resilient.
"Googlers, if you're reading this, JSON-LD could have the same level of public awareness as RSS if only you could release, and then shut down, some kind of app or service in this area. Please, for the good of the web: consider it."
[1] https://github.com/schemaorg/suggestions-questions-brainstor...
OWL is a modeling language to describe ontologies, e.g. some constraints people have agreed to follow about how to structure the information they publish in graphs. It can also be considered an advanced schema language.
The idea of a bounded context in DDD is that it is not a good use of time (or indeed may not be feasible at all) to get a single ontology for an entire domain, so different subdomains may be unified by some concepts but have differing or overlapping concepts that they use internally. Two contexts know they are talking about a product called "New Shimmer", even if understands it as a floor wax and the other uses it as a dessert topping.
The two pillars of the semantic web are public data and machine understanding, which IMHO pushes strongly toward the (often unachievable) goal of a single kitchen-sink schema.
When I started to use LLMs I thought that was the missing link to convert content to semantic representations, even taking into account the errors/hallucinations within them.
The idea being that everyone have their own ontology for the data they release and the system would make a consolidated ontology that could be used to automatic integration of data from different datasources.
regardless, that project did not get traction, so now it sits.
Many ontologies have a "poem" type (for example dbpedia (https://dbpedia.org/ontology/Poem) has one), as well as other publishing or book-oriented ontologies.
It's true that they are less commonly embedded as semantic data in web pages. There's a real bootstrapping problem there: no reason to embed data if no tools will read it, no reason to build tools if there's no data to read.
The real question is whether the average publisher is better than an LLM at accurately classifying their content. My guess is, when it comes to categorization and summarization, an LLM is going to handily win. An easy test is: are publishers experts on topics they talk about? The truth of the internet is no, they're not usually.
The entire world of SEO hacks, blogspam, etc exists because publishers were the only source of truth that the search engine used to determine meaning and quality, which has created all the sorts of misaligned incentives that we've lived with for the past 25 years. At best there are some things publishers can provide as guidance for an LLM, social card, etc, but it can't be the only truth of the content.
Perhaps we will only really reach the promise of 'the semantic web' when we've adequately overcome the principal-agent problem of who gets to define the meaning of things on the web. My sense is that requires classifiers that are controlled by users.
https://www.heise.de/en/news/Copilot-turns-a-court-reporter-...
My point though was that the core problem we should be trying to solve is overcoming the fundamental misalignment of incentives between publisher and reader, not whether we can put a better schema together that we hope people adopt intelligently & non-adversarially, because we know that won't happen in practice. I liked what the author wrote but they also didn't really consider this perspective and as such I think they haven't hit upon a fundamental understanding of the problem.
What do you think this sort of observation is worth?
Some people appreciate being shown fascinating aspects of human nature. Some people don't, and I wonder why they're on a forum dedicated to curiosity and discussion. And then, some people get weirdly aggressive if they're shown something that doesn't quite fit in their worldview. This topic in particular seems to draw those out, and it's fascinating to me.
Myself, I thought it was great to learn about spontaneous trait association, because it explains so much weird human behavior. The fact that LLMs do something so similar is, at the very least, an interesting parallel.
LLMs are not experts either. Furthermore, from what I gather, LLMs are trained on:
>The entire world of SEO hacks, blogspam, etc
LLMs are not that great at understanding semantics though
The concept of OWL and the other standards was to annotate the content of pages, that's where the real values lie. Each paragraph the author wrote should have had some metadata about its topic. At the very least, the article metadata was supposed to have included information about the categories of information included in the article.
Having a bit of info on the author, title (redundant, as HTML already has a tag for that), picture, and publication date is almost completely irrelevant for the kinds of things Web 3.0 was supposed to be.
1. Trust: How should one know that any data available marked up according to Sematic Web principles can be trusted? This is an even more pressing question when the data is free. Sir Berners-Lee (AKA "TimBL") designed the Semantic Web in a way that makes "trust" a component, when in truth it is an emergent relation between a well-designed system and its users (my own definition).
2. Lack of Incentives: There is no way to get paid for uploading content that is financially very valuable. I know many financial companies that would like to offer their data in a "Semantic Web" form, but they cannot, because they would not get compensated, and their existence depends on selling that data; some even use Semantic Web standards for internal-only sharing.
3. A lot of SW stuff is either boilerplate or re-discovered formal logic from the 1970s. I read lots of papers that propose some "ontology" but no application that needs it.
Note that `title` isn't one of the properties that BlogPosting supports. It supports `headline`, which may well be different from the `<title/>`. It's probably analogous to the page's `<h1/>`, but more reliable.
