I do not understand how to work with combined truth values of observation.
Let me put the following example:
I want to match a dataset of people from a well curated database (D1) to information that was collected from the web, with much lower confidence(D2)
In D1 each person has a phone number(PSTN) and SSN, and the truth value is always 1.
In D2 people may have either SSN or PSTN, or both. Truth value for each observation is set to 0.25i:
The closed predicates are:
D1_PSTN(P,PSTN),D2_PSTN(P,PSTN)
D1_SSN(P,SSN), D2_SSN(P,SSN)
The open predicate is:
SamePerson(P1,P2)
Rules are:
5: !SamePerson(P1,P2) -- prior
1. 10: D1_PSTN(P1,PSTN) & D2_PSTN(P2,PSTN) -> SamePerson(P1,P2)
2. 10: D1_SSN(P1,SSN) & D2_SSN(P2,SSN) -> SamePerson(P1,P2)
3. 1000: D1_PSTN(P1,PSTN) & D2_PSTN(P2,PSTN) & D1_SSN(P1,SSN) & D2_SSN(P2,SSN) -> SamePerson(P1,P2)
I think Rule number 3 is ineffective. It has a very high weight. Because it is unlikely that a person from D2 will have both PSNT and SSN like a person from D1 just by coincidence (even though the truth values are only 0.25).
But - Truth values are computed by Lukasiewicz t-norm. and between is computed as max(0,0.25+0.25-1), which is 0.
To my understanding – Giving high weight to a rule does not matter if the result has low truth value.
How can I make a rule that will assign high truth values when two low truth value observation appear in the same rule?
Thanks, Tzahi
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