Multi-Modal Knowledge Graph Embeddings

Introduction:

Use of multi-modal data and knowledge graphs are becoming an increasingly important way to address larger problems through Gen-AI. In a recent example, the author had to extract and create knowledge graphs for sensor data, drone images and meter readings and subsequently integrate them into advanced RAGs for processing.

Often these problems get attacked as multi-step inference by using knowledge graphs and varied inference at each step. One key issue, that the author has run into multiple times, is the hurdles in creating the right embeddings for such multi-modal knowledge graphs.

The 2 papers below represent significant recent progress in this field, and hence worth summarizing – the papers, as well as pointers to code used.

[Note: Architecture pictures are sourced from the papers referenced]

Paper #1 "Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion" (https://arxiv.org/abs/2402.15444):

Multi Modal Knowledge Graph Embeddings

Summary:

·        This paper introduces AdaMF-MAT (Adaptive Multi-modal Fusion and Modality Adversarial Training) for multi-modal knowledge graph completion (MMKGC). The approach addresses the imbalance problem of modality information among entities, which previous methods overlooked. AdaMF-MAT uses adaptive modality weights for multi-modal fusion and employs modality-adversarial training to enhance imbalanced modality information. The method co-designs the MMKGC model and training strategy to better utilize raw modality information. AdaMF-MAT outperforms 19 recent MMKGC methods on three public benchmarks, achieving state-of-the-art results. The authors highlight the importance of addressing modality imbalance in MMKGC tasks. The paper provides a novel perspective on handling multi-modal data in knowledge graph completion, focusing on the challenges of imbalanced information across different modalities.

Paper #2. "MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion" (https://arxiv.org/abs/2404.09468):

Multi Modal Knowledge Graph Embeddings

Summary:

·        This paper presents MyGO, a novel framework for multi-modal knowledge graph completion (MMKGC). MyGO addresses the issue of coarse handling of multi-modal data in existing methods by focusing on fine-grained semantic details and their interactions. The approach tokenizes multi-modal raw data into fine-grained discrete tokens and uses a cross-modal entity encoder to learn entity representations. MyGO incorporates fine-grained contrastive learning to enhance the specificity of entity representations. The method aims to process, fuse, and augment fine-grained modality information from multi-modal knowledge graphs more effectively. Experiments show that MyGO outperforms 20 of the latest models on standard MMKGC benchmarks. The paper emphasizes the importance of capturing nuanced, fine-grained semantic information in multi-modal data for improved MMKGC performance.

Differences in Approach:

While both papers address multi-modal knowledge graph completion (MMKGC), they propose different approaches to tackle the challenges in this field. Both papers aim to improve MMKGC, they take different approaches: AdaMF-MAT focuses on balancing and enhancing modality information, while MyGO emphasizes fine-grained tokenization and representation of multi-modal data. This evolution in approach suggests ongoing refinement and exploration of techniques in the field of multi-modal knowledge graph completion by the authors.

1. Focus and Problem Addressed:

Paper 1 (AdaMF-MAT): Focuses on the imbalance problem of modality information among entities, aiming to improve modal fusion and utilization of raw modality information.

\Paper 2 (MyGO): Addresses the issue of “coarse” handling of multi-modal data, focusing on capturing fine-grained semantic details and their interactions.

2. Proposed Approaches:

Paper 1: Introduces Adaptive Multi-modal Fusion and Modality Adversarial Training (AdaMF-MAT).

Paper 2: Proposes a framework called MyGO (My Graph Optimization).

3. Key Techniques:

Paper 1:

§  Uses adaptive modality weights for multi-modal fusion.

§  Employs modality-adversarial training to enhance imbalanced modality information.

Paper 2:

§  Tokenizes multi-modal raw data into fine-grained discrete tokens.

§  Utilizes a cross-modal entity encoder for learning entity representations.

§  Incorporates fine-grained contrastive learning to highlight entity representation specificity.

4. Methodology:

Paper 1: Co-designs the MMKGC model and training strategy.

Paper 2: Focuses on processing, fusing, and augmenting fine-grained modality information.

5. Performance Comparison:

Paper 1: Outperforms 19 recent MMKGC methods.

Paper 2: Surpasses 20 of the latest models.

6. Timing and Evolution:

The second paper (MyGO) is a more recent and potentially more advanced approach, building upon the insights gained from the first paper (AdaMF-MAT). It shows a shift in focus from addressing modality imbalance to capturing more nuanced, fine-grained semantic details.

Code

Paper #1: https://github.com/zjukg/adamf-mat (a little raw)

Paper #2: https://github.com/zjukg/mygo (far more ready for use)

Datasets: https://paperswithcode.com/dataset/mmkg

Key Trends in Multi-Modal Knowledge Graph embeddings.

Some key points summarizing the main directions:

1.  Fine-grained processing of multi-modal data: Recent work like MyGO focuses on tokenizing multi-modal information into discrete, fine-grained tokens to capture nuanced semantic details.

2.  Addressing modality imbalance: Papers like AdaMF-MAT tackle the issue of imbalanced modality information among entities, using adaptive fusion and adversarial training techniques.

3.  Improved fusion mechanisms: Researchers are developing more sophisticated methods to combine information from different modalities, such as adaptive weighting schemes.

4.  Robustness to missing modalities: There's a focus on creating models that can handle incomplete or missing modality information, which is common in real-world scenarios.

5.  Integration with large language models: Some work explores leveraging multi-modal knowledge graphs to enhance the reasoning capabilities of large language models.

6.  Cross-modal alignment: Techniques are being developed to better align representations across different modalities, improving overall embedding quality.

7.  Contrastive learning approaches: Methods like fine-grained contrastive learning are being used to enhance the specificity of entity representations.

8.  Scalability and efficiency: As knowledge graphs grow larger, there's ongoing work to make multi-modal embedding techniques more computationally efficient.

9.  Domain-specific applications: Researchers are applying multi-modal knowledge graph embeddings to specific domains like healthcare and recommender systems.

10. Evaluation on diverse benchmarks: There's an emphasis on testing new methods across multiple public benchmarks to demonstrate generalizability and performance improvements.

There’s a move towards more sophisticated, robust, and practical multi-modal knowledge graph embedding techniques that can handle real-world challenges and diverse applications.

Arindam Banerji PhD: banerji.arindam@gmail.com