A very bad example if the intention was to demonstrate how cool and useful semweb is :-)
"We've achieved victory! After over 25 years, if you want to know who wrote a blog post, you can get it from a few sites this way!"
I'd call it damning with faint success, except it really isn't even success. Relative to the promises of "Semantic Web" it's simply a failure. And it's not like Semantic Web was overpromised a bit, but there were good ideas there and the reality is perhaps more prosaic but also useful. No, it's just useless. It failed, and LLMs will be the complete death of it.
The "Semantic Web" is not the idea that the web contains "semantics" and someday we'll have access to them. That the web has information on it is not the solution statement, it's the problem statement. The semantic web is the idea that all this information on the web will be organized, by the owners of the information, voluntarily, and correctly, into a big cross-site Knowledge Graph that can be queried by anybody. To the point that visiting Wikipedia behind the scenes would not be a big chunk of formatted text, but a download of "facts" embedded in tuples in RDF and the screen you read as a human a rendered result of that, where Wikipedia doesn't just use self-hosted data but could grab "the Knowledge Graph" and directly embed other RDF information from the US government or companies or universities. Compare this dream to reality and you can see it doesn't even resemble reality.
Nobody was sitting around twenty years ago going "oh, wow, if we really work at this for 20 years some people might annotate their web blogs with their author and people might be able to write bespoke code to query it, sometimes, if we achieve this it will have all been worth it". The idea is precisely that such an act would be so mundane as to not be something you would think of calling out, just as I don't wax poetic about the <b> tag in HTML being something that changes the world every day. That it would not be something "possible" but that it would be something your browser is automatically doing behind the scenes, along with the other vast amount of RDF-driven stuff it is constantly doing for you all the time. The very fact that someone thinks something so trivial is worth calling out is proof that the idea has utterly failed.
I'll also add that I wouldn't even call what he's showing "semantic web", even in this limited form. I would bet that most of the people who add that metadata to their pages view it instead as "implenting the nice sharing link API". The fact that Facebook, Twitter and others decided to converge on JSON-LD with a schema.org schema as the API is mostly an accident of history, rather than someone mining the Knowledge Graph for useful info.
The really neat part is when you start considering universal ontologies and linking to resources published on other domains. This is where your data becomes interoperable and reusable. Even better, through linking you can contextualize and enrich your data. Since linked data is all about creating graphs, creating a link in your data, or publishing data under a specific domain are acts that involves concepts like trust, authority, authenticity and so on. All those murky social concepts that define what we consider more or less objective truths.
LLM's won't replace the semantic web, nor vice versa. They are complementary to each other. Linked data technologies allow humans to cooperate and evolve domain models with a salience and flexibility which wasn't previously possible behind the walls and moats of discrete digital servers or physical buildings. LLM's work because they are based on large sets of ground truths, but those sets are always limited which makes inferring new knowledge and asserting its truthiness independent from human intervention next to impossible. LLM's may help us to expand linked data graphs, and linked data graphs fashioned by humans may help improve LLM's.
Creating a juxtaposition between both? Well, that's basically comparing apples against pears. They are two different things.
https://www.meridiandiscovery.com/articles/pdf-forensic-anal...
Instead of using JSON-LD it uses RDF written as XML. Still uses the same concept of common vocabularies, but instead of schema.org it uses a collection of various vocabularies including Dublin Core.
1: LLMs "routinely get stuff wrong"
2: "pricy GPU time"
1: I make a lot of tests on how well LLMs get categorization and data extraction right or wrong for my Product Chart (https://www.productchart.com) project. And they get pretty hard stuff right 99% of the time already. This will only improve.
2: Loading the frontpage of Reddit takes hundreds of http requests, parses megabytes of text, image and JavaScript code. In the past, this would have been seen as an impossible task to just show some links to articles. In the near future, nobody will see passing a text through an LLM as a noteworthy amount of compute anymore.
The alleged low error rate of 1% can ruin your day/life/company, if it hits the wrong person, regards the wrong problem, etc. And that risk is not adequately addressed by hand-waving and pointing people to low error rates. In fact, if anything such claims would make me less confident in your product.
1% error is still a lot if they are the wrong kind of error in the wrong kind of situation. Especially if in that 1% of cases the system is not just slightly wrong, but catastrophically mind-bogglingly wrong.
(See also why automated face recognition in public surveillance cameras might be a bad idea.)
A human might fail to recognize another person in a photo, but at least they won't insist the person is definitely a cartoon character, or blindly follow "I am John Doe" written on someone's cheek in pen.
