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Currently, when using LLMs to convert text to RDF statements, only "skos:related" is used to describe the relationship between concepts. Doing so simplifies the task of LLMs but should provide stronger relationships in the final knowledge graph. SKOS provides more explicit relationships, such as "skos:broader" and "skos:narrower". We can always give concepts clearer relationships by post-processing, but it's worth trying to introduce more complex relationships when annotating the data. Therefore, these text-RDF pairs can be fed into LLMs as part of the prompt, thus providing LLMs with instructions for generating more complicated relationships.
The text was updated successfully, but these errors were encountered:
Agrontology is now used to better express the relationships between concepts. Agrontology is limited in its ability to express relationships; it only has 170 properties. Therefore, the current strategy is to use Agrontology whenever possible, and to use SKOS only when there is no suitable property.
Currently, when using LLMs to convert text to RDF statements, only "skos:related" is used to describe the relationship between concepts. Doing so simplifies the task of LLMs but should provide stronger relationships in the final knowledge graph. SKOS provides more explicit relationships, such as "skos:broader" and "skos:narrower". We can always give concepts clearer relationships by post-processing, but it's worth trying to introduce more complex relationships when annotating the data. Therefore, these text-RDF pairs can be fed into LLMs as part of the prompt, thus providing LLMs with instructions for generating more complicated relationships.
The text was updated successfully, but these errors were encountered: