5 ms·

I wish all of you not to fall in the trap of ontologies. I worked very hard in this domain my conclusion is that all ontologies fail to scale eventually. I woul

I wish all of you not to fall in the trap of ontologies. I worked very hard in this domain my conclusion is that all ontologies fail to scale eventually. I would recommend people in the field to go towards "perspectivism".

hnxs 5y ago
I’d like to read more about what you worked on specifically if you’re willing to share!
Veuxdo 5y ago
Is this just a way of saying that no relations are absolute?
- bluecerulean 5y ago
  He most likely means that reasoners and databases that provide reasoning abilities do not scale. This makes sense, specially for OWL ontologies. For most OWL reasoners, if you feed them with the ontology and with a large set of instance data (class instances connected by edges that are labeled with properties defined in said ontology), it will likely take way more time than you would like to produce results (if it produces something). The reason for that is twofold: 1. Many of tools created for reasoning are research-first tools. Some papers were published about the tool and it really was a petter and more scalable tool than anything before it. But every PhD student graduates and needs to find a job or move to the next hyped research area 2. Tools are designed under the assumption that the whole ontology, all the instance data and all results fit in main memory (RAM). This assumption is de-facto necessary for more powerful entailment regimes of OWL. Reason 2 as a secondary sub-reason that OWL ontologies use URIs (actually IRIs), which are really inneficient identifiers compared to 32/64-bit integers. HDT is a format that fixes this inneficiency for RDF (and thus is applicable to ontologies) but since it came about nearly all reasoners where already abandoned as per reason #1 above. Newer reasoners that actually scale quite a bit are RDFox [1] and VLog [2]. They use compact representations and try to be nice with the CPU cache and pipeline. However, they are limited to a single shared memory (even if NUMA). There is a lot of mostly academic distributed reasoners designed to scale horizontally instead of vertically. These systems technically scale, but vertically scaling the centralized aforementioned systems will be more efficient. The intrinsic problem with distributing is that (i) it is hard to partition the input aiming at a fair distribution of work and (ii) inferred facts derived at one node often are evidence that multiple other nodes need to known. loose from modern single-node However, the problem of computing all inferred edges from a knowledge graph involves a great deal of communication, since one inference found by one node is evidence required by another processing node. [1]: https://www.oxfordsemantic.tech/product https://www.oxfordsemantic.tech/product [2]: https://github.com/karmaresearch/vlog/ https://github.com/karmaresearch/vlog/
omginternets 5y ago
Could you (please, pretty please) elaborate?
- whatshisface 5y ago
  There's no such thing in practice as "California is in the US." What you can get in reality is "Alice told the computer in 2008 that Bob wrote in his 1999 book that California was in the US in 1470BC." Perspectivism is the understanding that it's impossible to interpret semantic "knowledge" without knowing the limitations and implicit context carried with the fallible, partial transcription of the truth (set in a world that obeys quantum mechanics, for one thing) into words (which run to about 1MB tops before the author gets bored.)
  - astrange 5y ago
    > partial transcription of the truth (set in a world that obeys quantum mechanics, for one thing) Quantum mechanics actually says there's less information needed to describe X area of space than classical physics implies there is. https://en.wikipedia.org/wiki/Bekenstein_bound https://en.wikipedia.org/wiki/Bekenstein_bound
    - whatshisface 5y ago
      Both limits are unimaginably far above the amount of text you could reasonably write to describe something.
quag 5y ago
Is this[1] an example of ontological perspectivism? Can you point us at a good place to start? [1]: https://link.springer.com/article/10.1007/s11406-021-00371-1 https://link.springer.com/article/10.1007/s11406-021-00371-1
- mistrial9 5y ago
  great start - I have presented this point of view myself.. no clue what "perspectivism" means really, though
  - chestervonwinch 5y ago
    I'm not understanding what you mean. The latter portion of your comment seems to disagree with the former.
cratermoon 5y ago
As Clay Shirky said,"Ontology is Overrated" https://oc.ac.ge/file.php/16/_1_Shirky_2005_Ontology_is_Overrated.pdf https://oc.ac.ge/file.php/16/_1_Shirky_2005_Ontology_is_Over... See also his talk https://www.youtube.com/watch?v=ujMgQqp8YSY https://www.youtube.com/watch?v=ujMgQqp8YSY
- specialist 5y ago
  Seconding the wisdom shared by u/julienreszka and you. Stuff like schemas and data dictionaries and reuse are chinese finger traps for us geeks. Exquisite problems we can't look away from. I eventually decided to treat most data ingestion (ETL) as screen scraping. Honoring Postel's Law. Pull out the interesting relevant bits as needed. Ignore the rest. There's still an internal model, natch. But it's the smallest, most obvious model to support my immediate use cases. Nothing more.
0xbadcafebee 5y ago
I don't think they're a trap if you admit ahead of time that the relevant knowledge and its associations are limited. You always have to qualify the result in context. I agree that they won't scale perfectly, but they're still a useful tool in many cases.
ta988 5y ago
Gene Ontology is a pretty useful tool. What are you referring to?
buildingmateri 5y ago
And how does "perspectivism" somehow scale? Any NLP application directly encoding knowledge (objects, perspectives, whatever) is going to have major scaling constraints, since it's impossible right now to encode human-level understanding automatically.
rambojazz 5y ago
> I would recommend people in the field to go towards "perspectivism". what does this mean in this context?

hnxs 5y ago

I’d like to read more about what you worked on specifically if you’re willing to share!