In reality most of the people are there during the day (false alarm every 10 seconds) and the error percentages are nowhere near 1%.
If you do the math to figure out the staff needed to react to those false alarms in any meaningful way you have to come to the conclusion that just putting people there instead of cameras would be a safer way to reach the goal.
If you're about to publish a career-ending allegation, you're going to spend some extra time fact-checking it.
Hypothetical example: Cops shoot the wrong person in x% of cases. If we equipped all surveillance cameras with guns that also shoot the wrong person in x% of cases the world would be a nightmare pandemonium, simply because there is more cameras and they are running 24/7.
Mind that the precise value of x and whether is constant or not does not impact the argument at all.
I'm also making the point that a human with an error rate of x% is not directly comparable to a machine with x% error rate, just via a different line of reasoning.
Yes, and I hate it. I closed Reddit many times because the wait time wasn't worth it.
These instances seem to be temporary bugs, but they show that it isn't getting any love (why would it? they only maintain it at all under sufferance) so at some point it'll no doubt be cut off as a cost cutting exercise during a time when ad revenue is low.
In fact, what you're doing there is building a local semantic database by automatically mining metadata using LLM. The searching part is entirely based on the metadata you gathered, so the GP's point 1 is still perfectly valid.
> In the near future, nobody will see passing a text through an LLM as a noteworthy amount of compute anymore.
Even with all that technological power, LLMs won't replace most simple-searching-over-index, as they are bad at adapting to ever changing datasets. They only can make it easier.
Critically, if the LLM gets something wrong, a user can notice and flag it, then someone can manually fix it. That's 100x less work than manually curating the product info (assuming 1% error rate).
Back in my day people used to bash on JavaScript. Today one can only dream of a world where JS is the worst of our engineering problems.
That's the whole benefit of using LLMs for categorization: they work for you, not for the SEO guy... well, prompt injection tricks aside.
What are a few examples of things with an 'intrinsic, instinctive quality'?
emotional or intellectual energy or intensity, especially as revealed in a work of art or an artistic performance. "their interpretation lacked soul"
Is this the definition used? I'm not sure how a JSON document is supposed to convey emotional or intellectual energy, especially since it's basically a collection of tags. Maybe I also lack soul?
Or is there yet another definition I didn't find?
So LLMs are not gritty and down and dirty, and don't get down. They're not the real stuff.
If you wanna be down you gotta keep it real, and mysticism is categorically not that.
Yes, things are getting better per unit (GPUs get more efficient, better yet AI-optimised chipsets are an order more efficient than using GPUs, etc.) but are they getting better per unit of compute faster than the number of compute units being used is increasing ATM?
Also, GPU pricing is hardly relevant. From now on we will see dedicated co-processors on the GPU to handle these things.
They will keep on keeping up with the demand until we meet actual physical limits.
Actually, building the AI agent for data research takes up most of my time these days.
Reminded me of the angst and negativity of these original "Web3" people, already bashing everything that was not in their mood back then.
• The crypto ecosystem is shady, I know, but the tech is great
FWIW I am genuinely asking. I don't know anything about the current tech. There's something about "zero knowledge proofs" but I don't understand how much of that is used in practice for real blockchain things vs just being research.
As far as I know, the throughput of blockchain transactions at scale is miserably slow and expensive and their usual solution is some kind of side channel that skips the full validation.
Distributed computation on the blockchain isn't really used for anything other than converting between currencies and minting new ones mostly AFAIK as well.
What is the great tech that we got from the blockchain revolution?
But zk-based really decentralized consensus now does 400 tps and it's extraordinary when you think about it and all the safety and security properties it brings.
And that's with proof-of-stake of course with decentralized sequencers for L2.
But I get that people here prefer centralized databases, managed by admins and censorship-empowering platforms. Your bank stack looks like it's designed for fraud too. Manual operations and months-long audits with errors, but that is by design. Thanks everyone for all the downvotes.
For many of us it isn't that we think the status quo is the RightWay™ - we just aren't convinced that crypto as it currently is presents a better answer. It fixes some problems, but adds a number of its own that many of us don't think are currently worth the compromise for our needs.
As you said yourself:
> The crypto ecosystem is shady, I know, but the tech is great
That but is not enough for me to want to take part. Yes the tech is useful, heck I use it for other things (blockchains existed as auditing mechanisms long before crypto-currencies), but I'm not going to encourage others to take part in an ecosystem that is as shady as crypto is.
> Thanks everyone for all the downvotes.
