> 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> >>>> >>> >>>
