8 ms·

Does AI mean we don't need the Semantic Web?

54 points by edent 3y ago

tycho-newman 3y ago
Maybe? The semantic web is useful because it makes us catalog our thoughts and language in a machine readable way. The key thing is the cost of running these semantic web machines is smaller than running a GPU powered language model. Also, didn't these models learn from the semantic web? No idea if that matters, but still.
- chrismorgan 3y ago
  Another hazard of the AI approach is that it’s probabilistic. Presuming the semantic information and implementation are all correct (which I will admit is not a given), they’ll always be correct, whereas the AI approach will sometimes give wrong answers.
  - brookst 3y ago
    As you note, semantic data created by humans is also probabilistic and can be wrong. Ditto with semantic data created by automated systems that were created by humans. There will always be incorrect data in the world. AI doesn't change that, but so what?
    - chrismorgan 3y ago
      There’s a difference, though: if the erroneous data comes from human input error, it can easily be corrected, and if the error is due to a software bug, it can be diagnosed and corrected; but errors that come from LLMs (the main type of AI in question here, and indicative enough of AIs in general, as it stands, though probably worse) are basically unfixable (that is: we don’t know how it would even be possible to fix them in general, so all you can do is patch things here and there, which will only probably fix the problems, and will increase the frequency of other problems).
      - brookst 3y ago
        How is an error from an LLM less correctable than one from a human? If "Paris is the capital of Germany" makes it into a news article, how does the origin of the error impact its correctability? Reinforcement learning seems pretty analogous to how we correct people who are wrong about something. I'm really not seeing a category difference here?
        chrismorgan 3y ago
        With a human, if you have an employee, you can instruct them to correct an error, and if they refuse to or just don’t, you can fire them—you have recourse. LLMs are complex like humans, but you lack the ability to make it do what you want, or to have recourse. If the model has gained the impression that Paris is the capital of Germany (a more concrete and fixable error than most, frankly), this is woven into its parameters throughout, and it’s really hard to fix, and for LLMs we basically have no idea how to actually truly do so. With such a simple error as “Paris is the capital of Germany”, we do know how to fine-tune the model to fairly effectively mask that error, but the important errors are often closer to what you might consider procedural than factual—rather than its “memory” containing a wrong fact, it’s “thinking” in the wrong way. But all up, I’d call “lack of recourse” the most important difference.
  - pixl97 3y ago
    Based on the failure of the semantic web as of so far, I think the statement 'not a given' is a given in itself. Humans are horrible at giving systems correct information.
  - grumpymouse 3y ago
    LLMs always remind me of pigeons you see in the city that have spent their whole life eating out of bins and pecking dropped chunks of kebab. We trained it on a bunch of stuff, but we're not sure quite what. Looks like it works OK, so let's get it on a plate!
- pjc50 3y ago
  > The semantic web is useful because it makes us catalog our thoughts and language in a machine readable way That's not a use, that's a cost.
- tgv 3y ago
  Idk what definition of "semantic web" you have in mind, but AFAIK, it usually relies labeling words and names, and adding some vague relations. That's not enough. You can't build enough, inspectable, correct knowledge that way. Sure, you can list genealogical trees and and the components of a car, but it won't help you one bit when someone asks "give the five most important things about XML in gangsta rap style" (0), because none of the needed knowledge can be represented in any form of semantic web I've seen, and there isn't an engine capable of running the required inferences. [0] https://libreddit.de/r/ProgrammerHumor/comments/13lgv3u/chatgpt_in_gangster_rap_style_give_the_top_five/ https://libreddit.de/r/ProgrammerHumor/comments/13lgv3u/chat... You may think that's a joke, but from the performance perspective, it's utterly impressive. Summarizing a paper is even more impressive, and is equally far outside the scope of the semantic web.
