DSGG's approach to relation distillation differs from previous methods in several key ways. Firstly, DSGG utilizes a predicate filter that dynamically rejects pairwise relations based on learned graph-aware queries, rather than relying on predefined sets of possible triplets. This allows the model to capture missing triplets and adapt to diverse relationships present in the data. Additionally, DSGG incorporates an MLP-based pairwise feature learning mechanism for relation distillation, enabling effective filtering and ranking of predicates based on entity semantics. By combining these techniques, DSGG can efficiently learn multiple instances of semantic relationships and improve the accuracy of pairwise relation prediction.