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I enjoyed this piece, mostly because I disagree so strongly with it :-). Clay takes the topic of Ontology, contextualizes it into an impractical domain, and the

I enjoyed this piece, mostly because I disagree so strongly with it :-). Clay takes the topic of Ontology, contextualizes it into an impractical domain, and then calls it overrated. Cars are overrated too because they can't cross oceans right? When done correctly, ontology combined with knowledge graphs can make an impractical problem much more practical. Consider for the moment the problem of 'fake news'. What characterizes 'fake news' is that it has facts or concepts that are not in alignment with "true" facts (authoritative). And without getting into the debate of what is and isn't true, you can take a document, a knowledge base populated with trusted facts, and a topic ontology and score that document for alignment with the existing data set. Whether you use that for checking Wikipedia articles or flagging fake news its a very powerful combination.

AnimalMuppet 8y ago
I'm not sure about that. In Shannon's Information Theory, stuff that aligns with the existing knowledge base contains 0 bits of information. Things that contain new information cannot line up with the existing knowledge base; "news" is exactly the word for such things. You might be able to say that fake news doesn't align with true facts that aren't part of the facts being reported as news. That is you can tell that "the color of the sky unexpectedly changed from green to yellow yesterday" is fake news, but you can't use true facts to tell that ""the color of the sky unexpectedly changed from blue to yellow yesterday" is fake news. (I mean, yes, you might still use true facts about atmospheric composition, light scattering, and the spectrum of solar output, but you can't use "the sky is blue".)
- ChuckMcM 8y ago
  I think that is exactly right. So lets say you consider a news story as a 'stream' of bits. And you can demodulate those bits using some formula and there are enough extra bits in the message that you can correct for errors in the channel. If you get 5 streams of bits and four of them correlate with some epsilon number of new bits, and the fifth has 10x epsilon new bits, one of things is likely true. Either the odd stream out is corrupted or the stream has changed. Apply that to news stories where the ontology (O) and knowledge graph (KG) are combined to "error correct" the content of the story. "Error bits" would be defined as relationships that don't match what the ontology says can be true, or facts that are different than those facts already in the knowledge base. You can flag the odd news story as lacking alignment with the existing KG&O, further you can quantify that lack of alignment fairly precisely. So if the news story has 0 bits of information it correlates very strongly with other existing stories. If it has many bits of information it contrasts with other existing stories. At some number of stories on the same topic you can begin to reason about which are more likely 'true' and which are more likely 'false.'
  - whatshisface 8y ago
    Pure contrast isn't enough to hint at falsity, I could tell you a technical detail about what I did today and it would have a 0% overlap with any article on CNN.com. In addition, correlation with your pre-existing beliefs is not sufficient evidence for truth, because today you are wrong about at least a few things. If you look at any given point in history and assume you aren't too special, the first thing you'll realize is that one if not many of the things that are repeated daily in our society are almost certainly false if not outright lies: after all, that claim is true about every other historical period and people group, so why shouldn't it be true about us today? To suggest a specific example, imagine a person living inside WWII-era Germany correlating a propaganda news article with every other news article they have read, all of which are also propaganda. Correlation-based news epistemology isn't even based on a prevailing social consensus, it's based on the consensus of broadcasters.
    - AnimalMuppet 8y ago
      That is, whoever controls the corpus of "established true facts" controls the determination of whether something is or is not fake news. Which probably gets us to competing corpuses (corpi?) of "established true facts", run by those who push competing meta-narratives. Which looks rather similar to the current situation...
      - ChuckMcM 8y ago
        Exactly so. The operator of the machine gets to control what it is told is 'true' and 'false'. That said, assuming a nominally good actor and an inability to have people process every article, this process provides a means to quantitatively analyze for coherence with your controlled corpus.
        whatshisface 8y ago
        "Quantitatively analysing the coherence of a controlled corpus," is not exactly a solution to fake news, although it's probably useful for a few other things.
deleted 8y ago
[deleted]
fluorocarbon 8y ago
> Consider for the moment the problem of 'fake news' ... without getting into the debate of what is and isn't true This comment reminds me of another Shirky essay [1]. He says: > [This is] the pattern for descriptions of the Semantic Web. First, take some well-known problem. Next, misconstrue it so that the hard part is made to seem trivial and the trivial part hard. Finally, congratulate yourself for solving the trivial part. The difficult part is figuring out exactly what is and what isn't true. If we had a perfect map of reality it would be easy to detect fake news. In fact we wouldn't need any news at all or journalists or anyone else except the people who build the ontology. [1] http://www.shirky.com/writings/herecomeseverybody/semantic_syllogism.html http://www.shirky.com/writings/herecomeseverybody/semantic_s...
