Re: [DL-Learner discussion] Questions on DL-Learner

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Hi Céline,

can you check if the latest version dllearner-1.0-beta-3 
<http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-3.zip/download> 
which is downloadable at 
https://sourceforge.net/projects/dl-learner/files/DL-Learner/ works for you?

Regards,
Lorenz
On 04/29/2014 09:48 AM, alec wrote:
> Hi Lorenz,
>
> Thanks for your answer, you were right, it was not the latest version.
> I tried the latest version on my "father_test" example. I got that:
>
> ------------------------------------------------------
> DL-Learner command line interface
> Initializing Component "OWL File"... OK (0ms)
> Initializing Component "fast instance checker"... OK (490ms)
> Initializing Component "PosNegLPStandard"... OK (0ms)
> Initializing Component "CELOE"... OK (20ms)
> Initializing Component "PCELOE"... OK (0ms)
> Running algorithm instance "alg1"(CELOE)
> more accurate (50,00%) class expression found: Thing
> more accurate (83,33%) class expression found: male
> Algorithm terminated successfully (time: 10s 0ms, 373537 descriptions 
> tested, 22
> 1907 nodes in the search tree).
>
> number of retrievals: 6
> retrieval reasoning time: 0ms ( 0ms per retrieval)
> number of instance checks: 4145956 (0 multiple)
> instance check reasoning time: 452ms ( 0ms per instance check)
> (complex) subsumption checks: 266 (0 multiple)
> subsumption reasoning time: 96ms ( 0ms per subsumption check)
> overall reasoning time: 549ms
>
> solutions:
> 1: male (pred. acc.: 83,33%, F-measure: 85,71%)
> 2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
> 3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
> 4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
> 5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
>
> Running algorithm instance "alg2"(PCELOE)
> more accurate (50,00%) class expression found: Thing
> more accurate (83,33%) class expression found: male
> Algorithm terminated successfully (time: 10s 52ms, 516119 descriptions 
> tested, 3
> 28678 nodes in the search tree).
>
> number of retrievals: 12
> retrieval reasoning time: 0ms ( 0ms per retrieval)
> number of instance checks: 10208696 (0 multiple)
> instance check reasoning time: 1s 111ms ( 0ms per instance check)
> (complex) subsumption checks: 552 (0 multiple)
> subsumption reasoning time: 163ms ( 0ms per subsumption check)
> overall reasoning time: 1s 274ms
>
> solutions:
> 1: male (pred. acc.: 83,33%, F-measure: 85,71%)
> 2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
> 3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
> 4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
> 5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
> -------------------------------------------------------------
>
> So, there is no solution with something like "hasAge integer[>=28]". 
> Maybe, I did not use the correct parameters. I used the same file than 
> "father.conf" (I just changed ks.fileName).
>
> Regards,
> Céline.
>
>
>
> Le 12.04.2014 11:51, Lorenz Bühmann a écrit :
>> Hi,
>>
>>  which version if DL-Learner do you use? The latest version online is
>>
>> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download 
>>
>> [7] but we plan to upload a new one as there are many new features and
>> bugfixes in the current SVN version.
>>  Form the error message, I assume you use a quite old version. Can you
>> try the latest version?
>>
>>  According to the modified example:
>>
>>  You have as positive examples:
>>  stefan -> male, 28
>>  markus -> male, 50
>>  martin -> male, 34
>>  and negative examples:
>>  heinz -> male, 17
>>  anna -> female, 10
>>  michelle -> female, 4
>>
>>  So I guess you want to learn something like
>>  "male AND hasAge integer[>=34]" ?
>>
>>  I'll check if this works, but it should be possible.
>>
>>  Regards,
>>  Lorenz
>>
>> On 04/10/2014 10:53 AM, alec wrote:
>>
>>> Hi Lorenz,
>>>
>>> Thank you very much for your answers.
>>> I'm planning to use DL-Learner to learn concept definitions from an
>>> ontology of holiday destinations (I don't have the ontology yet). I
>>> want to make sure it is possible to get definitions with
>>> inferiority/superiority signs (about numerical datatype properties
>>> not about cardinality restrictions).
>>> For example, I would like to get something like that:
>>> "Definition of a destination which is hot in Winter:
>>> hasJanuaryTemperature x and
>>> hasFebruaryTemperature y and
>>> hasMarchTemperature z and
>>> x>20 and
>>> y>20 and
>>> z>20".
>>>
>>> I tried to modify the "father.owl" file (see attachments) in
>>> DL-Learner examples. I put a "hasAge" datatype property and I
>>> deleted "hasChild". I was hoping to see if I could get a definition
>>> with a superiority/inferiority sign about age. I got that:
>>>
