Re: Semantic web vs big data / ML

This might be of interest to you:
https://developer.amazon.com/blogs/alexa/post/29f92b4d-1369-4d22-8494-7c4cc57650a3/amazon-scientists-to-present-more-sophisticated-semantic-representation-language-for-alexa

Alexa Meaning Representation Language is using RDF ontologies. That is
mentioned in the PDF article - link in the bottom of the post.
On Sat, 4 Aug 2018 at 02.19, Phillip Rhodes <motley.crue.fan@gmail.com>
wrote:

> There is, indeed, an entire field - and body of research - on
> "ontology learning".  See, for ref:
>
> https://en.wikipedia.org/wiki/Ontology_learning
>
>
> https://pdfs.semanticscholar.org/8198/9605fba59a415a28603ce709566cc6ebc45c.pdf
>
> https://arxiv.org/pdf/1311.1764.pdf
>
> etc...
>
>
> Phil
>
> This message optimized for indexing by NSA PRISM
>
>
> On Fri, Aug 3, 2018 at 6:57 PM, Michael F Uschold <uschold@gmail.com>
> wrote:
> > This is a very good question. I agree with other responses that ML and
> the
> > Semantic Web technologies are not in competition, they do very different
> > things. The question is whether and how they can work together in
> concert.
> > ML is used in a lot of NLP which is, in turn, used to extract triples
> from
> > text.  The state of the art here is improving all the time, but it is
> still
> > not that great. A common approach is for a NLP engine to take as input
> both
> > an ontology and a text document and to extract triples using the classes
> and
> > properties in the ontology. This works best if the ontology has a lot of
> > text descriptions of the classes and properties. I'm not sure how much
> these
> > triple-extractors make use of the ontology axioms, probably they use the
> > class hierarchy, and property domains and ranges.
> >
> > Another interesting possibility, which I have not seen much written is
> using
> > ontology vocabulary to express the features that are learned.  I'm not
> an
> > ML person, but in ML they talk about models, which to some extent
> describe
> > the kinds of things in the subject area and their attributes/features.
> > Probably someone has looked into this.
> >
> > Probably the most common use of ML is to put things into pre-determined
> > categories. But its just statistics. There is no human-grokkable way to
> > explain why something goes into one category vs. another.  It would be
> nice
> > if there was a way to do that, in terms of an ontology classes and
> > properties. Don't know if that is being done.
> >
> > Another possible link up is where ML is used to do automatic creation of
> > categories. Humans can look at the categories and give them meaningful
> > names, but to the computer they are just the result of statistics, they
> have
> > no meaning. It could be that meaning of these categories could be
> inferred
> > and matched against an ontology. General topics would probably start with
> > the DBpedia ontology.
> >
> > Way back in 2005 there was a Dagstuhl workshop on ML and the Semantic
> Web;
> > but there is not a lot of documentation on that event.
> >
> > Michael
> >
> >
> >
> >
> >
> >
> > On Fri, Aug 3, 2018 at 4:30 AM, John Leonard
> > <john.leonard@incisivemedia.com> wrote:
> >>
> >> Can someone please fill me in on the latest state of play between the
> >> symantec web and using machine learning techniques with massive
> unstructured
> >> datasets to derive probablistic links between data items. Are the two
> >> techniques in competition? Are they compatible? Or is it more a case of
> >> horses for courses? I refer to this 2009 paper
> >>
> https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf
> >> The Unreasonable Effectiveness of Data by Norvig et al.
> >> Thanks for any pointers
> >>
> >>
> >
> >
> >
> > --
> >
> > Michael Uschold
> >    Senior Ontology Consultant, Semantic Arts
> >    http://www.semanticarts.com
> >    LinkedIn: www.linkedin.com/in/michaeluschold
> >    Skype, Twitter: UscholdM
> >
> >
> >
>
>