Hello all,
Say, I have the following example sentences
- apple is rich in vitamins.
- apple makes the doctor away.
- apple is healthy.
- apple is red in color.
- eva eats apple.
- Steve Jobs invented apple.
- apple iphone is usually costly.
- headquaters of apple inc. is in california.
- apple products are robust.
i just parsed these sentences and got R2L outputs. Example of one such
output is as follows (apple products are robust):
((ImplicationLink
(PredicateNode "robust@a80f418c-0d57-477d-8d77-7077068e3033")
(PredicateNode "robust" (stv 0.045454547 0.0012484394))
)
(InheritanceLink
(ConceptNode "products@c9f041f5-5583-4cb0-ab57-eec13624a28b")
(ConceptNode "product" (stv 0.019607844 0.0012484394))
)
(EvaluationLink
(PredicateNode "robust@a80f418c-0d57-477d-8d77-7077068e3033")
(ListLink
(ConceptNode "products@c9f041f5-5583-4cb0-ab57-eec13624a28b")
)
)
(InheritanceLink
(InterpretationNode
"sentence@293aa7fb-891c-48a5-ab25-e5be24cbbf47_parse_0_interpretation_$X")
(DefinedLinguisticConceptNode "DeclarativeSpeechAct")
)
(InheritanceLink
(ConceptNode "apple@7f6ace14-d5bf-4ffb-aa3b-dfc4bce59f7a")
(ConceptNode "apple" (stv 0.17647059 0.011124846))
)
(InheritanceLink
(ConceptNode "products@c9f041f5-5583-4cb0-ab57-eec13624a28b")
(ConceptNode "apple@7f6ace14-d5bf-4ffb-aa3b-dfc4bce59f7a")
)
)
1. When you look at the example sentences, you will know that i have
framed all sentences with the word "apple" that comes in two different
contexts. Apple as a fruit and and as a company. I think, Atoms have
multiple truth values depending upon the context. (I assume Atoms with the
same Context will have similar Truth Values.?!) . Is it possible to filter
atoms based on some TV depending upon particular context?
2. Like STV, i am unable to see STI in the R2L output. I assume the
atom with the highest number of links gets automatically high STI value.
So is it possible to retrieve top most important atoms based on STI ? (
also may be top atom's related atoms in a similar context). If so, how can
i acheive that?
3. The process of forgetting and recovering from the disk is possible only
when i save atoms in postgres. So everytime when i have huge text, i should
parse each line using (nlp-parse ""), convert into R2L output using
(parse-get-r2l-outputs.....) and in turn should store the obtained results
in postgres. Am i missing anything here?.
Thanks in advance
Vishnu
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