MELT offers a wide range of-out-of-the-box matchers and filters. Below, we list the most significant matchers and filters. The remaining pages in this section list all available matchers and filters in MELT.
A filter is a matcher that does not add new correspondences to the alignment but instead further processes the given alignment by (1) removing correspondences and/or (2) adding new feature weights to existing correspondences. MELT filters implement the
List of Matchers (Selection)
Default String-matcher, used as default-baseline in evaluators.
Configurable String-matcher that scales well.
Wrapper of the Paris matching system.
List of Filters (Selection)
This filter learns and applies a classifier given a training sample and an existing alignment. You can refer to our article Supervised Ontology and Instance Matching with MELT for a more detailed description and application examples. In the example directory, you can find the implementations of the matchers described in the article.
It iterates over the sorted (descending) correspondences and uses the correspondence with the highest confidence. Afterwards removes every other correspondence with the same source or target.
Faster implementation than the HungarianExtractor to generate a one-to-one alignment.
Implementation of the Hungarian algorithm to find a one-to-one mapping.
Simple filter that removes correspondences with a confidence lower than a predefined threshold. Thresholds can be set per type.