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One of them is ontology matching that matches terms in ontologies up to a given confidence value.
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This would match terms in the Google Play and Apple stores.
The matched terms are considered for tag suggestion in the next step.
Note that term matching is implemented with insensitive case matching by which the N-gram matching can be considered to enhance the precision of the matching terms.
After matching terms to images, the model ranks its output using information from both the images and the original search.
The article may have no relevant domain if there is no the matched terms for all domains.
Here, the relevancy of an article to a domain is represented by the number of the matched terms.
The key success factor for an IR-based bug localization technique lies on its ability to match terms from bug reports and source files effectively.
Here, the article can be assigned into one or more domains if the numbers of the matched terms of those domains are equivalent.
PolySearch employs a text ranking scheme to score the most relevant sentences and abstracts that associate both the query and match terms with each other.
If there is a match, the term is added to the cache of matched terms.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com