deleted 3y ago
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deleted 3y ago
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evilspammer 3y ago
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potatolicious 3y ago
Somewhat? On the one hand, replacing a deterministic system with a probabilistic one is generally bad. Why replace something that's 100% accurate all the time with something that is... somewhat less than that? On the other hand, the semantic web has been a complete failure. Even present attempts at semantically understanding the web (aka what search engines do all day) require highly complex heuristic and machine-learned solutions... so the "fully deterministic" thing is only technically true, but not meaningfully useful in any sense. Even the actual semantic web standards that exist (and are poorly adopted) don't seem to really solve the core problem with the semantic web: there's no such thing as a universal ontology. Every attempt to produce a broad-based shared ontology has failed - there are fundamental disagreements about how to organize information, and information organization needs turn out to nearly always be application-specific, and be highly unstable over time, preventing there from being a useful, durable universal standard for various topics. So I guess long story short: we need AI (or just highly advanced heuristic methodologies) to scrape useful information out of natural content. We always have, because the deterministic alternative has never actually worked.
- alm1 3y ago
  I think the deterministic requirement was what made the semantic web nearly impossible at scale. Aligning on the schema for all the entities can be challenging within a small group, yet alone the world. Doing it continuously is impossible today. Wikidata has tons of abandoned and branches which are no longer relevant or even wrong. It seems like LLMs and vector representations for words are more practical ways to explore semantic data. Then the specific, reccuring queries may be optimized through graph representations.
  - potatolicious 3y ago
    A good way to think about it is that the "semantic web" was all about finding some kind of universal data representation for the world's data, and that has failed in large part because data representation is inherently application-specific. The only practicable version of the semantic web needs to embrace that reality. We need tools for converting, extracting, and interpreting data from one application-specific data representation to another. Sometimes that's LLMs, sometimes it can be done heuristically, but either way, the notion of the universal data structure simply isn't realistic.
jerf 3y ago
Whether or not we "need" it, it turns out that in practice the Semantic Web was actually harder than AI and we're getting the latter before the former. This has really always been the disconnect between the semantic web advocates and the skeptics like me. It's not that I don't buy your analysis of the benefits... it's that I do not and never have bought your analysis of the costs. The only question I have is how many orders of magnitude they undersell it by. It's certainly a non-trivial number. What AI will do is basically swamp the semantic web. The problems the world will be dealing with with AI, like the deluge of AI-generated meaningless garbage, training AIs accidentally on other AI-generated garbage, the complete overwhelming of all human voices by AI voices by sheer weight [1], will be so large and pressing that the problems of the semantic web will just get washed away in the flood. And, sadly, the tools of the semantic web will be effectively useless to help; all the signals they are based on are even easier to forge than a human voice in arbitrary text. No help at all. [1] What will it look like to live in a world where for every kilobyte of bespoke humanity I produce, an AI can produce megabytes? What will it look like when someone can afford to use AI to build an entire Reddit, just for me and only me, and isolate me there?