I don't think you are getting downvoted for supporting crypto, more likely because you basically said “you know that article you are all discussing?, well I think you'll want to know that I didn't bother to read it”, then without a hint of irony made assertions of “angst and negativity”.
And if I might make a mental health suggestion: caring about online downvotes is seldom part of a path to happiness :)
Both can be useful now and then, but the legit uses are lost in the noise.
And for blockchain... it was launched with the promise of decentralized currency. But we've had decentralized currency before in the physical world. Until the past few hundred years. Then we abandoned it in favor of centralized currency for some reason. I don't know, reliability perhaps?
Cryptocurrencies were launched with that promise.
They are but one use¹ of block-chains / merkle-trees, which existed long before them².
----
[1] https://en.wikipedia.org/wiki/Merkle_tree#Uses
[2] 1982 for blockchains/trees as part of a distributed protocol as people generally mean when they use the words now³, hash chains/trees themselves go back at least as far as 1979 when Ralph Merkle patented the idea
In fact it is only the 70s if you mean networks that learn via backprop & similar methods. Some theoretical work on artificial neurons was done in the 40s.
I for one fail to see the difference between these two kinds of snake oil.
> Some theoretical work on artificial neurons was done in the 40s.
"The perceptron was invented in 1943 by Warren McCulloch and Walter Pitts. The first hardware implementation was Mark I Perceptron machine built in 1957"
You seem to be labouring under the impression that blockchain and cryptocurrencies are one in the same. The point you seem to be missing is that I'm saying they are not the same. Blockchains (usually actually trees like merkle trees) are a thing that has existed long before cryptocurrencies which are one application of technique.
> I for one fail to see the difference between these two kinds of snake oil.
The gaggle of quackish sales people with miracle cures based on LLMs is pretty much the same sort of quackish sales people that touted miracle cures based on crypto currencies, yes. But LLMs are one use of neural networks and crypto/proof-of-work is one use of blockchains.
This all started with me correcting “And for blockchain... it was launched with the promise of decentralized currency.” — which is the incorrect equivalency of blockchain/cryptocurrency writ large.
> > Some theoretical work on artificial neurons was done in the 40s.
> "The perceptron was invented in 1943 by Warren McCulloch and Walter Pitts.
Exactly. You've just repeated my sentence with a little more detail.
40s: theory
50s: attempts at practical implementation
early 70s: backprop methods (as we currently mean the term wrt neural networks, backpropagation as a more general concept existed before that) first published, starting that decades' big excitement over the potential for neural networks.
> Then we abandoned it in favor of centralized currency for some reason. I don't know, reliability perhaps?
The global economy practically requires a centralized currency, because the value of your currency vs other countries becomes extremely important for trading in a global economy (importers want high value currency, exporters want low).
It’s also a requirement to do financial meddling like what the US has been doing with interest rates to curb inflation. None of that is possible on the blockchain without a central authority.
Even precious metal coins became endorsed by one authority or another (the cities/banks/little kingdoms stamping the coins). Because you as a normal person don't have the resources to validate every single piece of gold/silver you are paid with.
There has also been a short period when every 3rd bank had its own paper currency. That seems to be gone too. Perhaps because as a normal person maintaining a list of banks you could trust was too much.
It would have been an inconvenient currency for small transactions, but it’s still a currency.
The bank currencies were weird. Iirc, some of that was wrapped up in the Civil War and the Confederate currency being “official” but also basically worthless towards the end of the war. I think the Great Depression killed them, when banks became insolvent and their currencies became worthless.
+100
Rule in blockchain: Whenever there is money beyond paying for services/infra like AWS, there is a problem.
Still, every of my post that is more or less supportive of crypto gets downvoted. And I am the first to tell the ecosystem is one of the worst in tech so that's always mild support.
But yes, you're right it's probably sem web people overreacting to _my_ rant :)
I feel like this is so obvious to point out that I must be missing something, but the whole article goes to heroic lengths to avoid... HTML. Is it because HTML is difficult and scary? Why invent a custom JSON format and a custom JSON-to-HTML compiler toolchain than just write HTML?
The semantics aren't hidden in the markup. The semantics are the markup.
The typical HTML page these days is horrifically bloated, and whilst it’s machine parsable, it’s often complicated to actually understand what’s what. It’s random nested divs and unified everything. All the way down.
But I do wonder if adding context to existing HTML might be better than a whole other JSON blob that’ll get out of sync fast.
Basically, what you are saying is already rdf/xml, except that devs don't like xml so json-ld came along as a man-machine-friendlier way to do rdf/xml.
