  Audio tagging aims at predicting sound events occurred in a recording.
Traditional models require enormous laborious annotations, otherwise
performance degeneration will be the norm. Therefore, we investigate robust
audio tagging models in low-resource scenarios with the enhancement of
knowledge graphs. Besides existing ontological knowledge, we further propose a
semi-automatic approach that can construct temporal knowledge graphs on diverse
domain-specific label sets. Moreover, we leverage a variant of relation-aware
graph neural network, D-GCN, to combine the strength of the two knowledge
types. Experiments on AudioSet and SONYC urban sound tagging datasets suggest
the effectiveness of the introduced temporal knowledge, and the advantage of
the combined KGs with D-GCN over single knowledge source.