- brookst 3y ago
  Humans represent a tiny fraction of stock trades, telecom bits sent, hours of video created. If you're happier with a unique personalized reddit, is that worse than preferring video games to movies?
  - pixl97 3y ago
    >a tiny fraction of stock trade In which the rich have been getting richer, and asset inflation has been growing ever higher. >If you're happier with a unique personalized reddit The question here is this depends. Simulacra of which Reddit? Of the one that shows you a good world where we try to become better people? Of the Reddit that tells you "They" are out to get you so you should be afraid? Personalized of the 'Real' world, or just made up and trending towards the imaginary? I mean, when I play a video game, I know I'm not playing reality. The line starts breaking down as the level of fidelity of reality is neared.
dtagames 3y ago
Yes, because the semantic web required people to mark up stuff semantically, which they didn't do. The LLM web doesn't need people to explain the role of headings, paragraphs, etc. It can figure those out. It's faster, cheaper, and more effective than manually applying markup.
- wildrhythms 3y ago
  How do you reckon it's cheaper?
  - worewood 3y ago
    Automatic labor vs. Manual labor
  - spacetime_cmplx 3y ago
    While OP's reply answers your question, it's important to not apply current costs to predict the future of AI. Hardware for LLMs is one step function away from unimaginable capabilities. That breakthrough could be in performance, cost, or more likely, both. Imagine GPT-4 at 1/1000th the cost. That's where we're going. And you can bet your ass Nvidia is working on it as we speak. Or maybe someone else will leapfrog them like ARM did to Intel.
    - deleted 3y ago
      [deleted]
  - pixl97 3y ago
    Remember when a Cray supercomputer was nearly 10 million dollars and had 8MB of RAM. And remember that now we carry something insanely more powerful in our pocket today. If something is possible but not cheaper than people on a computer today, then is very very much likely it will become cheaper than people in a few years. Computers keep scaling and individuals don't.
    - dns_snek 3y ago
devmor 3y ago
A similar thinking point might be: Do casinos mean we don't need jobs?
- ukuina 3y ago
  Could you expand on this? I am unable to see the connection.
  - devmor 3y ago
    ML (as currently used) is probabilistic. Whether or not you get what you want is up to chance. Granted, based on the dataset, it's quite a bit more likely than winning big at the casino - but regardless, it's still not deterministic like intentional semantic markup.
wildrhythms 3y ago
>Suppose you saw this text written for a human: >"Our opening hours are: Weekdays 10 until 7. Weekend 10 until 10 (Early closing 9 o'clock Sunday)." Pardon me, but have you visited a local business' website recently? Rarely is information like this presented in such an explicit way; it's usually crammed into some corner of the footer and without the preamble describing the relevant information. And if the business has multiple locations? Now the visual blocking and semantics become relevant to accurately parse the thing.
- AshamedCaptain 3y ago
  And they will use tricks that are only apparent visually, e.g. using tables and putting "same as above" which utterly confuses the Bing bot.
KrugerDunnings 3y ago
The big lie about the semantic web is this ridiculous notion that it is machine readable just because it has an unambiguous grammar to parser and has ontologies to disambiguate homonyms.
- SeanLuke 3y ago
  That's not what the semantic web is about at all. It has nothing to do with text.
  - KrugerDunnings 3y ago
    Chapter 2 Verse 1: https://www.w3.org/TR/rdf11-primer/ https://www.w3.org/TR/rdf11-primer/
    - SeanLuke 3y ago
