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Meta-Bootstrap Learner (MBL) was also proposed by the authors of CPL in<ref name=cpl2010 />. Meta-Bootstrap learner couples the training of multiple extraction techniques with a multi-view constraint, which requires the extractors to agree. It makes addition of coupling constraints on top of existing extraction algorithms, while treating them as black boxes, feasible. MBL assumes that the errors made by different extraction techniques are independent. Following is a quick summary of MBL.
Input: An ontology O, a set of extractors
Output: Trusted instances for each predicate
for i=1,2,...,∞ do
foreach predicate p in O do
foreach extractor e in ε do
Extract new candidates for p using e with recently promoted instances;
end
FILTER candidates that violate mutual-exclusion or type-checking constraints;
PROMOTE candidates that were extracted by all extractors;
end
end
Subordinate algorithms used with MBL do not promote any instance on their own, they report th evidence about each candidate to MBL and MBL is responsible for promoting instances.
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