Difference between revisions of "Vulcan/SystemPrototype"
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* <b>What does this exercise suggest?</b> | * <b>What does this exercise suggest?</b> | ||
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| − | * | + | * Keep predicates with small arity. For example, avoid writing rules entire nested tuples as predicates. |
| + | * For now we can compute this from the score of its components: Score(nested_tuple) = Score(top tuple) * Score (nested). | ||
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Revision as of 19:22, 27 August 2013
Overview
The prototype is designed to work on three questions. We want the system to output the following:
- Score for the input proposition.
- New facts inferred.
- Facts and rules used in scoring.
Status
The MLN programs and output from Tuffy can be found here.
- Knowledge: Worked out the facts and rules required.
- Score: The system outputs scores for each query predicate. If query is not in output then score is zero.
- Does it work?
- In all three examples the correct answer is assigned higher score compared to the incorrect ones.
- Facts inferred by larger number of steps have a lower score compared to those inferred by a smaller number of steps.
- What other diagnostics do we have?
- Inferred facts along with their probabilities.
- Rules that are reachable from the query fact. i.e., Clauses in the MLN that are relevant to the inference of the query fact.
- What diagnostics do we NOT have?
- Connections between the clauses in the MLN.
- A reconstruction/visualization of the MLN network.
- What does this exercise suggest?
- Keep predicates with small arity. For example, avoid writing rules entire nested tuples as predicates.
- For now we can compute this from the score of its components: Score(nested_tuple) = Score(top tuple) * Score (nested).