- taeric 8y ago
  That is now one of my favorite essays. Thanks for posting. It does amuse me how much this framing of problems happens. Both in the annoying cases of arm chair quarterbacks, and in the cases of folks that take on problems previously thought of as too difficult.
- ChuckMcM 8y ago
  I don't find this dismissal persuasive. If I understand you're point, you are arguing that without perfect truth, no partial solution has any value. If that is indeed your argument, it is remarkably similar to the argument "Perfect security is impossible, this system is worthless." I think it is demonstrably obvious that there is a related parameter in both cases, which is the cost of overcoming the system is greater than the gain from overcoming said system. As an example of how that argument fails in practice, consider textbooks. When I was working on this problem full time the organization had a relationship with a major publisher of textbooks. Consider the system which can construct an ontology and knowledge graph based on the contents of a text book. It may not be 'true' in the Aristotle sense, but it is an effective tool for evaluating whether or not a paper or essay correlates that ontology and knowledge graph or doesn't. In such a system there is value in evaluating the extent with which the knowledge in the textbook has been communicated to the person writing the essay. Being able to automate the analysis has other benefits as well which extend beyond flagging documents which disagree a previous established canon.
astazangasta 8y ago
>And without getting into the debate of what is and isn't true Nice try, but this is missing the whole point. This is the crux of the matter and the problem with the category of "fake news", and precisely why this ontology is garbage.
- heavenlyblue 8y ago
  I must absolutely disagree with you: in my opinion the problem of fake news stems from compartmentalisation of news sources (lack of their diversity), rather than lack of actual facts in the news sources. The fact that you can read about the same topic in several (more often opposing) ways is already a reason to red-flag the piece in the first place. This isn’t readily available through any of the current newspapers. Everyone has an opinion. I would happily pay for an aggregator that simply ejects facts (stated facts, not truths) from the stories. Then combines that with similar other pieces.
  - JetSpiegel 8y ago
    > I would happily pay for an aggregator that simply ejects facts There is already the AP news ticker, or Reuters. Bloomberg has the same thing for financial matters. If you read only that, you will die of boredom.
    - heavenlyblue 8y ago
      Well, truth is boring, isn't it? Would you otherwise prefer to read Russian propaganda speaking of German authorities policing Germans of Russian heritage? Because that's exactly what they are currently doing. 0 facts, amazing opinionated narrative in multi-paragraph form. Pictures on top of that. But that definitely sounds more fun. Moreover - that sort of narrative is way closer to the general sentiment of the working classes who are generally predisposed towards learning about how "the Man" is taking advantage of them in the first place.
      - JetSpiegel 8y ago
        I can't be so boring it leads to complete apathy. Unless you are a computer, there's a limit to the boredom you can take.
cdbattags 8y ago
I know this might be fuel to the fire on here, but I think Tanushree's work at Georgia Tech in her paper "A Parsimonious Language Model of Social Media Credibility Across Disparate Events" [1] is a good stab at the "fake news" problem with a lens of "credibility". As a disclaimer, we tried to get this model off the ground in YC summer 2017 batch but were rejected after the phone interview. I did not assist in the research; only an effort to publicize. I whole-heartedly believe this is our best bet at combatting "fake news" online for the moment. Taking the lessons of "The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive" [2], we might tease out credibility instead of fact or fiction to help guide our biased/opinionated labels. From the abstract: "In other words, the language used by millions of people on Twitter has considerable information about an event’s credibility. For example, hedge words and positive emotion words are associated with lower credibility" [1]: https://credcount.com/whitepaper.pdf https://credcount.com/whitepaper.pdf [2]: https://www.amazon.com/Most-Human-Artificial-Intelligence-Teaches/dp/0307476707 https://www.amazon.com/Most-Human-Artificial-Intelligence-Te...
- camelite 8y ago
  The resiliance of any fake news detection heuristic is impossible to prove until it is consequential enough to incentivise subversion.
- DoctorOetker 8y ago
