[Pellet-users] A lot of descendant queries

[Alan Ruttenberg]

Can you direct me to the line of code that does that so I can comment  
it out in the short run?
Or give me an invocation that lets me remove it from the taxonomy  
right after I do the query?
Might be good to add another method that just says: get me the  
subclasses just this once, I  won't be asking about this class ever  
again.
-Alan


On May 3, 2007, at 2:28 PM, Evren Sirin wrote:

> On 5/3/07 11:59 AM, Alan Ruttenberg wrote:
>> One problem with doing it the slow way is that memory consistently  
>> grows. Is there a reason that the call to getSubClasses need to  
>> allocate any long lived memory?
> The getSubClasses function will insert the given concept into the  
> class taxonomy and doing many calls to getSubClasses will probably  
> eat up all the memory. It would be a good idea for us to put some  
> kind of limit on how many (anonymous) concepts will be cached  
> inside the taxonomy.
>
> Cheers,
> Evren
>> So when doing this the way I suggest on the GO biological process  
>> ontology I eventually run out of memory when I don't think I should.
>>
>> -Alan
>>
>>
>> On May 3, 2007, at 7:46 AM, Evren Sirin wrote:
>>
>>> On 5/1/07 3:44 PM, Alan Ruttenberg wrote:
>>>> Hi,
>>>> I'm using pellet to compute inferred class level part_of  
>>>> relations  between classes in GO.
>>>> The way I do this is to load the ontology and then iterate over  
>>>> all  classes c asking for descendants of
>>>> (restriction part_of (some-values-from c))
>>>>
>>>> This takes quite a while. Is there a fast way to do this?
>>>>
>>> I cannot think of any straightforward way that is faster. So you  
>>> want to compute the mereological hierarchy similar to subsumption  
>>> hierarchy. I think the same strategy (and similar optimizations)  
>>> for computing the subsumption hierarchy would apply here. That is  
>>> you compute told part_of relations, do a top search for each  
>>> concept to find what it is part of and do a bottom search to find  
>>> what parts it has.  This strategy avoids many unnecessary tests  
>>> and reuses the previously found information effectively. A lot of  
>>> the code inside Pellet classifier could be reused but there is  
>>> still a fair amount of work that needs to be done to customize  
>>> the classification code for this purpose.
>>>
>>> Cheers,
>>> Evren
>>>> -Alan
>>>>
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>>
>