There are also various microdata formats that allow you to annotate html in a way the machines can parse it as rdf. But that can be limited in some cases if you want to convery more metadata.
Web 2.0 = read/write
Web 3.0 = read/write/own
Back in actual Web 2.0, the internet was not dominated by large platforms, but more spread out by ppl hosting their own websites. Interaction was everywhere and the spirit resolved around "p2p exchange" (not technologically speaking).
Now, most traffic goes over large companies which own your data, tell you what to see and severely limit genuine exchange. Unless you count out the willingness of "content monkeys", that is.
What has changed? The internet has settled for a lowest-common denominator and moved away from a space of tech savy people (primarily via the arrival of smartphones). The WWW used to be the unowned land in the wild west, but has now been colonized by an empire from another world.
This is "reminagined" and more commonly known as just "Web3". Entirely different from the older conceptual "Web 3.0" in context of semantic web.
So more like:
Web 3.0 = read/write/describe
https://en.wikipedia.org/wiki/Semantic_Web
The resolution to this conondrum is of course to do both simultaneously and refer to it as "Web 5". Ask my friend Jack.
This would also address the two reasons why the author thinks AI is not suited to this task:
1. human stays in the loop by (ideally) checking the JSON-LD before publishing; so fewer hallucination errors
2. LLM compute is limited to one time per published content and it’s done by the publisher. The bots can continue to be low-GPU crawlers just as they are now, since they can traverse the neat and tidy JSON-LD.
——————
The author makes a good case for The Semantic Web and I’ll be keeping it in mind for the next time I publish something, and in general this will add some nice color to how I think about the web.
The author (and much of HN?) seems to be unaware that it's not just thousands of websites using JSON-LD, it's millions.
For example: install WordPress, install an SEO plugin like Yoast, and boom you're done. Basic JSON-LD will be generated expressing semantic information about all your blog posts, videos etc. It only takes a few lines of code to extend what shows up by default, and other CMSes support this took.
SEOs know all about this topic because Google looks for JSON-LD in your document and it makes a significant difference to how your site is presented in search results as well as all those other fancy UI modules that show up on Google.
Anyone who wants to understand how this is working massively, at scale, across millions of websites today, implemented consciously by thousands of businesses, should start here:
https://developers.google.com/search/docs/appearance/structu...
https://search.google.com/test/rich-results
Is this the "Semantic Web" that was dreamed of in yesteryear? Well it hasn't gone as far and as fast as the academics hoped, but does anything?
The rudimentary semantic expression is already out there on the Web, deployed at scale today. Someone creative with market pull could easily expand on this e.g. maybe someday a competitor to Google or another Big Tech expands the set of semantic information a bit if it's relevant to their business scenarios.
It's all happening, it's just happening in the way that commercial markets make things happen.
Already implemented in part on my Conzept Encyclopedia project (using OpenAI): https://conze.pt/explore/%22Neuro-symbolic%20AI%22?l=en&ds=r...
Something like this is much easier done using the semantic web (3D interactive occurence map for an organism): https://conze.pt/explore/Trogon?l=en&ds=reference&t=link&bat...
On Conzept one or more bookmarks you create, can be used in various LLM functions. One of the next steps is to integrate a local WebGPU-based frontend LLM, and see what 'free' prompting can unlock.
JSON-LD is also created dynamically for each topic, based on Wikidata data, to set the page metadata.
Google has been pushing JSON-LD to webmasters for better SEO for at least 5 years, if not more: https://developers.google.com/search/docs/appearance/structu...
There really isn't a need to do it as most of the relevant page metadata is already captured as part of the Open Graph protocol[0] that Twitter and Facebook popularized 10+ years ago as webmasters were attempting to set up rich link previews for URLs posted to those networks. Markup like this:
<meta property="og:type" content="video.movie" />
is common on most sites now, so what benefit is there for doing additional work to generate JSON-LD with the same data?
If archival systems and library's are using XML, wouldn't it be preferable to follow their lead and whatever standards they are using? Since they are the ones who are going to use this stuff most, most likely.
If nothing else, you can add a processing instruction to the document they use to convert it to HTML.
Promoting JSON-LD potentially makes it more palatable to the modern web creators, perhaps increasing adoption. The bots have already adapted.
As far as I can tell archivists don't care about "modern web creators", and they likely shouldn't, since archiving is a long term project. I know I don't, and I'm only building software for digital archiving.
> [MarcXML, BibTex etc] actually have very, very deep support in many places (for example in library and archival systems) but on the open web they are not a goer.
