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We propose a Temporal Multi-Modal Knowledge Graph Generation (TMMKGG) method that can automatically construct TMMKGs, aiming to reduce construction costs.

We first construct a novel Multi-Modal Temporal Knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge …

Dec 1, 2024 · A novel method of multi-graph learning for temporal knowledge graph reasoning. An expressive graph aggregator to model spatial–temporal interactions. An adaptive way to …

We proposed methods compressing MMTKGs of videos and conducted experiments to deter-mine their efectiveness. We focused on two types of MMTKGs: KGs with video frame images …

Knowledge graph, or knowledge base, plays an important role in a variety of applications in the field of artificial intelligence. In both research and applicati.

Jun 28, 2024 · In this survey, we provide a rigorous definition of MMKGs along with a classification scheme based on how existing approaches address four fundamental …

Oct 21, 2024 · Temporal Knowledge Graph (TKG) reasoning is a crucial task that aims to predict future facts based on historical information. In the process of reasoning over TKGs, we identify …

Jul 29, 2024 · The Temporal Knowledge Graphs (TKGs) reasoning task aims to predict undiscovered links from the already known facts of TKG, which is crucial for areas such as …

Mar 20, 2025 · The demand for integrating multimodal information, such as text and images, has grown significantly as it enables richer and more comprehensive knowledge representations. …

4 days ago · To address these limitations, we introduce a novel Knowledge Graph Enhanced Generative Multi-modal model (KG-GMM) that builds an evolving knowledge graph throughout …

4 days ago · As a fundamental task in multi-modal learning as shown in Fig. 2, VQA is frequently used to evaluate multi-modal foundation models [5]. Multi-modal Knowledge Graphs (MMKG) …

Jun 4, 2024 · In this paper, we propose the Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method, which leverages multimodal knowledge graphs (MMKGs) to learn …

We propose a Temporal Multi-Modal Knowledge Graph Generation (TMMKGG) method that can automatically construct TMMKGs, aiming to reduce construction costs.

To address the above problems, this paper integrates visual-textual multimodal information, accumulates domain-specific and external multi-modal knowledge, introduces and …

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed by recently, which is very …

The manufacturing process of a product serves as a bridge connecting product design to production, and it is the cornerstone ensuring product quality in the manufacturing sector. The …

For most true multi-modal knowledge graphs, the missing links in the learning graph are very important. Most of the existing methods are based on the observed s.

Jan 5, 2025 · We propose a multimodal knowledge graph construction framework that combines advanced machine-learning techniques with human guidance to address this challenge. Our …

2 days ago · Towards unified multimodal editing with enhanced knowledge collaboration. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. Qian et al. …

Mar 13, 2019 · We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between …

Jun 26, 2024 · We propose a Temporal Multi-Modal Knowledge Graph Generation (TMMKGG) method that can automatically construct TMMKGs, aiming to reduce construction costs.

2 days ago · Jia, X. et al. Few-shot radiology report generation via knowledge transfer and multi-modal alignment. In 2022 IEEE International Conference on Bioinformatics and Biomedicine …

Mar 19, 2025 · In light of this issue, multimodal data fusion approaches offer a promising solution. The importance and the necessity of multimodal data fusion in AM has been increasingly …

3 days ago · The utility of deep learning in the context of intermediate fusion in multimodal settings, particularly within biomedical research, is marked by its ability to process and fuse …

Mar 19, 2025 · In this paper, we propose a novel Mixture of Spatial–Temporal Graph Transformer Networks (MoSTGTN) for urban congestion prediction using multimodal transportation data. ...