  I think our best bet at combatting "fake news" is to repeal the censorship and copyright, and to formalize belief systems (this may seem like arduous work, and it is, but I believe machine learning will soon be able to formalize texts, science, physics, math to the point where we will have package managers for believe systems) Ten years ago, I would have said that this would not happen within 20 years, but now I am much less certain, due to a couple of profound breakthroughs: word embeddings (Glove etc), and their famous linear analogies, and sense disambiguation. These have not only been achieved in the emergent "look how amazingly bizarre" sense, but have over the last few years gotten a much more rigorous footing: notably the papers: [1] Linear Algebraic Structure of Word Senses, with Applications to Polysemy (RECOMMEND reading both first and last version, as some things are more lucid in the first version, and some in the last. The main takeaway is that the word vectors are linear combinations of a low number of sense vectors, and these sense vectors can be recovered by sparse coding) [2] Towards Understanding Linear Word Analogies (Which finally explains the "king" + "woman" - "man" = "queen" phenommena) Now consider physics for example, running text and equations, then consider * an "overscored n": is this 1) normal vector or 2) an antineutron or 3) ... ? * a greek beta symbol: is this 1) v/c as in relativity 2) kT as in thermmal or statistical physics or 3) ... ? * an e symbol: is this the exponent function, or the electron or ... ? With today's tools it should already be possible to make this: type a LaTeX formmula, or perhaps even a picture of a formula, then mouse-over an ambiguous symbol to see the sense of the symbol, or alternatively generate a wikipedia style list: "where ... is ..." It should be possible to do this for math as well, and pretty soon software should be able to formalize quasi-formal mathematics books, papers, derivations, proofs ... into MetaMath, and the beautiful part? MetaMath contains the ground truth upon verifying so it can keep training the formalizer while formalizing without supervision! The verifier will become the supervisor! Of course MetaMath will not tell you if your postulates and axioms are correct, but it will check if your reasoning is sound. The axioms or postulates will be what characterize the belief systems, and any rammifications or inconsistencies will be linked to in the "belief package" managers so that whenever you encounter a formalized claim you can then reply with how the claim does not follow in your own belief system, and it will hence become netiquette to not just share vague claims, but formalized ones and not just formalized ones but accompanying a reference to the claims proof and in which belief systems they hold, it will become much easier to identify exactly where people who disagree with you differ in assumptions or axioms, and hence easier to collaborate on identifying inconsistensies in the endless stream of forked belief systems to avoid being filtered by the reader's or platform's access to gossiped inconsistency proofs... A corollary is that the semi-crackpot semi-true realpolitik interpretations from the lower regions of society will force the higher regions of society to either formalize explicit injustice and oppression, or else democractically cede the definition of the boundary between just and unjust to the populace, so that we can have a formalized egalitarian society... Just like the printing press democratized communication, machine learning and formalization will democratize interpretation.
james_s_tayler 8y ago
I agree with you and I would like to point out if the 16th century Catholic church had used the same process they would have concluded that Copernicus was 'fake news'. It's a hard problem.
dithering 8y ago
Putting aside the difficulty of building that database of true facts, and of parsing unknown facts out of English text, that solution feels like it's inviting an arms race. I can write a bunch of uncontroversial "true" facts into an article containing one or two false facts.
- usrusr 8y ago
  Diluting lies with verifiable but only tangentially related truths is a well-established tactic to fooling humans as well. Someone with an agenda in the symbolic vs statistic AI question could take this parallel as an example of how close ontology-based AI approaches are to the way humans think. And then someone with the opposite agenda would point out that the example is all hypothetical.
afpx 8y ago
Can you point me to some examples where it’s been done correctly? I’ve followed this topic off and on for 20 years, and I’ve yet to see a big success. I’ve only ever seen this cycle: 1) Someone discovers ontology and rdf, 2) They implement some obvious examples, 3) They get hyped and tell everyone, 4) They try to generalize their examples to real-world problems, 5) They get silent and disappear.
ThomPete 8y ago
Ontology is a reduction of reality and shows the limits of our minds. Fake news is a great example of how sloppy categorization can end up being. Fake news is mostly about creating clickbaity stories which will make people share and thus increase advertising revenue, yet today most people think about it as some sort of political propaganda. Reality is more nuanced than our categories allow us to explore and often times they are limiting our ability to think about the world around us. Clays point sees to be that the value of information is not in categorizing it but allowing for new relationships to be made, which is exactly what ontology can often hinder.
barrkel 8y ago
Ontologies are problematic because they are almost invariably hierarchical and binary. If your ontology is probabilistic and multi-dimensional, it's much more likely to represent truth. I actually think ontologies are a trap for smart people. For people who like to systematize, they have an allure. Ontologies give you the illusion of putting the chaotic world into order. Even phrases like "true facts" - in reality, facts are hard to come by and change over time, and the closer you look at them the more woolly they get - predicates are far too abstract and simplistic, and when you dig in, the fractal messiness overwhelms.