Like XSLT?
I will still include JSON-LD when it make financial sense for a site. In practice that usually just means business metadata for search results and product data for any ecommerce pages.
Unfortunately, it is unlikely we will ever get something like a Semantic web. It seemed like a good idea in the beginning of 2000s but now there is honestly no need for it as it is quite cheap and easy to attach meaning to text due to the progress in LLMs and NLP.
The semantic web original idea was the interconnection of every bit of information in a format a machine can travel for a human, so the human can find any specific bit ever written with little to no effort without having to humanly scan pages of moderately related stuff.
We never achieve such goal. Some have tried to be more on the machine side, like WikiData, some have pushed to the extreme the library science SGML idea of universal classification not ended to JSON but all are failures because they are not universal nor easy to "select and assemble specific bit of information" on human queries.
LLMs are a, failed, tentative of achieve such result from another way, their hallucinations and slow formation of a model prove their substantial failure, they SEEMS to succeed for a distracted eye perceiving just the wow effect, but they practically fails.
Aside the issue with ALL test done on the metadata side of the spectrum so far is simple: in theory we can all be good citizens and carefully label anything, even classify following Dublin Core at al any single page, in practice very few do so, all the rest do not care, or ignoring the classification at all or badly implemented it, and as a result is like an archive with some missing documents, you'll always have holes in information breaking the credibility/practical usefulness of the tool.
Essentially that's why we keep using search engines every day, with classic keyword based matches and some extras around. Words are the common denominator for textual information and the larger slice of our information is textual.
The problem I have with ML guided search is the ML takes web average view of what I mean, which sometimes I need to understand and then try and work around if that's wrong. It can become impossible to find stuff off the beaten track.
The nice thing about keyword and exact text searching with fast iteration is it's my mental model that is driving the results. However if it's an area I don't know much about there is a chicken and egg problem of knowing which words to use.
Personally I notes news, importing articles in org-mode, so I have a "trail" of the news I think are relevant in a timeline, sometimes I remember I've noted something but I can't find it immediately in my own notes with local full-text search on a very little base compared to the entire web, simply because a title does express something with very different words than another and at the moment of a search I do not think about such possible expression.
For casual searches we do not notice, but for some specific searches emerge very clear as a big limitation, however so far LLMs does not solve it, they are even LESS able to extract relevant information, and "semantic" classifications does not seems to be effective either, a thing even easier to spot if you use Zotero and tags and really try to use tags to look for something, in the end you'll resort on mere keyword search for anything.
That's why IMVHO it's an unsolved so far problem.
So effective search more about excluding than including.
Exact phrases or particular keywords are great tools here.
Note there is also a difference between finding an answer to a particular question and finding web pages around a particular topic. Perhaps LLM's are more useful for the former - where there is a need to both map the question to an embedding, and summarize the answer - but for the latter I'm not interested in a summary/quick answer, I'm interested in the source material.
Sometimes you can combine the two - LLM's for a quick route into the common jargon, which can then be used as keywords.
That's exactly the Gessner's problem and unimplementable so far solution: we have information packed in various form, when we want specific bits it's hard do narrow enough available information packages (books, articles, post etc).
For common stuff in the vast see of the web we tend to end up very frequently on exactly or nearly exactly what we want, but for less common stuff there is a big problem. Just knowing the historical temperature of a small place where many have recorded temperatures, but still no one have create a table and a graph or daily minima and maxima for let's say the 10 years period we want it's hard to find an answer. For some kind of data we have wikidata, for some others we have public datasets from various sources, but still no easy way to locate and narrow them.
LLM are in theory an answer, unfortunately not in practice first for freshness problem (you have to train a model, ingesting new information is still training, far longer than a search engine crawler) and training itself witch is like the opposite of semantic web: instead of having the author prepare the information for machines we have third party who do it en masse, unfortunately they tend to be even less precise than semantic web mean authors PLUS models hallucinations and essentially no ability to tell where their answer came from.
That's why we still miss semantic search...
Browsers, for example, completely ignore classes when building the accessibility tree for a web page. Only the HTML structure and a handful of CSS properties have an impact on accessibility.
Class names were always meant as an ease of use feature for styling, overloading them with semantic meaning could break a number of sites built over the last few decades.
The trick, as always, is to get people to use it.
There is an additional cost to making or using ontologies, making them available and publishing open data on the semantic web. The cost is quite high, the returns aren't immediate, obvious or guaranteed at all.
The vision of the semantic web is still valid. The incentives to get there are just not in place.
Is JSON-LD just reinventation of this?