      Thanks, I was rather involved in the research community. It has little to do with parsing text NLP-style. It has everything to do with annotating documents with formally structured KR data, or embedding the same into them. In fact, my own involvement in the field started with Jim Hendler suggesting I look into the possibility of developing a spider that would wander across HTML pages and glean knowledge from them with an NLP technique. I worked on that for a while, then abandoned it and proposed to him instead that web pages should be marked up with formally parseable structured semantic data. Why parse a student's page saying he "Goes to U Maryland" when he could just write <claim obj1="me" obj2="UMaryland" rel="attend" ontology="http://ontology.org/university-ontology/ http://ontology.org/university-ontology/">. That was 1995.
      - KrugerDunnings 3y ago
        Well I am at least glad that someone name drops someone other then TBL.
        SeanLuke 3y ago
dimal 3y ago
Is anyone actually doing the semantic web anymore? I thought people gave up on that over a decade ago. It’s hard enough to get devs to just make content semantic enough for accessibility, let alone defining semantics for computers.
- tracerbulletx 3y ago
  The closest practical application that's actually happening I think would be Google's Structured Data guidelines using schema.org types and JSON-LD to power a bunch of enhanced search display features for Rich Results, and for powering Google Shopping. Compliance with that is pretty ubiquitous for Search optimization reasons.
valine 3y ago
Why is no one mentioning the hardware requirements to run each? The semantic version runs with 2KB of RAM and takes a couple of microseconds. The LLM needs at least 30GB of RAM and maybe 5 seconds of processing time.
- pjc50 3y ago
  Nobody cares because the semantic web results are nowhere near as interesting as the "AI" results.
hosh 3y ago
I think if anything, Semantic Web makes more sense with AI, not less. Not necessarily that we mark up web pages, but that formally defining an ontology that can interlink with other ontologies can help define the cultural boundaries we are seeking with ethical and safe use of AI. For example, there is an old effort called Cyc, which is an ontology and knowledge base of common sense. Something like this can be used to align LLMs. However, what is common sense is not necessarily universal across cultures. The underlying ideas behind semantic web can be used for ontologies that don’t agree with each other.
tracerbulletx 3y ago
If anything I think that LLMs are making the Semantic Web realizable in a new way. LLM can summarize the raw content down to a schematized representation, that you can then reference to express deterministic programmatic actions using the resulting properties. You still might want to do that rather than let the LLM do everything.
jimwhite 3y ago
Humans don't need the Semantic Web, AI/bots do. It is expensive to turn text into the useful semantic bits that are useful for doing computations on large (or even moderate) quantities of data. So the AI will create a new Semantic Web3 that any bot (or human) can contribute to on any topic (especially about anything on the web).
amanzi 3y ago
> As much as I love the semantic web, it is hard for humans to write, update, and maintain. Of course, ChatGPT can help a human to write the tags to put on the website: > Me: I have a shop that was the following opening hours: "Our opening hours are: Weekdays 10 until 7. Weekend 10 until 10 (Early closing 9 o'clock Sunday)." I would like to include these opening hours on the website of the shop using meta tags, so that search engines can easily read and parse them. > ChatGPT 3.5:  <meta itemprop="openingHours" content="Mo-Fr 10:00-19:00"> <meta itemprop="openingHours" content="Sa 10:00-22:00"> <meta itemprop="openingHours" content="Su 10:00-21:00"> (ChatGPT actually responded with a lot more text and code in the response, but I just quoted the relevant bit.)
PeterStuer 3y ago
EU spent so many research Euros on Semantic Web projects. They all went something like this: CS researchers: Hey guys, here's the niche marginal improvement our grad student spent 200 hours developing. Here are the guidelines for you domain experts on how to encode and load your knowledge. START! Domain experts: euh ... what you are asking us to do would take 9 manyears for the initial base and a 2 FTE upkeep. Why exactly would we do this? CS researchers: not my problem. Paper has been submitted and accepted. Your question was out of scope. Want to join our next proposal?