>>> DL-Learner 2010-08-07 command line interface
>>> starting component manager ... OK (82ms)
>>> initialising component "OWL file" ... OK (0ms)
>>> initialising component "fast instance checker" ... OK (388ms)
>>> initialising component "pos neg learning problem" ... OK (0ms)
>>> initialising component "OCEL" ... OK (14ms)
>>>
>>> starting top down refinement with: Thing (50% accuracy)
>>> more accurate (83,33%) class expression found: male
>>> Exception in thread "main" java.lang.OutOfMemoryError: GC overhead
>>> limit exceeded
>>> at java.util.LinkedList.linkLast(Unknown Source)
>>> at java.util.LinkedList.add(Unknown Source)
>>> at java.util.LinkedList.clone(Unknown Source)
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:474)
>>>
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:498)
>>>
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:470)
>>>
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:413)
>>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:551) 
>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521) 
>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLearner2.start(ROLearner2.java:436) 
>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java:441) 
>>
>>> at org.dllearner.cli.Start.start(Start.java:347)
>>> at org.dllearner.cli.Start.main(Start.java:209)
>>>
>>> Kind regards,
>>> Céline
>>>
>>> Le 10.04.2014 00:02, Lorenz Bühmann a écrit :
>>>
>>>> Hi Céline,
>>>>
>>>> of course we can give you more information about DL-Learner if
>>>> you're
>>>> interested in.
>>>>
>>>> 1.) I'm not exactly sure what you mean by target language, but if
>>>> if you
>>>> refer to what's the expressivity of the learned class
>>>> expressions, then
>>>> no, the target language of DL-Learner is not ALC.
>>>> Depending on the used learning algorithm, DL-Learner of course
>>>> supports
>>>> datatype properties and for example can also learn class
>>>> expressions
>>>> which consist of constructs used in Description Logics beyond
>>>> ALC, like
>>>> for example qualified cardinality restrictions(Q).
>>>>
>>>> 2.) see 1.)
>>>>
>>>> 3.) We do not have any numbers, but in general the internally
>>>> used OWL
>>>> reasoner(e.g. Pellet or HermiT) might be a bottleneck. If you're
>>>> able to
>>>> just load the necessary part of the ontology, this can of course
>>>> positively influence the learning process. Maybe we're both
>>>> taking about
>>>> different things when using the term "noise", but I wouldn't
>>>> declare
>>>> unnecessary information as noise.
>>>>
>>>> 4.) Limits in which sense?
>>>>
>>>> Can you give us any insights into what you're planning to do with
>>>> the
>>>> DL-Learner?
>>>>
>>>> Kind regards,
>>>> Lorenz
>>>> On 04/08/2014 09:00 AM, alec wrote:
>>>>
>>>>> Hello,
>>>>>
>>>>> I am a PhD student in Laboratoire de Recherche en Informatique
>>>>> in
>>>>> Université Paris Sud (France). I have read papers on
>>>>> DL-Learner. For my
>>>>> thesis project, I might be interested in using an ILP tool to
>>>>> learn
>>>>> concept definitions. But the ontology I will use as input will
>>>>> have
>>>>> datatype properties (numerical values) and I would like to use
>>>>> a tool
>>>>> which can learn
>>>>> definitions using these datatype properties.
>>>>>
>>>>> I would like to have some additional information on DL-Learner
>>>>> if it is
>>>>> possible. I would be grateful if you could answer my questions.
>>>>>
>>>>>
>>>>> 1. I understood that the target language of your algorithm is
>>>>> ALC
>>>>> description logic. Can you confirm me that we cannot get a
>>>>> definition of
>>>>> a concept with datatype properties (other than string datatype
>>>>> properties)?
>>>>> For example, something like an adult is a person whose age
>>>>> hasValue x
>>>>> with x>=18.
>>>>>
>>>>> 2. If I understood right:
>>>>> Is there a particular reason for that? Has it a real complexity
>>>>> to
>>>>> implement? Or do you know tools (open source or free of charge
>>>>> for
>>>>> academic research) that can generate a definition with
>>>>> numerical
>>>>> datatype properties (e.g. in SHOIN(D) description logic)?
>>>>>
>>>>> 3. Are there any constraints about the input ontology? Can it
>>>>> be a big
>>>>> ontology with potential information which is not interesting
>>>>> for
>>>>> defining a concept (i.e. with noise)? Or has it to be just the
>>>>> interesting part of the ontology?
>>>>>
>>>>> 4. Can you say what the limits of DL-Learner are?
>>>>>
>>>>> I would greatly appreciate any help you might be able to give
>>>>> me.
>>>>>
>>>>> Best regards,
>>>>> Céline Alec
>>>>>
>>>>>
>>>>
>>>
>>
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>>
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>> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download 
>>
>
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