JSON-LD to me looks more like trying to glue different documents together, its not about the transformation itself.
The idea is the best, but arguably the implementation is lacking.
> Is JSON-LD just reinventation of this?
Yup. It's "RDF/XML but we don't like XML"
- OpenGraph (by Facebook, probably used by Whatsapp) – https://ogp.me/
- Schema.org markup (the main point of this blog) – https://schema.org/
- oEmbed (used to embed media in another page, e.g. YouTube videos on a WordPress blog) – https://oembed.com/
I don't think that one is necessarily better than the other, but imagining that llms are a silver bullet when another trending story in the front pages is about prompt injection used against the slack ai bots sounds a bit over optimistic.
And prompt injection is irrelevant because the alternative we're considering is letting publishers directly choose the metadata.
LLMs are much better when the user adapts the categories to their needs or crunches the text to pull only the info relevant to them. Communicating those categories and the cutoff criteria would be an issue in some contexts, but still better if communication is not the goal. Domain knowledge is also important, because nitch topics are not represented in the llm datasets and their abilities fail in such scenarios.
As I said above, one is not necessarily better than the other and it depends on the use cases.
How does price affect the relevance of prompt injection? That doesn't make sense.
> nitch
Niche. Pronounced neesh.
I don't mind the delusions of most people, but the idea that llms will deal with spam if you throw a million times more electricity against it is what makes the planet burning.
JSON-LD in a page's header is much easier for a CMS to present to the page author for editing. It can be a form in the editing UI. Wordpress et al have SEO plugins that make editing the JSON-LD data pretty straightforward.
But it's not a standard that is recognised, and so is no kind of metadata format.
You can see what I mean learning about the Solid Protocol, I gave a talk about it a couple of years ago: https://m.youtube.com/watch?v=kPzhykRVDuI
if the "preview" link relation type is worth mentioning it's worth substantiating the claims about adoption. when did the big players adopt? why? what of the rest of the types and their relation to would-be "a.i." claims?
how would we write html differently and what capabilities would we expose more readily to driving by links, like carousels only if written with a11y in mind? how would our world-wild web look different if we wrote html like we know it? than only give big players a pass when we view source?
For example, Microdata[0] is one in-line way to do it, and RDFa[1] is another.
See also this comment above: https://news.ycombinator.com/item?id=41309555
TFA mentions it at the end: ‘There is also “microdata.” It’s very simple but I think quite hard to parse out.’ I disagree: it’s no harder to parse than HTML, and one already must parse HTML in order to correctly extract JSON-LD from a script tag (yes, one can incorrectly parse HTML, and it will work most of the time).
Either way it sounds awfully expensive for data that probably isn't used by the client most of the time. Do you have to explicitly ask for it? Is there some ad-hoc way to tell the server "hey I don't need the JSON-LD data?"
I actually implemented a simple link preview system a while ago. It uses opengraph and twitter cards meta data that is commonly added to web pages for SEO. That works pretty well.
Ironically, I did use chat gpt for helping me implement this stuff. It did a pretty good job too. It suggested some libraries I could use and then added some logic to extract titles, descriptions, icons, images, etc. with some fallbacks between various fields people use for those things. It did not suggest me to add logic for json-ld.
IMDB could easily be a service entirely dedicated to hosting movie metadata as RDF or JSON-LD. They need to fund it though, and the go to seems to be advertising and API access. Advertising means needing human readable UI, not metadata, and if they put data behind an API its a tough sell to use a standardized and potentially limiting format.
It's not widely adopted, it's used as an attempted growth hack in a few locations that may or may not be of use (with value being relative to how US centric your and your audiences Internet use is)
Thus, every tool makes CIM documents slightly differently and there are no guarantees that a document created in one tool will be usable in another
Using contemporary AI models aren't all websites machine-readable? - or potentially even more readable than semantic web unless an ai model actually does the semantic classification while reading it?
I do wonder how any of this is better than using the meta tags of the html, though? Especially for such use cases as the preview. Seems the only thing that isn't really there for the preview is the image? (Well, title would come from the title tag, but still...)
Be nice to bots. This is advertisment after all.
Support standards even if Google does not. Other bots might not be as sofisticated.
For me, yes, it is worth the bother
Hm.
Just today, I am working on an experimental training/consuming app pair. The training part will leverage JSON data from a backend I designed.
In which case we have all been on it since the mid 90s.
It leans too hard on text and doesn't have enough concepts defined as resources but what do you expect, Python didn't have a good package manager for decades because 2 + 2 = 3.9 with good vibes beats 2 + 2 = 4 with honest work and rigor for too many people.
