Re: Semantic web vs big data / ML

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
>
>
>

Received on Friday, 3 August 2018 23:15:59 UTC