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Overall accuracies ranged from 41to72%2%, depending on the validation method (per pixel vs. 3 × 3 windows of pixels, per STU vs. STU grouped by semantic proximity (n = 204)).
where denotes the semantic proximity of words and.
In the next section we define the semantic proximity between knowledge parts of a competency.
The semantic proximity is close to 1 for highly related words and null for nonrelated words.
The semantic proximity is taken as the cosine distance between context vectors of and, normalized in the interval.
We thus propose to define the semantic proximity between knowledge elements, based on their situation in the domain ontology.
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The results of the cluster analysis are visualized for our present purposes in a galaxy diagram in which cluster proximity reflects semantic relatedness.
In addition to using geo-coordinates to express geographical proximity, the Geospatial Semantic Web provides for relating locations as well as regions to each other using explicit semantic relationship types such as containment or shared borders.
Therefore, we first show that the similarity score between two genes in a gene semantic similarity network correlates with the proximity score of the corresponding proteins in a protein-protein interaction network.
Existing clustering techniques did not perform well for this task, and it was therefore necessary to develop a new spatial-semantic clustering method which considered the proximity of nearby tags and the similarity of their label content.
These results suggest that gene semantic similarity scores are correlated with protein proximity scores.
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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