3 ms·

You're right, there are a lot of academics that like the idea of a semantic web, it rings true to a lot of scientific principles. There are also a lot of ideas

by xipho 8y ago

You're right, there are a lot of academics that like the idea of a semantic web, it rings true to a lot of scientific principles. There are also a lot of ideas and far fewer day-to-day applications. Science ticks a long a lot slower than startup culture, however, so it's not suprising that a) understanding of the issues comes slower, but also b) some "experiments" that would utilize semantic data have not yet been fully tested. Read a list of points that address "why the semantic web is dead", many of those points are precisely what science seeks, i.e. principles that promote a slow, and deep understanding of a domain of knowledge. With open-science mandates coming from governments around the world researchers are looking for ways to share their data in meaningful ways. I can think of a significant amount of research that regularly consumes RDF, particularly in the fields of medical biology and genomics where it's used to annotate data. This is where I'd guess you'll see it take a foothold, for example medical diagnosis codes are notoriously disparate and there is a strong appreciation for what semantics could address. Unify, exchange, and consume medical diagnoses ... proffit. Links etc. off the top of my head- * GO - The gene ontology, used in hundreds of thousands of genomic anotations https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944782/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944782/ * UBERON - https://genomebiology.biomedcentral.com/articles/10.1186/gb-2012-13-1-r5 https://genomebiology.biomedcentral.com/articles/10.1186/gb-... * The second year of US2TS - http://us2ts.org/2019/posts/registration.html http://us2ts.org/2019/posts/registration.html * OBO foundry - https://github.com/OBOFoundry/OBOFoundry.github.io https://github.com/OBOFoundry/OBOFoundry.github.io

degyves 8y ago
Even more, we're already there: US ONC Health IT and HL7 are currently on the FHIR standard, which is slowly integrating linked data principles. E.g. currently uses JSON-LD: https://www.healthit.gov/buzz-blog/interoperability/heat-wave-the-u-s-is-poised-to-catch-fhir-in-2019 https://www.healthit.gov/buzz-blog/interoperability/heat-wav...
mbrock 8y ago
I’ve been learning about Barry Smith’s project for a scientific base ontology, Basic Formal Ontology, and it’s really fascinating stuff.
- msamwald 8y ago
  I always found that in introduced unpragmatic, counterintuitive roadblocks for actual knowledge integration. The most annoying thing is how it carves up reality into representations of different granularity, which are then made disjoint.
  - xipho 8y ago
    One of the proposed selling points of representing your data semantically is that you can infer across it, disjoint assertions are pretty important in this regard. How disjoint assertions are used (Smith) is another issue. A related beef though, is that with any reasonable size dataset ontology-based inference is computationally very difficult, you have to cut all sorts of corners and know all sorts of tricks to actually infer across your data. In other words- if semantic data are going to truly become ubiquitous we need to infer in real time across them. Inference takes everything in your dataset into account, so adding a single axiom means if you want to be complete, you have to compute all over again -> slow.

by xipho 8y ago

degyves 8y ago

Even more, we're already there: US ONC Health IT and HL7 are currently on the FHIR standard, which is slowly integrating linked data principles. E.g. currently uses JSON-LD: https://www.healthit.gov/buzz-blog/interoperability/heat-wave-the-u-s-is-poised-to-catch-fhir-in-2019 https://www.healthit.gov/buzz-blog/interoperability/heat-wav...

mbrock 8y ago

I’ve been learning about Barry Smith’s project for a scientific base ontology, Basic Formal Ontology, and it’s really fascinating stuff.

msamwald 8y ago
I always found that in introduced unpragmatic, counterintuitive roadblocks for actual knowledge integration. The most annoying thing is how it carves up reality into representations of different granularity, which are then made disjoint.
- xipho 8y ago
  One of the proposed selling points of representing your data semantically is that you can infer across it, disjoint assertions are pretty important in this regard. How disjoint assertions are used (Smith) is another issue. A related beef though, is that with any reasonable size dataset ontology-based inference is computationally very difficult, you have to cut all sorts of corners and know all sorts of tricks to actually infer across your data. In other words- if semantic data are going to truly become ubiquitous we need to infer in real time across them. Inference takes everything in your dataset into account, so adding a single axiom means if you want to be complete, you have to compute all over again -> slow.