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The process of ElementKG embedding. We derive a corpus of three documents (structure document, lexical document and combined document) from ElementKG, considering the structural topology, literal semantics and correspondence between entity IDs and literal words in ElementKG, respectively. We then train a language model to learn entity and relation embeddings from this corpus. This process enables the integration of element and functional group knowledge into a unified representation, which facilitates downstream molecular property prediction.

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