dl-learner-discussion

[DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

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

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

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

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
>
> ------------------------------------------------------------------------------
> Put Bad Developers to Shame
> Dominate Development with Jenkins Continuous Integration
> Continuously Automate Build, Test & Deployment
> Start a new project now. Try Jenkins in the cloud.
> http://p.sf.net/sfu/13600_Cloudbees
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

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

[alec <Cel...@lr...>]

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
>>
>> 
>> ------------------------------------------------------------------------------
>> Put Bad Developers to Shame
>> Dominate Development with Jenkins Continuous Integration
>> Continuously Automate Build, Test & Deployment
>> Start a new project now. Try Jenkins in the cloud.
>> http://p.sf.net/sfu/13600_Cloudbees
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
> 
> ------------------------------------------------------------------------------
> Put Bad Developers to Shame
> Dominate Development with Jenkins Continuous Integration
> Continuously Automate Build, Test & Deployment
> Start a new project now. Try Jenkins in the cloud.
> http://p.sf.net/sfu/13600_Cloudbees
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

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

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

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 
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
>>>
>>>
>>> ------------------------------------------------------------------------------ 
>>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li...
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>
>>
>>
>> ------------------------------------------------------------------------------ 
>>
>> Put Bad Developers to Shame
>> Dominate Development with Jenkins Continuous Integration
>> Continuously Automate Build, Test & Deployment
>> Start a new project now. Try Jenkins in the cloud.
>> http://p.sf.net/sfu/13600_Cloudbees
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
> ------------------------------------------------------------------------------
> Put Bad Developers to Shame
> Dominate Development with Jenkins Continuous Integration
> Continuously Automate Build, Test & Deployment
> Start a new project now. Try Jenkins in the cloud.
> http://p.sf.net/sfu/13600_Cloudbees
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

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

[alec <Cel...@lr...>]

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
>>>>
>>>>
>>>
>>
> 
> ------------------------------------------------------------------------------
>>>>
>>>> Put Bad Developers to Shame
>>>> Dominate Development with Jenkins Continuous Integration
>>>> Continuously Automate Build, Test & Deployment
>>>> Start a new project now. Try Jenkins in the cloud.
>>>> http://p.sf.net/sfu/13600_Cloudbees [1]
>>>> _______________________________________________
>>>> dl-learner-discussion mailing list
>>>> dl-...@li... [2]
>>>>
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>>> [3]
>>>
>>>
>>
> 
> ------------------------------------------------------------------------------
>>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees [4]
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li... [5]
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [6]
>>
>>
> 
> ------------------------------------------------------------------------------
>> Put Bad Developers to Shame
>> Dominate Development with Jenkins Continuous Integration
>> Continuously Automate Build, Test & Deployment
>> Start a new project now. Try Jenkins in the cloud.
>> http://p.sf.net/sfu/13600_Cloudbees
>>
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
>
> Links:
> ------
> [1] http://p.sf.net/sfu/13600_Cloudbees
> [2] mailto:dl-...@li...
> [3] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [4] http://p.sf.net/sfu/13600_Cloudbees
> [5] mailto:dl-...@li...
> [6] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [7]
> 
> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download

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

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

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
>>>>>
>>>>>
>>>>
>>>
>>
>> ------------------------------------------------------------------------------ 
>>
>>>>>
>>>>> Put Bad Developers to Shame
>>>>> Dominate Development with Jenkins Continuous Integration
>>>>> Continuously Automate Build, Test & Deployment
>>>>> Start a new project now. Try Jenkins in the cloud.
>>>>> http://p.sf.net/sfu/13600_Cloudbees [1]
>>>>> _______________________________________________
>>>>> dl-learner-discussion mailing list
>>>>> dl-...@li... [2]
>>>>>
>>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>>>> [3]
>>>>
>>>>
>>>
>>
>> ------------------------------------------------------------------------------ 
>>
>>>>
>>>> Put Bad Developers to Shame
>>>> Dominate Development with Jenkins Continuous Integration
>>>> Continuously Automate Build, Test & Deployment
>>>> Start a new project now. Try Jenkins in the cloud.
>>>> http://p.sf.net/sfu/13600_Cloudbees [4]
>>>> _______________________________________________
>>>> dl-learner-discussion mailing list
>>>> dl-...@li... [5]
>>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>>> [6]
>>>
>>>
>>
>> ------------------------------------------------------------------------------ 
>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees
>>>
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li...
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>
>>
>>
>> Links:
>> ------
>> [1] http://p.sf.net/sfu/13600_Cloudbees
>> [2] mailto:dl-...@li...
>> [3] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [4] http://p.sf.net/sfu/13600_Cloudbees
>> [5] mailto:dl-...@li...
>> [6] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [7]
>>
>> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download 
>>
>
>
> ------------------------------------------------------------------------------
> "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE
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Re: [DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

Hi Lorenz,

I cannot make it work... I am on Windows:

--------------------------------------------------------------------
...\dllearner-1.0-beta-3\bin>cli.bat ../examples/father.conf
La ligne entrée est trop longue.
La syntaxe de la commande n'est pas correcte.
--------------------------------------------------------------------
which means
The input line is too long.
The syntax of the command is not correct.

This same command line works on dllearner-1.0-beta-2.

Regards,
Céline



Le 23/05/2014 14:05, Lorenz Bühmann a écrit :
> Hi Céline,
>
>  can you check if the latest version dllearner-1.0-beta-3 [15] which
> is downloadable at
> https://sourceforge.net/projects/dl-learner/files/DL-Learner/ [16]
> 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
>>> [4]
>>> [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(ROLe
>>>
>>>> at
>>> /blockquote>
>>>
>>
> 
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521)
>>>
>>> at
>>>
>>>> org.dllearner.algorithms.refinement2.ROLearner2.start(ROLe
>>> 36)
>>> at
>>>
>>>
>>
> 
> org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java
>>>
>>>> at org.dllearner.cli.Start.sta
>>> :347)
>>> at org.dllearner.cli.Start.main(Start.java:209)
>>>
>>> Kind regards,
>>>
>>>> Le 1
>>> 02, Lorenz Bühmann a écrit :
>>>
>>> Hi Céline,
>>>
>>> of course
>>>
>>>> ormation about DL-Learner if
>>> br> you're
>>> interested in.
>>>
>>> 1.) I'm not exactly sure what you mean by target langua
>>>
>>>> if you
>>> refer to what's the expressivity of the learned class
>>> expressions, then
>>> no, the ta
>>>
>>>> ner 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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