Difference between revisions of "Vulcan/SystemTarget"

Revision as of 04:29, 22 August 2013

This page describes the features to target for the first implementation.

I/O

Input

Natural language sentences as input.

The system will handle propositions that correspond to three questions.

Q1: X is the best conductor of electricity. X = {Iron nail, wax crayon, plastic cup, rubber boat}
Q2: X causes leaves of a plant to become larger. X = {Growth, A repair, Germination, Decomposition}
Q3: X helps a fox find food. X = {A sense of smell, ...}

Output

Marginal inference probability for each proposition according to Tuffy.

Set of rules and axioms used (human-readable and MLN format). Note this is going to be hand generated in this iteration.

Debugging output that shows the set of axioms, rules and new facts used in the inference. Preferably in a graphical format.

Method

Setting up axioms and rules.

1. Hand generate the set of rules in a human-readable format (click [[|here]] for an example. 
   Convert this into the Tuffy MLN format. 

2. Process the study guide sentences using Open IE 4.0. Have it indexed 

Steps performed in verifying the input sentence.

1. Create query tuple: Run Open IE 4.0 on each input sentence. Extract the best tuple to use as the query tuple for MLN. 
   Use the one that covers the sentence best. If there is a tie use the tuple with the longest relation phrase.

2. Search for evidence: Find tuples that have some keyword overlap with the input sentence. 
   Translate these tuples into Tuffy's evidence format. 

3. Run Tuffy's MLN inference procedure for obtaining the marginal probability of the query tuple.

4. Parse Tuffy's output to extract the final score and find the set of rules, axioms and newly inferred facts used in the inference. 
   This would require some understanding of the database schemas that Tuffy uses. 

System Components

The system will have the following components implemented.

1. Proposition Generator

2. Rules converter

3. Tuffy Wrapper

4. Axioms generator