54 points by edent 3y ago

Maybe? The semantic web is useful because it makes us catalog our thoughts and language in a machine readable way. The key thing is the cost of running these semantic web machines is smaller than running a GPU powered language model. Also, didn't these models learn from the semantic web? No idea if that matters, but still.

chrismorgan 3y ago
Another hazard of the AI approach is that it’s probabilistic. Presuming the semantic information and implementation are all correct (which I will admit is not a given), they’ll always be correct, whereas the AI approach will sometimes give wrong answers.
- brookst 3y ago
  As you note, semantic data created by humans is also probabilistic and can be wrong. Ditto with semantic data created by automated systems that were created by humans. There will always be incorrect data in the world. AI doesn't change that, but so what?
  - chrismorgan 3y ago
    There’s a difference, though: if the erroneous data comes from human input error, it can easily be corrected, and if the error is due to a software bug, it can be diagnosed and corrected; but errors that come from LLMs (the main type of AI in question here, and indicative enough of AIs in general, as it stands, though probably worse) are basically unfixable (that is: we don’t know how it would even be possible to fix them in general, so all you can do is patch things here and there, which will only probably fix the problems, and will increase the frequency of other problems).
    - brookst 3y ago
      How is an error from an LLM less correctable than one from a human? If "Paris is the capital of Germany" makes it into a news article, how does the origin of the error impact its correctability? Reinforcement learning seems pretty analogous to how we correct people who are wrong about something. I'm really not seeing a category difference here?
      - chrismorgan 3y ago
        With a human, if you have an employee, you can instruct them to correct an error, and if they refuse to or just don’t, you can fire them—you have recourse. LLMs are complex like humans, but you lack the ability to make it do what you want, or to have recourse. If the model has gained the impression that Paris is the capital of Germany (a more concrete and fixable error than most, frankly), this is woven into its parameters throughout, and it’s really hard to fix, and for LLMs we basically have no idea how to actually truly do so. With such a simple error as “Paris is the capital of Germany”, we do know how to fine-tune the model to fairly effectively mask that error, but the important errors are often closer to what you might consider procedural than factual—rather than its “memory” containing a wrong fact, it’s “thinking” in the wrong way. But all up, I’d call “lack of recourse” the most important difference.
- pixl97 3y ago
  Based on the failure of the semantic web as of so far, I think the statement 'not a given' is a given in itself. Humans are horrible at giving systems correct information.
- grumpymouse 3y ago
  LLMs always remind me of pigeons you see in the city that have spent their whole life eating out of bins and pecking dropped chunks of kebab. We trained it on a bunch of stuff, but we're not sure quite what. Looks like it works OK, so let's get it on a plate!
pjc50 3y ago
> The semantic web is useful because it makes us catalog our thoughts and language in a machine readable way That's not a use, that's a cost.
tgv 3y ago
Idk what definition of "semantic web" you have in mind, but AFAIK, it usually relies labeling words and names, and adding some vague relations. That's not enough. You can't build enough, inspectable, correct knowledge that way. Sure, you can list genealogical trees and and the components of a car, but it won't help you one bit when someone asks "give the five most important things about XML in gangsta rap style" (0), because none of the needed knowledge can be represented in any form of semantic web I've seen, and there isn't an engine capable of running the required inferences. [0] https://libreddit.de/r/ProgrammerHumor/comments/13lgv3u/chatgpt_in_gangster_rap_style_give_the_top_five/ https://libreddit.de/r/ProgrammerHumor/comments/13lgv3u/chat... You may think that's a joke, but from the performance perspective, it's utterly impressive. Summarizing a paper is even more impressive, and is equally far outside the scope of the semantic web.

deleted 3y ago

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deleted 3y ago

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evilspammer 3y ago

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potatolicious 3y ago

Somewhat? On the one hand, replacing a deterministic system with a probabilistic one is generally bad. Why replace something that's 100% accurate all the time with something that is... somewhat less than that? On the other hand, the semantic web has been a complete failure. Even present attempts at semantically understanding the web (aka what search engines do all day) require highly complex heuristic and machine-learned solutions... so the "fully deterministic" thing is only technically true, but not meaningfully useful in any sense. Even the actual semantic web standards that exist (and are poorly adopted) don't seem to really solve the core problem with the semantic web: there's no such thing as a universal ontology. Every attempt to produce a broad-based shared ontology has failed - there are fundamental disagreements about how to organize information, and information organization needs turn out to nearly always be application-specific, and be highly unstable over time, preventing there from being a useful, durable universal standard for various topics. So I guess long story short: we need AI (or just highly advanced heuristic methodologies) to scrape useful information out of natural content. We always have, because the deterministic alternative has never actually worked.

alm1 3y ago
I think the deterministic requirement was what made the semantic web nearly impossible at scale. Aligning on the schema for all the entities can be challenging within a small group, yet alone the world. Doing it continuously is impossible today. Wikidata has tons of abandoned and branches which are no longer relevant or even wrong. It seems like LLMs and vector representations for words are more practical ways to explore semantic data. Then the specific, reccuring queries may be optimized through graph representations.
- potatolicious 3y ago
  A good way to think about it is that the "semantic web" was all about finding some kind of universal data representation for the world's data, and that has failed in large part because data representation is inherently application-specific. The only practicable version of the semantic web needs to embrace that reality. We need tools for converting, extracting, and interpreting data from one application-specific data representation to another. Sometimes that's LLMs, sometimes it can be done heuristically, but either way, the notion of the universal data structure simply isn't realistic.