AnimalMuppet 8y ago

I'm not sure about that. In Shannon's Information Theory, stuff that aligns with the existing knowledge base contains 0 bits of information. Things that contain new information cannot line up with the existing knowledge base; "news" is exactly the word for such things. You might be able to say that fake news doesn't align with true facts that aren't part of the facts being reported as news. That is you can tell that "the color of the sky unexpectedly changed from green to yellow yesterday" is fake news, but you can't use true facts to tell that ""the color of the sky unexpectedly changed from blue to yellow yesterday" is fake news. (I mean, yes, you might still use true facts about atmospheric composition, light scattering, and the spectrum of solar output, but you can't use "the sky is blue".)

ChuckMcM 8y ago
I think that is exactly right. So lets say you consider a news story as a 'stream' of bits. And you can demodulate those bits using some formula and there are enough extra bits in the message that you can correct for errors in the channel. If you get 5 streams of bits and four of them correlate with some epsilon number of new bits, and the fifth has 10x epsilon new bits, one of things is likely true. Either the odd stream out is corrupted or the stream has changed. Apply that to news stories where the ontology (O) and knowledge graph (KG) are combined to "error correct" the content of the story. "Error bits" would be defined as relationships that don't match what the ontology says can be true, or facts that are different than those facts already in the knowledge base. You can flag the odd news story as lacking alignment with the existing KG&O, further you can quantify that lack of alignment fairly precisely. So if the news story has 0 bits of information it correlates very strongly with other existing stories. If it has many bits of information it contrasts with other existing stories. At some number of stories on the same topic you can begin to reason about which are more likely 'true' and which are more likely 'false.'
- whatshisface 8y ago
  Pure contrast isn't enough to hint at falsity, I could tell you a technical detail about what I did today and it would have a 0% overlap with any article on CNN.com. In addition, correlation with your pre-existing beliefs is not sufficient evidence for truth, because today you are wrong about at least a few things. If you look at any given point in history and assume you aren't too special, the first thing you'll realize is that one if not many of the things that are repeated daily in our society are almost certainly false if not outright lies: after all, that claim is true about every other historical period and people group, so why shouldn't it be true about us today? To suggest a specific example, imagine a person living inside WWII-era Germany correlating a propaganda news article with every other news article they have read, all of which are also propaganda. Correlation-based news epistemology isn't even based on a prevailing social consensus, it's based on the consensus of broadcasters.
  - AnimalMuppet 8y ago
    That is, whoever controls the corpus of "established true facts" controls the determination of whether something is or is not fake news. Which probably gets us to competing corpuses (corpi?) of "established true facts", run by those who push competing meta-narratives. Which looks rather similar to the current situation...
    - ChuckMcM 8y ago
      Exactly so. The operator of the machine gets to control what it is told is 'true' and 'false'. That said, assuming a nominally good actor and an inability to have people process every article, this process provides a means to quantitatively analyze for coherence with your controlled corpus.
      - whatshisface 8y ago
        "Quantitatively analysing the coherence of a controlled corpus," is not exactly a solution to fake news, although it's probably useful for a few other things.

deleted 8y ago

[deleted]

fluorocarbon 8y ago

> Consider for the moment the problem of 'fake news' ... without getting into the debate of what is and isn't true This comment reminds me of another Shirky essay [1]. He says: > [This is] the pattern for descriptions of the Semantic Web. First, take some well-known problem. Next, misconstrue it so that the hard part is made to seem trivial and the trivial part hard. Finally, congratulate yourself for solving the trivial part. The difficult part is figuring out exactly what is and what isn't true. If we had a perfect map of reality it would be easy to detect fake news. In fact we wouldn't need any news at all or journalists or anyone else except the people who build the ontology. [1] http://www.shirky.com/writings/herecomeseverybody/semantic_syllogism.html http://www.shirky.com/writings/herecomeseverybody/semantic_s...