The big trouble I have with RDF tooling is inadequate handling of ordered lists. Funny enough 90% of the time or so when you have a list you don't care about the order of the items and frequently people use a list for things that should have set semantics. On the other hand, you have to get the names of the authors of a paper in the right order or they'll get mad. There's a reasonable way to turn native JSON lists into RDF lists
https://www.w3.org/TR/json-ld11/#lists
although unfortunately this uses the slow LISP lists with O(N) item access and not the fast RDF Collections that have O(1) access. (What do you expect from M.I.T.?)
The trouble is that SPARQL doesn't support the list operations that are widespread in document-based query languages like
https://www.couchbase.com/products/n1ql/
https://docs.arangodb.com/3.11/aql/
or even Postgresql. There is a SPARQL 1.2 which has some nice additions like
https://www.w3.org/TR/sparql12-query/#func-triple
but the community badly needs a SPARQL 2 that catches up to today's query languages but the semantic web community has been so burned by pathological standards processes that anyone who can think rigorously or code their way out of a paper bag won't go near it.
A substantial advantage of RDF is that properties live in namespaces so if you want to add a new property you can do it and never stomp on anybody else's property. Tools that don't know about those properties can just ignore them, but SPARQL, RDFS and all that ought to "just work" though OWL takes some luck. That's got a downside too which is that adding namespaces to a system seems to reduce adoption by 80% in many cases because too many people think it's useless and too hard to understand.
If there's anything that sucks today it is that people feel they have to add all kinds of markup for different vendors (such as Facebook's Open Graph) I remember the Semweb folks who didn't think it was a problem that my pages had about 20k of visible markup and 150k of repeated semantic markup. It's like the folks who don't mind that an article with 5k worth of text has 50M worth of Javascript, ads, trackers and other junk.
On the other hand I have no trouble turning
<meta name="description" content="A brief description of your webpage content.">
@prefix meta: <http://example.com/my/name/space> .
<http://example.com/some/web/page> meta:description "A brief description of your webpage content." .
As such semantic web was not a natural follower to what we had before, and not web 3.0.
> It would of course be possible to sic Chatty-Jeeps on the raw markup and have it extract all of this stuff automatically. But there are some good reasons why not. > > The first is that large language models (LLMs) routinely get stuff wrong. If you want bots to get it right, provide the metadata to ensure that they do. > > The second is that requiring an LLM to read the web is throughly disproportionate and exclusionary. Everyone parsing the web would need to be paying for pricy GPU time to parse out the meaning of the web. It would feel bizarre if "technological progress" meant that fat GPUs were required for computers to read web pages.
The second point is silly, because there is no reason for everyone to train their own LLMs on the raw web. You'd have a few companies or projects that handle the LLM training, and everyone else uses those LLMs.
I'm not a big fan of LLMs, and not even a big believer in their future, but I still think they have a much better chance of being useful for these types of tasks than the semantic web. Semantic web is a dead idea, people should really allow it to rest.
In 5 years resource price is likely negligible and accuracy is high enough that you just trust it.
What makes you think that I am not an expert btw?
It indeed seems like you appear to believ that what's written on the internet is true. So if someone writes that LLMs are not a contester to semantic web - then it might be true.
Could it be, that I merely challenge that author of the blog article and don't take his predictions for granted?
To think a turd such as JSON-LD can save the "SemWeb" (which doesn't really exist), and even add CSV as yet another RDF format to appease "JSON scientists" lol seems beyond absurd. Also, Facebook's Open Graph annotations in HTML meta-links are/were probably the most widespread (trivial) implementation of SemWeb. SemWeb isn't terrible but is entirely driven by TBL's long-standing enthusiasm for edge-labelled graph-like databases (predating even his WWW efforts eg [1]), plus academia's need for topics to produce papers on. It's a good thing to let it go in the last decade and re-focus on other/classic logic apps such as Prolog and SAT solvers.
What which one should you use, and why?
Should you use several? How does that impact the site?
But it is true, if you can't make sense of your data, then the Semantic Web probably isn't for you. (It's the least of your problems.)
Yet another reason NOT to use the semantic web. I don't want to help any LLMs.
[1] This is also something the screen assist software should do, not the publisher.
Why? Because a significant percentage of people working on web development think a webpage is composed as many <spans> and <divs> as you like, styled with CSS and the content is injected into it with JavaScript.
These people don't know what an <img> tag is, let alone alt-text, or semantic heading hierarchy. And yet, those are exactly the things that Screen Reader software understands.