jerf 3y ago

Whether or not we "need" it, it turns out that in practice the Semantic Web was actually harder than AI and we're getting the latter before the former. This has really always been the disconnect between the semantic web advocates and the skeptics like me. It's not that I don't buy your analysis of the benefits... it's that I do not and never have bought your analysis of the costs. The only question I have is how many orders of magnitude they undersell it by. It's certainly a non-trivial number. What AI will do is basically swamp the semantic web. The problems the world will be dealing with with AI, like the deluge of AI-generated meaningless garbage, training AIs accidentally on other AI-generated garbage, the complete overwhelming of all human voices by AI voices by sheer weight [1], will be so large and pressing that the problems of the semantic web will just get washed away in the flood. And, sadly, the tools of the semantic web will be effectively useless to help; all the signals they are based on are even easier to forge than a human voice in arbitrary text. No help at all. [1] What will it look like to live in a world where for every kilobyte of bespoke humanity I produce, an AI can produce megabytes? What will it look like when someone can afford to use AI to build an entire Reddit, just for me and only me, and isolate me there?

brookst 3y ago
Humans represent a tiny fraction of stock trades, telecom bits sent, hours of video created. If you're happier with a unique personalized reddit, is that worse than preferring video games to movies?
- pixl97 3y ago
  >a tiny fraction of stock trade In which the rich have been getting richer, and asset inflation has been growing ever higher. >If you're happier with a unique personalized reddit The question here is this depends. Simulacra of which Reddit? Of the one that shows you a good world where we try to become better people? Of the Reddit that tells you "They" are out to get you so you should be afraid? Personalized of the 'Real' world, or just made up and trending towards the imaginary? I mean, when I play a video game, I know I'm not playing reality. The line starts breaking down as the level of fidelity of reality is neared.

dtagames 3y ago

Yes, because the semantic web required people to mark up stuff semantically, which they didn't do. The LLM web doesn't need people to explain the role of headings, paragraphs, etc. It can figure those out. It's faster, cheaper, and more effective than manually applying markup.

wildrhythms 3y ago
How do you reckon it's cheaper?
- worewood 3y ago
  Automatic labor vs. Manual labor
- spacetime_cmplx 3y ago
  While OP's reply answers your question, it's important to not apply current costs to predict the future of AI. Hardware for LLMs is one step function away from unimaginable capabilities. That breakthrough could be in performance, cost, or more likely, both. Imagine GPT-4 at 1/1000th the cost. That's where we're going. And you can bet your ass Nvidia is working on it as we speak. Or maybe someone else will leapfrog them like ARM did to Intel.
  - deleted 3y ago
    [deleted]
- pixl97 3y ago
  Remember when a Cray supercomputer was nearly 10 million dollars and had 8MB of RAM. And remember that now we carry something insanely more powerful in our pocket today. If something is possible but not cheaper than people on a computer today, then is very very much likely it will become cheaper than people in a few years. Computers keep scaling and individuals don't.
  - dns_snek 3y ago

devmor 3y ago

A similar thinking point might be: Do casinos mean we don't need jobs?

ukuina 3y ago
Could you expand on this? I am unable to see the connection.
- devmor 3y ago
  ML (as currently used) is probabilistic. Whether or not you get what you want is up to chance. Granted, based on the dataset, it's quite a bit more likely than winning big at the casino - but regardless, it's still not deterministic like intentional semantic markup.

wildrhythms 3y ago

>Suppose you saw this text written for a human: >"Our opening hours are: Weekdays 10 until 7. Weekend 10 until 10 (Early closing 9 o'clock Sunday)." Pardon me, but have you visited a local business' website recently? Rarely is information like this presented in such an explicit way; it's usually crammed into some corner of the footer and without the preamble describing the relevant information. And if the business has multiple locations? Now the visual blocking and semantics become relevant to accurately parse the thing.

AshamedCaptain 3y ago
And they will use tricks that are only apparent visually, e.g. using tables and putting "same as above" which utterly confuses the Bing bot.

KrugerDunnings 3y ago

The big lie about the semantic web is this ridiculous notion that it is machine readable just because it has an unambiguous grammar to parser and has ontologies to disambiguate homonyms.

