DSF2 :DUAL-SPACE GUIDED DISCRIMINATIVE HIGH AND LOW FREQUENCY FUSION NETWORK FOR MULTIMODAL KNOWLEDGE GRAPH COMPLETION

Multimodal knowledge graph completion (MMKGC) aims to complete missing facts in knowledge graphs by jointly leveraging multimodal information associated with entities. Existing methods primarily fuse multimodal entity embeddings in the spatial domain, yet lack explicit modeling of frequency characteristics at different scales of variation. To capture these multiscale characteristics, shifting to the frequency domain provides a new perspective by employing spectral decomposition and filtering to facilitate multimodal fusion, but such methods neglect the pervasive coupling of spectral signals at the boundaries and the distinct characteristics of different frequencies, thereby hampering the precise characterization of multimodal features and limiting the exploitation of multimodal spectral information. To address these limitations, we propose DSF2, Dual-Space Guided Discriminative High and Low Frequency Fusion Network for MMKGC, an innovative framework that leverages different spatial geometrical properties to disentangle the pervasive inter-frequency coupling and applies distinct modeling strategies tailored to band-specific characteristics. Concretely, we design Dual-Space Frequency Disambiguation (DSFD), which better models the coupling of spectral signals by leveraging the geometric differences of corresponding spectral embeddings across these spaces to derive more discriminative features. We further design distinct processing strategies for differentiated frequencies, including a low-frequency Shared-memory Interaction Strategy (LSIS) and a High-frequency Relation-aware Gating Strategy (HRGS). Specifically, LSIS captures stable global low-frequency semantics through shared-memory interaction to promote cross-modal semantic communication and enhance multimodal consistency. HRGS focuses on high-frequency semantics characterized by local variations, employing a relation-aware gating mechanism to emphasize local information discrepancies and strengthen relation-specific high-frequency representations. This distinct strategy effectively captures differentiated frequency characteristics, thereby fully exploiting the complementary low- and high-frequency semantics. Extensive experiments on multiple benchmark datasets consistently validate the effectiveness and robustness of DSF2.