GOOOOOAAAAALLL directed learning…

This is the first real result from Smeagol, where it actually makes a plan to learn and does succeed!

(click for readable version)

I realise that this diagram is probably completely incomprehensible to anyone but me, so here is a quick explanation:

1. The "top-level goal" given in this case is to answer the query:

[] ql:select ( ?s ) ;
ql:where {
?s a bibtex:InProceedings ;
?s foaf:maker ?a ;
?a pers:expertIn wiki:Semantic_Web
} ;
ql:results ?r .

i.e. give all papers written by Semantic Web Experts.

2. Smeagol first makes the trivial plan to read some data (there are many issues about this, I skip them all), and perform the query.

3. The trivial plan fails (otherwise this would be boring), because there are no people who are Semantic Web experts who have written any papers (in my extremely artificial hand-made dataset). Papers written by other people DO exist…

4. Several plans are made that may introduce more triples matching any of the patterns evaluated in the query before it failed. In this particular case my heuristic reordered the query to be [(?s a Inproceedings),(?a expertIn SemanticWeb), (?s maker ?a)], i.e. find all the people and papers first and do the join later, and it was the very last pattern that failed to match, therefore further results for any of these patterns could be useful.

5. The easiest (the actions have weights) plan is chosen first, this is read the ontology for the bibtex classes, and attempt to find more "things" that are "InProceedings" based on RDFS inference, maybe they are a only explicitly declared to be a subclass of InProceedings for example. Unfortunately, this is not the case, and the this plan fails.

6. Smeagol returns to the second easiest plan, this plan involves actual learning: Find the set of things that are already of type InProceedings => attempt to use ILP to learn a description of this set => use this description to classify further instances as InProceedings. Alas, this also fails, in this case it failed because it failed to find any good negative examples, but I wont go into that here.

7. Returning to the third easiest plan, this is using the same pattern as the previous plan, but finds a set of people who are Semantic Web experts instead. In this case the description learning DOES work and we learn the rule:

{ ?A <> <> }
{ ?A rabbi:learnedCategory "blah" }.

8. This rule is then used to classify more instances as Semantic Web experts, and since I very carefully constructed this examples it finds one! :)

9. After the sub-plan has succeeded, Smeagol returns to the previous failed plan and re-tries the failed action, and now the query works. Hurrah!

All in all this took a ridiculous number of lines of code, many hours of debugging, and the result still isn't very impressive, but hopefully I have ironed out most planning bugs now and I can get to work on creating the final set of examples that will finish off this PhD! :)

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