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The semantic resources to construe death are expanded by both lexical and grammatical metaphor.
For that reason, some of the things we report are prototypes rather than completely established semantic resources.
In bioscience these ideas have been taken up at an early stage and many semantic resources have been created.
Apart from the development of InChI [10] (a semantic identifier system for connection tables), there has been very little central community interest in creating semantic resources.
Accordingly transitivity analysis and ergativity analysis are to be employed to bring out the respective semantic resources behind (Davidse1992: 132,1998).
In medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight.
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We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource.
In this sense, the semantic resource InferenceNet was linked to the LOD cloud (Linked Open Data) through DBPedia, Yago, and WordNet, as described in [26].
The WordNet database was used as the only semantic resource that provided all the important concepts for the question generation process.
The entities and their relations were further annotated by Chem2Bio2OWL ontology, making it a rich semantic resource for integrative searches [31] and data mining [32].
This is in line with the current view that knowledge intensive methods for NLP will reason better if they consider several knowledge bases as a joint semantic resource.
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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