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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".
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".
[1]: https://link.springer.com/article/10.1007/s11406-021-00371-1
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".
Is this just a way of saying that no relations are absolute?
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 [2]: https://github.com/karmaresearch/vlog/
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".
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
KG are cool, but I haven't find a practical framework of combining simple logical predicates with temporal facts (things that are true at a certain moment in time) and information provenance (the truthiness of information given the origin). There might be ways to encode this information in a hyper graph but they are far from practical.
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".
Could you (please, pretty please) elaborate?
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.)
KG are cool, but I haven't find a practical framework of combining simple logical predicates with temporal facts (things that are true at a certain moment in time) and information provenance (the truthiness of information given the origin). There might be ways to encode this information in a hyper graph but they are far from practical.
Wikidata statements (which roughly correspond to the edges in the Knowledge Graph) have quite a bit of Metadata associated with them: they can have refer to sources that state this particular bit of knowledge, they have a so-called rank that allows distinguishing preferred and deprecated statements, and the can be qualified by another statement in the graph. Temporal validity is encoded using a combination of rank an…
https://en.wikipedia.org/wiki/Wikidata
Thanks for that! TIL.
It seems a fascinating project in epistemiology!
Earlier quoted context omitted.
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
great start - I have presented this point of view myself.. no clue what "perspectivism" means really, though
The definition seems faulty to me, since the pair (E: subset(N × N), f: E → L) does not admit of multiple edges with different labels, connecting the same ordered pair of nodes. Of course this is most often allowed in practical KG's.
You could then choose to add labels as either an additional function label: E -> L, or as several graphs layered on top of one another (especially helpful when you view each graph as an arrow N -> N, which in turn makes more sense when you have several different classes of nodes).
If anyone's interested I'm loosely basing this description on the description of petri nets as outlined in Tai-Danae Bradley's interesting (and readable) Applied Category Theory: https://arxiv.org/pdf/1809.05923.pdf