Thank you Lorenz. And sorry for the off-topic query.
<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=icon> Virus-free. www.avast.com <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=link> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> On Tue, Jan 23, 2018 at 10:59 AM, Lorenz Buehmann < [email protected]> wrote: > > The > > following is a paper which used "inconsistency detection" but not used > ML. > I know the authors and the paper. Your statement is not correct, in fact > they used machine learning to find new schema axioms to be added the > DBpedia ontology, in particular disjointness axioms which are quite > often a "good" source for inconsistency. > > There are also other groups that already used machine learning to > "replace" standard reasoning procedures. > > But again, it's too much off-topic here. > > > On 22.01.2018 17:56, javed khan wrote: > > Martin, thanks a lot. Its very useful for me.. > > > > I think it will be possible also to predict inconsistencies in ontologies > > (via machine learning). It could be my research project which is about to > > start but the problem is I cant find anything related on the web. The > > following is a paper which used "inconsistency detection" but not used > ML. > > > > [1] > > https://hpi.de/fileadmin/user_upload/fachgebiete/meinel/ > papers/Web_3.0/2012_Toepper_ISEM.pdf > > > > > > > > <https://www.avast.com/sig-email?utm_medium=email&utm_ > source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=icon> > > Virus-free. > > www.avast.com > > <https://www.avast.com/sig-email?utm_medium=email&utm_ > source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=link> > > <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > > > > On Mon, Jan 22, 2018 at 6:52 PM, Martin Vachovski < > > [email protected]> wrote: > > > >> Hi all, > >> > >> I have seen some papers on "ontology matching" > >> which is to say- apply a ML algorithm in order to "map" > >> the semantics of two different ontologies which apply to the same object > >> > >> https://homes.cs.washington.edu/~pedrod/papers/hois.pdf > >> http://disi.unitn.it/~p2p/RelatedWork/Matching/0411csit10.pdf > >> > >> While the examples are not exactly the ones seek by the question, they > >> show that the idea of combining of ML and semantic data storage is not > new > >> Hope that points towards the right direction > >> > >> Cheers > >> Martin > >> > >> > >> ________________________________________ > >> From: javed khan <[email protected]> > >> Sent: Monday, January 22, 2018 3:12 PM > >> To: [email protected] > >> Subject: Re: Rules and machine learning > >> > >> Thank you Lorenz.. Yes rules can not be consider machine learning as > its a > >> kind of hard coding and machine will not learn by itself.. > >> > >> > >> > >> <https://www.avast.com/sig-email?utm_medium=email&utm_ > >> source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=icon> > >> Virus-free. > >> www.avast.com > >> <https://www.avast.com/sig-email?utm_medium=email&utm_ > >> source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=link> > >> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > >> > >> On Mon, Jan 22, 2018 at 7:35 AM, Lorenz Buehmann < > >> [email protected]> wrote: > >> > >>> I've just some very minimal experience in machine learning and rule > >>> processing... > >>> > >>> The important keywords here are "machine" and "learning" - if you > >>> provide a set of rules, then there was no learning. Except by the human > >>> who used his/her knowledge to make the rules - if it's done by a > >>> "machine", then you can call this machine learning of such rules (e.g. > >>> rule induction) and use the rules to "infer" data - not predict. The > >>> rules are just a (human-readable) way to encode the machine learning > >> model. > >>> But, it's off-topic for sure, thus, I will not go further into details. > >>> > >>> > >>> Lorenz > >>> > >>> > >>> On 21.01.2018 14:50, javed khan wrote: > >>>> Hello > >>>> > >>>> I am not sure if the question is related to the jena group but I will > >>>> appreciate the answer. > >>>> > >>>> I want to ask is it possible we take the functionality of machine > >>> learning > >>>> techniques (bayes algorithm, decision tree etc) using semantic web > >>> rules. I > >>>> dont know much about machine learning but I know it makes prediction > >>> based > >>>> on past experience/past data. > >>>> > >>>> Like we provide set of rules based on past data (if this, then that) > >> and > >>>> make predictions/optimizations. For instance, we want to make bug > >>>> predictions in a software using Semantic rules, so is it possible?? > >>>> > >>>> Thank you > >>>> > >>>> <https://www.avast.com/sig-email?utm_medium=email&utm_ > >>> source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=icon> > >>>> Virus-free. > >>>> www.avast.com > >>>> <https://www.avast.com/sig-email?utm_medium=email&utm_ > >>> source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=link> > >>>> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > >>>> > >>> > >>> > >