SeanLuke 3y ago
That's not what the semantic web is about at all. It has nothing to do with text.
- KrugerDunnings 3y ago
  Chapter 2 Verse 1: https://www.w3.org/TR/rdf11-primer/ https://www.w3.org/TR/rdf11-primer/
  - SeanLuke 3y ago
    Thanks, I was rather involved in the research community. It has little to do with parsing text NLP-style. It has everything to do with annotating documents with formally structured KR data, or embedding the same into them. In fact, my own involvement in the field started with Jim Hendler suggesting I look into the possibility of developing a spider that would wander across HTML pages and glean knowledge from them with an NLP technique. I worked on that for a while, then abandoned it and proposed to him instead that web pages should be marked up with formally parseable structured semantic data. Why parse a student's page saying he "Goes to U Maryland" when he could just write <claim obj1="me" obj2="UMaryland" rel="attend" ontology="http://ontology.org/university-ontology/ http://ontology.org/university-ontology/">. That was 1995.
    - KrugerDunnings 3y ago
      Well I am at least glad that someone name drops someone other then TBL.
      - SeanLuke 3y ago

dimal 3y ago

Is anyone actually doing the semantic web anymore? I thought people gave up on that over a decade ago. It’s hard enough to get devs to just make content semantic enough for accessibility, let alone defining semantics for computers.

tracerbulletx 3y ago
The closest practical application that's actually happening I think would be Google's Structured Data guidelines using schema.org types and JSON-LD to power a bunch of enhanced search display features for Rich Results, and for powering Google Shopping. Compliance with that is pretty ubiquitous for Search optimization reasons.

valine 3y ago

Why is no one mentioning the hardware requirements to run each? The semantic version runs with 2KB of RAM and takes a couple of microseconds. The LLM needs at least 30GB of RAM and maybe 5 seconds of processing time.

pjc50 3y ago
Nobody cares because the semantic web results are nowhere near as interesting as the "AI" results.

hosh 3y ago

I think if anything, Semantic Web makes more sense with AI, not less. Not necessarily that we mark up web pages, but that formally defining an ontology that can interlink with other ontologies can help define the cultural boundaries we are seeking with ethical and safe use of AI. For example, there is an old effort called Cyc, which is an ontology and knowledge base of common sense. Something like this can be used to align LLMs. However, what is common sense is not necessarily universal across cultures. The underlying ideas behind semantic web can be used for ontologies that don’t agree with each other.

tracerbulletx 3y ago

If anything I think that LLMs are making the Semantic Web realizable in a new way. LLM can summarize the raw content down to a schematized representation, that you can then reference to express deterministic programmatic actions using the resulting properties. You still might want to do that rather than let the LLM do everything.

jimwhite 3y ago

Humans don't need the Semantic Web, AI/bots do. It is expensive to turn text into the useful semantic bits that are useful for doing computations on large (or even moderate) quantities of data. So the AI will create a new Semantic Web3 that any bot (or human) can contribute to on any topic (especially about anything on the web).

amanzi 3y ago

> As much as I love the semantic web, it is hard for humans to write, update, and maintain. Of course, ChatGPT can help a human to write the tags to put on the website: > Me: I have a shop that was the following opening hours: "Our opening hours are: Weekdays 10 until 7. Weekend 10 until 10 (Early closing 9 o'clock Sunday)." I would like to include these opening hours on the website of the shop using meta tags, so that search engines can easily read and parse them. > ChatGPT 3.5:  <meta itemprop="openingHours" content="Mo-Fr 10:00-19:00"> <meta itemprop="openingHours" content="Sa 10:00-22:00"> <meta itemprop="openingHours" content="Su 10:00-21:00"> (ChatGPT actually responded with a lot more text and code in the response, but I just quoted the relevant bit.)

PeterStuer 3y ago

EU spent so many research Euros on Semantic Web projects. They all went something like this: CS researchers: Hey guys, here's the niche marginal improvement our grad student spent 200 hours developing. Here are the guidelines for you domain experts on how to encode and load your knowledge. START! Domain experts: euh ... what you are asking us to do would take 9 manyears for the initial base and a 2 FTE upkeep. Why exactly would we do this? CS researchers: not my problem. Paper has been submitted and accepted. Your question was out of scope. Want to join our next proposal?