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Available Matchers and Filters

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 Filter interface.

List of Matchers (Selection)

List of Filters (Selection)

  • MachineLearningScikitFilter
    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.
  • NaiveDescendingExtractor
    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.
  • MaxWeightBipartiteExtractor
    Faster implementation than the HungarianExtractor to generate a one-to-one alignment.
  • HungarianExtractor
    Implementation of the Hungarian algorithm to find a one-to-one mapping.
  • ConfidenceFilter
    Simple filter that removes correspondences with a confidence lower than a predefined threshold. Thresholds can be set per type.

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