Multimodal link prediction on multimodal knowledge graphs is an inference task aimed at finding missing triples, which seeks to improve prediction accuracy by leveraging a wide range of information. However, current multimodal knowledge graph link prediction methods are primarily designed in the spatial domain, necessitating ever-growing complexity in fusion strategies. In addition, most of them focus on only three modalities (text, image, and structure). Forward-looking approaches, however, should accommodate a broader array of modalities. Motivated by the operational simplification enabled by transforming features into the frequency (or time-frequency) domain, we propose a wavelet-transform-based multimodal link prediction method, WFF, which offers high modal extensibility and low fusion complexity. Specifically, for unimodal information, we designed a Unimodal Time-Frequency Knowledge Enhancement module, UTFKE, which extracts time-frequency features via discrete wavelet transform and enhances information quality through adaptive filtering. To address the challenge of multimodal fusion, we devised a Multimodal Time-Frequency Knowledge Fusion module MTFKF that supports high modal extensibility and enables effective, efficient integration. Extensive experiments on multiple well-known datasets demonstrate that WFF outperforms strong baselines and achieves state-of-the-art performance. In addition, WFF extends modality to audio and video, further validating the model's effectiveness. Our code is available at https://github.com/xxd12315/WFF.

Different from traditional knowledge graphs, where facts are usually represented as (subject, relation, object), hyper-relational knowledge graphs (HKGs) allow facts to be associated with additional relation-entity pairs to constrain the validity of facts. HKGs contain a substantial amount of textual information, which plays a crucial role in enriching representations. However, existing HKG embedding methods mainly rely on structural information but overlook textual information in HKGs, which are less effective in representing entities with limited structural information. To address this issue, the paper proposes HIST (Hyper-relational Knowledge Graph Encoder Integrating Structure and Text), which incorporates textual information and structural information in HKGs to enhance representations of entities and relations. HIST adopts the graph convolutional network to extract structural information and utilizes it to generate the Structure Soft Prompt. During the Structure Soft Prompt Tuning process, the textual information and structural information are fully integrated to generate more comprehensive representations. Additionally, an effective contrastive learning method for HKG embedding is formulated to improve the efficiency of negative sampling. Experimental results show that HIST achieves state-of-the-art performance on several public datasets. Our code is available at https://github.com/QieFangBaiLuQingYaJian/HIST.