Re: Semantic web vs big data / ML

Download:
https://ai.google/research/pubs/pub34405

And see why we need category theory to sort out the many flavors of AI, ML, NLP and semantic technologies in the area of knowledge representation and processing. Milton Ponson
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    On Saturday, August 4, 2018 10:13 AM, Martynas Jusevičius <martynas@atomgraph.com> wrote:
 

 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

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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
>
>
>