Papers with MKGC
Noise-powered Multi-modal Knowledge Graph Representation Framework (2025.coling-main)
Copied to clipboard
| Challenge: | Current efforts to integrate MMKG with pretraining are scarce. |
| Approach: | They propose a method that integrates multi-modal entity features into MMKGs using a Transformer-based architecture equipped with modality-level noise masking. |
| Outcome: | The proposed method achieves SOTA performance across ten datasets. |
Joint Multilingual Knowledge Graph Completion and Alignment (2022.findings-emnlp)
Copied to clipboard
Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, Mathias Niepert
| Challenge: | Existing work on multilingual KG completion has focused on entity and relation alignments, but understanding of how it can aid multilingual alignments is limited. |
| Approach: | They propose to combine two components that jointly accomplish KG completion and alignment. |
| Outcome: | The proposed model outperforms existing competitive baselines on a public multilingual benchmark and achieves state-of-the-art results. |
Let Modalities Teach Each Other: Modal-Collaborative Knowledge Extraction and Fusion for Multimodal Knowledge Graph Completion (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have focused on missing triples in knowledge graphs, but lack correlation between modalities. |
| Approach: | They propose a framework to foster mutual guidance and collaboration in unimodal knowledge extraction and multimodal knowledge fusion. |
| Outcome: | Extensive experiments on three real-world datasets demonstrate advantages of Moodle over state-of-the-art methods. |
Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing MKGC research ignores the shareability of cross-lingual knowledge. |
| Approach: | They propose a multilingual knowledge Graph Completion framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER). |
| Outcome: | The proposed framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits @3, and Hits_10 metrics, respectively, compared with existing state-of-the-art (SOTA) MKGC method. |
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to predict missing entities share relation representation across modalities, which results in mutual interference between modality. |
| Approach: | They propose a framework for multimodal knowledge graph completion that learns modality-split relation embeddings for each modality instead of a single modality shared one. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three KG datasets. |
Not All Modalities at Once: Dynamic Dropout and Bidirectional Fusion for Robust Multi-modal Knowledge Graph Completion (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing MKGC methods train with all modalities available, implicitly assuming consistent complementarity . however, this often induces modality dependence and modality competition under heterogeneous noise, which can hinder robust multi-modal fusion and limit overall performance. |
| Approach: | They propose a framework to infer missing links in multimodal knowledge graphs by leveraging structured triples together with auxiliary modalities such as text and images. |
| Outcome: | The proposed framework outperforms baselines and achieves new state-of-the-art results. |