To solve the problem of scattered storage of information science objects among paper texts, citation paths, author institutions, source journals, topic tags and time periods, a multimodal knowledge graph empirical sample based on open academic metadata has been established and its function in knowledge organization and data mining has been tested. The sample includes records of information science and related directions from 2014 to 2024. After processing, 5200 papers, 13172 entities and 105148 relationships remain, forming seven kinds of nodes: papers, authors, institutions, sources, keywords, topics and years, and relationships such as authorship, institutional affiliation, source publication, keyword association, topic attribution, citation linkage and co-occurrence. In terms of methodology, the titles, abstracts and keywords of the papers, the citation network, the author-institution network, the source-topic distribution and the annual period were encoded into five kinds of modal features and compared in four tasks: entity alignment, relationship completion, cross-modal retrieval and topic mining: Text only, Citation GCN, Late Fusion, MKG-BERT and Proposed MDF-KG. The results show that the average Macro-F1 of Proposed MDF-KG in four types of tasks is 0.8535, which is higher than the 0.8213 of MKG-BERT and the 0.7880 of Late Fusion; the Mean Reciprocal Rank (MRR) reaches 0.912 in the relationship completion task and the normalized discounted cumulative gain at 10 (nDCG@10) reaches 0.858 in cross-modal retrieval. The results of the ablation study indicate that the text modality has the highest average contribution and the performance decreases by 0.0553 after deleting it; the citation modality decreases by 0.0415 and the source topic modality decreases by 0.0318. The robustness test shows that when the field is missing or the noise ratio reaches 0.5, the average Macro-F1 of the Proposed MDF-KG is still 0.8080. The research findings suggest that the multimodal field organization can enhance the connectivity, ranking quality and error correction capability of information science knowledge graphs, but author alias, topic granularity and weak citation context are still the main obstacles for further development.