taeric 8y ago
That is now one of my favorite essays. Thanks for posting. It does amuse me how much this framing of problems happens. Both in the annoying cases of arm chair quarterbacks, and in the cases of folks that take on problems previously thought of as too difficult.
ChuckMcM 8y ago
I don't find this dismissal persuasive. If I understand you're point, you are arguing that without perfect truth, no partial solution has any value. If that is indeed your argument, it is remarkably similar to the argument "Perfect security is impossible, this system is worthless." I think it is demonstrably obvious that there is a related parameter in both cases, which is the cost of overcoming the system is greater than the gain from overcoming said system. As an example of how that argument fails in practice, consider textbooks. When I was working on this problem full time the organization had a relationship with a major publisher of textbooks. Consider the system which can construct an ontology and knowledge graph based on the contents of a text book. It may not be 'true' in the Aristotle sense, but it is an effective tool for evaluating whether or not a paper or essay correlates that ontology and knowledge graph or doesn't. In such a system there is value in evaluating the extent with which the knowledge in the textbook has been communicated to the person writing the essay. Being able to automate the analysis has other benefits as well which extend beyond flagging documents which disagree a previous established canon.

astazangasta 8y ago

>And without getting into the debate of what is and isn't true Nice try, but this is missing the whole point. This is the crux of the matter and the problem with the category of "fake news", and precisely why this ontology is garbage.

heavenlyblue 8y ago
I must absolutely disagree with you: in my opinion the problem of fake news stems from compartmentalisation of news sources (lack of their diversity), rather than lack of actual facts in the news sources. The fact that you can read about the same topic in several (more often opposing) ways is already a reason to red-flag the piece in the first place. This isn’t readily available through any of the current newspapers. Everyone has an opinion. I would happily pay for an aggregator that simply ejects facts (stated facts, not truths) from the stories. Then combines that with similar other pieces.
- JetSpiegel 8y ago
  > I would happily pay for an aggregator that simply ejects facts There is already the AP news ticker, or Reuters. Bloomberg has the same thing for financial matters. If you read only that, you will die of boredom.
  - heavenlyblue 8y ago
    Well, truth is boring, isn't it? Would you otherwise prefer to read Russian propaganda speaking of German authorities policing Germans of Russian heritage? Because that's exactly what they are currently doing. 0 facts, amazing opinionated narrative in multi-paragraph form. Pictures on top of that. But that definitely sounds more fun. Moreover - that sort of narrative is way closer to the general sentiment of the working classes who are generally predisposed towards learning about how "the Man" is taking advantage of them in the first place.
    - JetSpiegel 8y ago
      I can't be so boring it leads to complete apathy. Unless you are a computer, there's a limit to the boredom you can take.

cdbattags 8y ago

I know this might be fuel to the fire on here, but I think Tanushree's work at Georgia Tech in her paper "A Parsimonious Language Model of Social Media Credibility Across Disparate Events" [1] is a good stab at the "fake news" problem with a lens of "credibility". As a disclaimer, we tried to get this model off the ground in YC summer 2017 batch but were rejected after the phone interview. I did not assist in the research; only an effort to publicize. I whole-heartedly believe this is our best bet at combatting "fake news" online for the moment. Taking the lessons of "The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive" [2], we might tease out credibility instead of fact or fiction to help guide our biased/opinionated labels. From the abstract: "In other words, the language used by millions of people on Twitter has considerable information about an event’s credibility. For example, hedge words and positive emotion words are associated with lower credibility" [1]: https://credcount.com/whitepaper.pdf https://credcount.com/whitepaper.pdf [2]: https://www.amazon.com/Most-Human-Artificial-Intelligence-Teaches/dp/0307476707 https://www.amazon.com/Most-Human-Artificial-Intelligence-Te...

