Re: [DL-Learner discussion] DL-Learner solution not including object properties

[Lorenz B. <spo...@st...>]

Hi Kamruzzaman,


> Hi Lorenz,
>
> Many thanks for your reply. I am sorry that I attached wrong ontology
> file here.
>
>
> *Here I am attaching the corrected ontology file, corresponding
> configuration file and the corresponding solution file.*
>
>
> *I have did the same experiments and got similar result (solution not
> including object properties for father_3.conf problem, which is using
> father_3.owl ).*
>
no worries, thank you for the updated ontologies.
>
>  
>
> Is it the case that object properties need to be connected to all
> individuals/classes if we want a solution which includes that object
> property?
>
Well, there are two points you have to keep in mind for the current example:

1. DL-Learner tries to solve the learning problem such that a solution
covers all positive examples while not covering any negative example.
Especially the coverage of the positive examples is important, and
DL-Learner is very strict here in. Indeed, this assumes a perfect world
in which we always have perfect solutions - we know, that's usually not
the case, therefore, we have a configuration parameter called
"noisePercentage" which allows to lower the restrictions on the coverage
of the positive examples. For instance, if you add the following line to
the *.conf file

alg.noisePercentage = 30

DL-Learner will also return

Note, that in your example the concept "male" is already a perfect
solution, thus, it will probably not find any concept with a score lower
than 100% .

In any case, you should always set some value for the noise as long as
you don't know that the data is perfect and there must be a perfect
solution.


Hope this helps, but feel free to ask if you have any further questions.

Cheers,
Lorenz
>
>
> With Regards,
>
> -------------
>
> Md Kamruzzaman Sarker
>
> PhD Student
>
> Dept. of Computer Science and Engineering, Wright State University,
> Dayton, OH
>
> http://dase.cs.wright.edu/people/kamruzzaman-sarker
>
>
>
> ------------------------------------------------------------------------
> *From:* Lorenz B. <spo...@st...>
> *Sent:* Tuesday, May 2, 2017 5:38 AM
> *To:* dl-...@li...
> *Subject:* Re: [DL-Learner discussion] DL-Learner solution not
> including object properties
>  
> Hi,
>
> unfortunately your ontologies do not contain what you say, i.e.
>
> * father_2 is the same as father_0 and does not contain an additional
> class "transgender"
> * father_3 contains a class "transgender" but not the classes "human"
> and "animal" and furthermore not the class assertions human(zaman),
> animal(cow)
>
> This is especially problematic since your conf file states that there
> is a positive example "zaman".
> Note, "male" is already a trivial solution for your configuration of
> the learning problem file, thus, it's not that clear which other
> solution you expect.
>
>
> Cheers,
> Lorenz
>
>> Dear Concern,
>>
>> With due respect, I am exploring DL-Learner and running it for
>> Learning Problem (Class learning).
>>
>>
>> From the experiments I am doing, *I am not getting the solution which
>> includes objectProperties*.
>>
>> I am doing the experiments using the default father.conf (which using
>> father.owl file). 
>>
>> When using the default father.owl file the result includes *hasChild*
>> object properties.
>>
>> But if I include a couple of extra classes in ontology and run with
>> the same configuration file then the result does not include object
>> properties.
>>
>>
>> *Does it mean,*
>>
>> *If we need a solution including object properties, is it required
>> for every owlClass have to be related to the object property? *
>>
>> *
>> *
>>
>> The experiments result I have attached here, I have attached here the
>> configuration and ontology file here for reference. 
>>
>>
>> ##Original ontology given in example folder of DL-Learner. I renamed
>> it with father_0.owl
>>
>> solutions:
>> 1: male (pred. acc.: 100.00%, F-measure: 100.00%)
>> 2: male (pred. acc.: 100.00%, F-measure: 100.00%)
>> 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure:
>> 100.00%)
>> 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>> 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>> 7: (not (female)) or (hasChild some (not (male))) (pred. acc.:
>> 100.00%, F-measure: 100.00%)
>> 8: male or (hasChild some (hasChild some Thing)) (pred. acc.:
>> 100.00%, F-measure: 100.00%)
>> 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>> 10: male or (hasChild some (hasChild some female)) (pred. acc.:
>> 100.00%, F-measure: 100.00%)
>>
>>
>> ##father_2.owl 
>> ##original ontology with transgender class and individual boby
>>
>> solutions:
>> 1: male (pred. acc.: 100.00%, F-measure: 100.00%)
>> 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%)
>> 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure:
>> 100.00%)
>> 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 6: male or (hasChild some transgender) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>> 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure:
>> 100.00%)
>> 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>> 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>> 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%,
>> F-measure: 100.00%)
>>
>>
>> ##father_3.owl 
>> ##original ontology with transgender, human and animal class 
>> ## individual human(zaman) , individual animal(cow)
>>
>> solutions:
>> 1: male (pred. acc.: 100.00%, F-measure: 100.00%)
>> 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%)
>> 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%)
>> 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%)
>> 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure:
>> 100.00%)
>> 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%)
>> 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%)
>>  
>>
>>
>>
>> -------------
>>
>> Md Kamruzzaman Sarker
>>
>> PhD Student
>>
>> Dept. of Computer Science and Engineering, Wright State University,
>> Dayton, OH
>>
>> http://dase.cs.wright.edu/people/kamruzzaman-sarker
>>
>>
>>
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