Go to ACM TheWebConf 2025 Conference homepageTowards Multimodal Inductive Learning: Adaptively Embedding MMKG via Prototypes
Track: Semantics and knowledge
Keywords: Inductive Learning, Multimodal Knowledge Graph, Link Prediction, Knowledge Representation Learning and Embedding
Abstract: Multimodal Knowledge Graphs (MMKG) models integrate multimodal contexts to improve link prediction performance. All existing MMKG models follow the transductive setting with a fixed predefined set, meaning that all the entities, relations, and multimodal information in the test graph are observed during training. This hinders their generalization to real-world MMKG with unseen entities and relations. Intuitively, a MMKG model trained on DBpedia cannot infer on Freebase. To address above limitations, we make the first attempt towards inductive learning for MMKG and propose a multimodal \underline{\textbf{Ind}}uctive \underline{\textbf{MMKG}} model (\textbf{IndMKG}) that is \textit{\textbf{universal}} and \textit{\textbf{transferable}} to any MMKG. Distinct from existing transductive methods, our model does not rely on specific trained embeddings; instead, IndMKG generates adaptive embeddings conditioned on any new MMKG via multimodal prototypes. Specifically, we construct class-adaptive prototypes to appropriately characterize the multimodal feature distribution of the given graph and equip IndMKG with robust adaptability to multimodal information across MMKGs. In addition, IndMKG learns non-specific structural embeddings based on meta relations. Such strategies tackle the challenge of notable multimodal feature discrepancies in cross-graph induction and allow the pre-trained IndMKG model to effectively zero-shot generalize to any MMKG. The strong performance in both inductive and transductive settings, across more than 20+ different scenarios, confirms the effectiveness and robustness of IndMKG. Our code is released at https://anonymous.4open.science/r/IndMKG.
Submission Number: 2867
Loading