Difference: Multinet (3 vs. 4)

Revision 42006-10-28 - FabrizioMorbini

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META TOPICPARENT name="SKIL"

Paper 1

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    • sort: hierarchy, must have category. not combinable (exclusive?).
    • feature: polyhierarchy, typical category. combinable.
  • multidimensionality of each node:
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    • intensional/extensional: few attempts? (compared to what? modal logic seems to be based on that distinction, no?). Called preextensional because an extensional concept is normally not fully expressed but expressed only by few examples. Interesting part, talking about the example "nearly all girls in the class love a boy". Intensional layer: condensed info. Extensional layer: all info.
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    • intensional/extensional: few attempts? (compared to what? modal logic seems to be based on that distinction, no?). Called preextensional because an extensional concept is normally not fully expressed but expressed only by few examples. Interesting part, talking about the example "nearly all girls in the class love a boy". Intensional layer: condensed info. Extensional layer: all info. Here intensional/extensional is not the same as intension/extension as used in logic.
 
    • determinate/indeterminate: attributes REF={DET, INDET} and VARI={VAR, CO}. For example, a determiner that has an indepedent reference (i.e. a constant, even Skolem) will have REF=DET and VARI=CO. Something depending on something else (i.e. a skolem function) will have REF=INDET and VARI=VAR. (examples of intermediate situations?)
    • generic/non-generic: attribute GENER={GE,SP}. properties of a generic (GENER=GE) are inherited by default (typical or non-categorical, non-monotonic) by individuals of that class.
    • virtual/real: attribute "FACT", real for concepts that exists, virtual for hipotesized concepts. See figure 6.
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  In the conclusions it says that it can express donkey sentences (what is "combined quantification"?)
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The comparison doesn't seem completely fair because DRT, GQT and FCT weren't done for knowledge representation and inference.
 

Observations (starting points for discussion)

the initial list of properties of a knowledge representation language are good, but:
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what is the cognitive adequacy thing?
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what is the cognitive adequacy thing? It seems to be the property of being able to retrieve information based on the concepts involved in it (propagation in a graph where the nodes are concepts).
 
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But how do you produce that representation from normal NL? Example: "the lion is dangerous", "the lion is a mammal". First generic, second no.
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But how do you produce that representation from normal NL? Example: "the lion is dangerous", "the lion is a mammal". First generic, second no. This difference is based on the property of the predicates involved.
 
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Example in figure 3. How do you express "John gave Mary several dollars", "It was more than John gave to another girl" and "Two of them were counterfeits".
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Example in figure 3. How do you express "John gave Mary several dollars", "It was more than John gave to another girl" and "Two of them were counterfeits". One way is to existentially quantify over the set of several dollars and that is the reference for 'it'.
  Interesting classification of KRLs for NLU in the conclusion.
 
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