Re: Fwd: Semantic web vs big data / ML

I've added a link to the new Wiki page at 
https://www.w3.org/community/aikr/wiki/Concept_Maps

Paola Di Maio wrote:
> of interest?
>
> ---------- Forwarded message ----------
> From: *Martynas Jusevičius* <martynas@atomgraph.com 
> <mailto:martynas@atomgraph.com>>
> Date: Sat, Aug 4, 2018 at 3:06 PM
> Subject: Re: Semantic web vs big data / ML
> To: Phillip Rhodes <motley.crue.fan@gmail.com 
> <mailto:motley.crue.fan@gmail.com>>
> Cc: Michael F Uschold <uschold@gmail.com <mailto:uschold@gmail.com>>, 
> John Leonard <john.leonard@incisivemedia.com 
> <mailto:john.leonard@incisivemedia.com>>, "semantic-web@w3.org 
> <mailto:semantic-web@w3.org>" <semantic-web@w3.org 
> <mailto:semantic-web@w3.org>>
>
>
> 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 
> <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 
> <mailto: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://en.wikipedia.org/wiki/Ontology_learning>
>
>     https://pdfs.semanticscholar.org/8198/9605fba59a415a28603ce709566cc6ebc45c.pdf
>     <https://pdfs.semanticscholar.org/8198/9605fba59a415a28603ce709566cc6ebc45c.pdf>
>
>     https://arxiv.org/pdf/1311.1764.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 <mailto: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
>     <mailto: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
>     <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
>     <http://www.linkedin.com/in/michaeluschold>
>     >    Skype, Twitter: UscholdM
>     >
>     >
>     >
>
>

-- 
Regards

Chris
++++

Chief Executive, Lacibus <https://lacibus.net> Ltd
chris@lacibus.net