Veuxdo 5y ago

Is this just a way of saying that no relations are absolute?

bluecerulean 5y ago
He most likely means that reasoners and databases that provide reasoning abilities do not scale. This makes sense, specially for OWL ontologies. For most OWL reasoners, if you feed them with the ontology and with a large set of instance data (class instances connected by edges that are labeled with properties defined in said ontology), it will likely take way more time than you would like to produce results (if it produces something). The reason for that is twofold: 1. Many of tools created for reasoning are research-first tools. Some papers were published about the tool and it really was a petter and more scalable tool than anything before it. But every PhD student graduates and needs to find a job or move to the next hyped research area 2. Tools are designed under the assumption that the whole ontology, all the instance data and all results fit in main memory (RAM). This assumption is de-facto necessary for more powerful entailment regimes of OWL. Reason 2 as a secondary sub-reason that OWL ontologies use URIs (actually IRIs), which are really inneficient identifiers compared to 32/64-bit integers. HDT is a format that fixes this inneficiency for RDF (and thus is applicable to ontologies) but since it came about nearly all reasoners where already abandoned as per reason #1 above. Newer reasoners that actually scale quite a bit are RDFox [1] and VLog [2]. They use compact representations and try to be nice with the CPU cache and pipeline. However, they are limited to a single shared memory (even if NUMA). There is a lot of mostly academic distributed reasoners designed to scale horizontally instead of vertically. These systems technically scale, but vertically scaling the centralized aforementioned systems will be more efficient. The intrinsic problem with distributing is that (i) it is hard to partition the input aiming at a fair distribution of work and (ii) inferred facts derived at one node often are evidence that multiple other nodes need to known. loose from modern single-node However, the problem of computing all inferred edges from a knowledge graph involves a great deal of communication, since one inference found by one node is evidence required by another processing node. [1]: https://www.oxfordsemantic.tech/product https://www.oxfordsemantic.tech/product [2]: https://github.com/karmaresearch/vlog/ https://github.com/karmaresearch/vlog/

omginternets 5y ago

Could you (please, pretty please) elaborate?

whatshisface 5y ago
There's no such thing in practice as "California is in the US." What you can get in reality is "Alice told the computer in 2008 that Bob wrote in his 1999 book that California was in the US in 1470BC." Perspectivism is the understanding that it's impossible to interpret semantic "knowledge" without knowing the limitations and implicit context carried with the fallible, partial transcription of the truth (set in a world that obeys quantum mechanics, for one thing) into words (which run to about 1MB tops before the author gets bored.)
- astrange 5y ago
  > partial transcription of the truth (set in a world that obeys quantum mechanics, for one thing) Quantum mechanics actually says there's less information needed to describe X area of space than classical physics implies there is. https://en.wikipedia.org/wiki/Bekenstein_bound https://en.wikipedia.org/wiki/Bekenstein_bound
  - whatshisface 5y ago
    Both limits are unimaginably far above the amount of text you could reasonably write to describe something.

quag 5y ago

Is this[1] an example of ontological perspectivism? Can you point us at a good place to start? [1]: https://link.springer.com/article/10.1007/s11406-021-00371-1 https://link.springer.com/article/10.1007/s11406-021-00371-1

mistrial9 5y ago
great start - I have presented this point of view myself.. no clue what "perspectivism" means really, though
- chestervonwinch 5y ago
  I'm not understanding what you mean. The latter portion of your comment seems to disagree with the former.

cratermoon 5y ago

As Clay Shirky said,"Ontology is Overrated" https://oc.ac.ge/file.php/16/_1_Shirky_2005_Ontology_is_Overrated.pdf https://oc.ac.ge/file.php/16/_1_Shirky_2005_Ontology_is_Over... See also his talk https://www.youtube.com/watch?v=ujMgQqp8YSY https://www.youtube.com/watch?v=ujMgQqp8YSY

specialist 5y ago
Seconding the wisdom shared by u/julienreszka and you. Stuff like schemas and data dictionaries and reuse are chinese finger traps for us geeks. Exquisite problems we can't look away from. I eventually decided to treat most data ingestion (ETL) as screen scraping. Honoring Postel's Law. Pull out the interesting relevant bits as needed. Ignore the rest. There's still an internal model, natch. But it's the smallest, most obvious model to support my immediate use cases. Nothing more.

0xbadcafebee 5y ago

I don't think they're a trap if you admit ahead of time that the relevant knowledge and its associations are limited. You always have to qualify the result in context. I agree that they won't scale perfectly, but they're still a useful tool in many cases.

ta988 5y ago

Gene Ontology is a pretty useful tool. What are you referring to?

buildingmateri 5y ago

And how does "perspectivism" somehow scale? Any NLP application directly encoding knowledge (objects, perspectives, whatever) is going to have major scaling constraints, since it's impossible right now to encode human-level understanding automatically.

rambojazz 5y ago

> I would recommend people in the field to go towards "perspectivism". what does this mean in this context?