Re: [DL-Learner discussion] dl-learner and noise

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Hello Céline,

let me try to explain why things do not work as you would expect.

First of all, please always keep in mind that DL-Learner is searching 
for OWL class expressions that

1. cover as many positive examples as possible
2. cover as less negative examples as possible

This search space is quite large and that's why, assuming that a perfect 
solution exists (this is not always the case despite the trivial 
solution), it might be the case that DL-Learner needs much more time 
than the default 10 seconds.

According to your examples:

1. has6culture.conf
There is a default limit of n=5 for cardinalities, that's why your 
solution is never tested. We are working on a more intelligent solution. 
For now, you can increase the limit by

op.type = "rho"
op.cardinalityLimit = 10

Based on the latest source code, I get

solutions:
1: hasCulture min 6 Thing (pred. acc.: 100.00%, F-measure: 100.00%)
2: hasCulture min 6 (not WaterBody) (pred. acc.: 100.00%, F-measure: 
100.00%)
3: hasCulture min 6 (not Volcano) (pred. acc.: 100.00%, F-measure: 100.00%)
4: hasCulture min 6 (not View) (pred. acc.: 100.00%, F-measure: 100.00%)
5: hasCulture min 6 (not Urban) (pred. acc.: 100.00%, F-measure: 100.00%)
6: hasCulture min 6 (not TriumphalArch) (pred. acc.: 100.00%, F-measure: 
100.00%)
7: hasCulture min 6 (not Tower) (pred. acc.: 100.00%, F-measure: 100.00%)
8: hasCulture min 6 (not Stadium) (pred. acc.: 100.00%, F-measure: 100.00%)
9: hasCulture min 6 (not QualityEnvironment) (pred. acc.: 100.00%, 
F-measure: 100.00%)
10: hasCulture min 6 (not Amphitheatre) (pred. acc.: 100.00%, F-measure: 
100.00%)

2. hasOldTownOrShopping.conf
You have to increase the runtime.

alg.maxExecutionTimeInSeconds = 600

Moreover, it could help to disable some OWL constructs like negation and 
universal restrictions.
op.type = "rho"
op.useNegation = false //disable negation (not)
op.useAllConstructor = false //disable universal restrictions (only)

The output is

solutions:
1: (hasActivity some OldTown or hasEnvironment some Shopping) (pred. 
acc.: 100.00%, F-measure: 100.00%)
2: (hasActivity some OldTown or hasEnvironment some Urban) (pred. acc.: 
97.50%, F-measure: 98.04%)
3: (hasActivity some Promenade or hasEnvironment some Urban) (pred. 
acc.: 92.50%, F-measure: 94.34%)
4: (hasActivity some Promenade or hasEnvironment some Shopping) (pred. 
acc.: 92.50%, F-measure: 94.34%)
5: hasActivity min 2 (Excursion or Lazing) (pred. acc.: 70.00%, 
F-measure: 80.65%)
6: hasActivity min 2 (Excursion or WaterActivity) (pred. acc.: 68.75%, 
F-measure: 80.00%)
7: hasActivity min 2 (Excursion or Relaxation) (pred. acc.: 68.75%, 
F-measure: 80.00%)
8: hasActivity min 2 (Excursion or Nightlife) (pred. acc.: 68.75%, 
F-measure: 80.00%)
9: hasActivity min 2 (Excursion or NaturalEnvironment) (pred. acc.: 
68.75%, F-measure: 80.00%)
10: hasActivity min 2 (Environment or Excursion) (pred. acc.: 68.75%, 
F-measure: 80.00%)

3. hasBathingMidSummer.conf
The same holds for your 3rd example - this is really complex and far 
from easy to learn. Increase the runtime and disable some OWL 
constructs. Additionally, if you assume to get longer descriptions, you 
can set a parameter for the search heuristic like

h.type ="celoe_heuristic"
h.expansionPenaltyFactor = 0.02

The algorithm is still running and the output so far is

(hasEnvironment some Bathing and hasWeather some (avgTemperatureC some 
double[>= 21.8563] and precipitationMm some double[<= 33.65835] and 
concernMonth some hasSeason some MidSummer))

which was found after 1min 43s 32ms.

I attached all config files.

Kind regards,
Lorenz

> Hello Lorenz,
>
> Thanks a lot, that helps.
>
> I have another problem. I have an ontology describing holiday’s 
> destinations. If I want to learn some simple definitions like 
> "hasActivity some Animation" (or any "hasObjectProperty some Concept") 
> it works well. But if my definitions are more complicated, it does not 
> work. There is no noise in my examples.
>
> I want to learn:
> 1. "hasCulture min 6 Culture"
> 2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)"
> 3. "(has Activity some Bathing) and (hasWeather min 2 ( 
> (avgTemperature some double [>=23.0]) and (precipitationMm some 
> double[<=70.0]) and (concernMonth some hasSeason some MidSummer)))"
>
> I get:
>
> 1. hasCulture min 5 ((not Archaeology) and (not Architecture)) (pred. 
> acc.: 97,50%, F-measure: 94,74%)
> 2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%, 
> F-measure: 80,00%)
> 3. (hasActivity some Excursion and hasEnvironment some WaterActivity) 
> (pred. acc.: 68,75%, F-measure: 73,12%)
>
> Could you explain why I do not get the good definitions (or at least 
> some definitions with 100% of accuracy)? Are my definitions too 
> complicated? Did I miss something?
> Thank you in advance! Sorry for disturbing you again.
>
> Best regards,
> Céline
>
>
>
>
> Le 18-12-2014 18:40, Lorenz Bühmann a écrit :
>> Hello Céline,
>>
>>  there is a noise parameter called "noisePercentage" for the learning
>> algorithm CELOE, so you could simply define a noise value like 20%.
>> This will allow to return solutions in which 20% of the positive
>> examples are not covered by the solution.
>>
>>  Your conf file should be extended by the line:
>>
>>  alg.noisePercentage = "20"
>>
>>  Hope this helps.
>>
>>  Kind regards,
>>  Lorenz
>>
>>> Hello,
>>>
>>> I have an ontology describing 10000 films. I have built a conf file
>>> with positive and negative examples for “American films”. I want
>>> to learn that an American film “isFromCountry some _US”. When I
>>> run DL-Learner, it works well. Now, if I add a little bit of noise
>>> (changing one positive example into negative example and vice
>>> versa), it does not work anymore. “isFromCountry some Country”
>>> is learned (acc=42.86%). I have 10000 films, so this noise
>>> represents only 0.02% of error.
>>> Is it possible to get the good definition with some noise in my
>>> examples by running DL-Learner with some parameters I don’t know
>>> about or does DL-Learner just not handle noise?
>>>
>>> Thanks in advance for your answer. I attached the files if you want
>>> to make some tests. I use dllearner-1.0-beta-3 on Windows.
>>>
>>> Best regards,
>>> Céline
>>>
>>>
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