camelite 8y ago
The resiliance of any fake news detection heuristic is impossible to prove until it is consequential enough to incentivise subversion.
DoctorOetker 8y ago
I think our best bet at combatting "fake news" is to repeal the censorship and copyright, and to formalize belief systems (this may seem like arduous work, and it is, but I believe machine learning will soon be able to formalize texts, science, physics, math to the point where we will have package managers for believe systems) Ten years ago, I would have said that this would not happen within 20 years, but now I am much less certain, due to a couple of profound breakthroughs: word embeddings (Glove etc), and their famous linear analogies, and sense disambiguation. These have not only been achieved in the emergent "look how amazingly bizarre" sense, but have over the last few years gotten a much more rigorous footing: notably the papers: [1] Linear Algebraic Structure of Word Senses, with Applications to Polysemy (RECOMMEND reading both first and last version, as some things are more lucid in the first version, and some in the last. The main takeaway is that the word vectors are linear combinations of a low number of sense vectors, and these sense vectors can be recovered by sparse coding) [2] Towards Understanding Linear Word Analogies (Which finally explains the "king" + "woman" - "man" = "queen" phenommena) Now consider physics for example, running text and equations, then consider * an "overscored n": is this 1) normal vector or 2) an antineutron or 3) ... ? * a greek beta symbol: is this 1) v/c as in relativity 2) kT as in thermmal or statistical physics or 3) ... ? * an e symbol: is this the exponent function, or the electron or ... ? With today's tools it should already be possible to make this: type a LaTeX formmula, or perhaps even a picture of a formula, then mouse-over an ambiguous symbol to see the sense of the symbol, or alternatively generate a wikipedia style list: "where ... is ..." It should be possible to do this for math as well, and pretty soon software should be able to formalize quasi-formal mathematics books, papers, derivations, proofs ... into MetaMath, and the beautiful part? MetaMath contains the ground truth upon verifying so it can keep training the formalizer while formalizing without supervision! The verifier will become the supervisor! Of course MetaMath will not tell you if your postulates and axioms are correct, but it will check if your reasoning is sound. The axioms or postulates will be what characterize the belief systems, and any rammifications or inconsistencies will be linked to in the "belief package" managers so that whenever you encounter a formalized claim you can then reply with how the claim does not follow in your own belief system, and it will hence become netiquette to not just share vague claims, but formalized ones and not just formalized ones but accompanying a reference to the claims proof and in which belief systems they hold, it will become much easier to identify exactly where people who disagree with you differ in assumptions or axioms, and hence easier to collaborate on identifying inconsistensies in the endless stream of forked belief systems to avoid being filtered by the reader's or platform's access to gossiped inconsistency proofs... A corollary is that the semi-crackpot semi-true realpolitik interpretations from the lower regions of society will force the higher regions of society to either formalize explicit injustice and oppression, or else democractically cede the definition of the boundary between just and unjust to the populace, so that we can have a formalized egalitarian society... Just like the printing press democratized communication, machine learning and formalization will democratize interpretation.

james_s_tayler 8y ago

I agree with you and I would like to point out if the 16th century Catholic church had used the same process they would have concluded that Copernicus was 'fake news'. It's a hard problem.

dithering 8y ago

Putting aside the difficulty of building that database of true facts, and of parsing unknown facts out of English text, that solution feels like it's inviting an arms race. I can write a bunch of uncontroversial "true" facts into an article containing one or two false facts.

usrusr 8y ago
Diluting lies with verifiable but only tangentially related truths is a well-established tactic to fooling humans as well. Someone with an agenda in the symbolic vs statistic AI question could take this parallel as an example of how close ontology-based AI approaches are to the way humans think. And then someone with the opposite agenda would point out that the example is all hypothetical.

afpx 8y ago

Can you point me to some examples where it’s been done correctly? I’ve followed this topic off and on for 20 years, and I’ve yet to see a big success. I’ve only ever seen this cycle: 1) Someone discovers ontology and rdf, 2) They implement some obvious examples, 3) They get hyped and tell everyone, 4) They try to generalize their examples to real-world problems, 5) They get silent and disappear.

ThomPete 8y ago

Ontology is a reduction of reality and shows the limits of our minds. Fake news is a great example of how sloppy categorization can end up being. Fake news is mostly about creating clickbaity stories which will make people share and thus increase advertising revenue, yet today most people think about it as some sort of political propaganda. Reality is more nuanced than our categories allow us to explore and often times they are limiting our ability to think about the world around us. Clays point sees to be that the value of information is not in categorizing it but allowing for new relationships to be made, which is exactly what ontology can often hinder.

barrkel 8y ago

Ontologies are problematic because they are almost invariably hierarchical and binary. If your ontology is probabilistic and multi-dimensional, it's much more likely to represent truth. I actually think ontologies are a trap for smart people. For people who like to systematize, they have an allure. Ontologies give you the illusion of putting the chaotic world into order. Even phrases like "true facts" - in reality, facts are hard to come by and change over time, and the closer you look at them the more woolly they get - predicates are far too abstract and simplistic, and when you dig in, the fractal messiness overwhelms.