Title: LBMKGC: Large Model-Driven Balanced Multimodal Knowledge Graph Completion
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-modal Knowledge Graphs

Knowledge Graph Completion

Cross-modal Interaction

Large Vision-Language Model
Abstract: Multi-modal Knowledge Graph Completion (MMKGC) aims to predict missing entities, relations, or attributes in knowledge graphs by collaboratively modeling the triple structure and multimodal information (e.g., text, images, videos) associated with entities.
This approach facilitates the automatic discovery of previously unobserved factual knowledge.
However, existing MMKGC methods encounter several critical challenges: (i) the imbalance of inter-entity information across different modalities; (ii) the heterogeneity of intra-entity multimodal information; and (iii) for a given entity, the informational contributions of different modalities are inconsistent across contexts.
In this paper, we propose a novel **L**arge model-driven **B**alanced **M**ultimodal **K**nowledge **G**raph **C**ompletion framework, termed LBMKGC.
Subsequently, to bridge the semantic gap between heterogeneous modalities, LBMKGC aligns the multimodal embeddings of entities semantically by using the CLIP (Contrastive Language-Image Pre-Training) model.
Furthermore, LBMKGC adaptively fuses multimodal embeddings with relational guidance by distinguishing between the perceptual and conceptual attributes of triples.
Finally, extensive experiments conducted against 21 state-of-the-art baselines demonstrate that LBMKGC achieves superior performance across diverse datasets and scenarios while maintaining efficiency and generalizability.
Our code and data are publicly available at: https://github.com/guoynow/LBMKGC.


Peer reviews:
comments: SUMMARY:
This paper introduces LBMKGC, a novel framework for Multimodal Knowledge Graph Completion designed to address three key challenges: modality imbalance, intra-entity modality heterogeneity, and inter-modality information inconsistency. Through evaluations and comparisons with state-of-the-art methods, the effectiveness of LBMKGC is demonstrated.

STRENGTHS AND WEAKNESSES:
Strengths:

The paper is well-written, clearly presenting the motivation and structure of the proposed method. The key contributions of the paper include: introducing the   Large model-driven Balanced MMKGC (LBMKGC) for multimodal knowledge graph completion, evaluation of LBMKGC against state-of-the-art MMKGC methods; and a good level of detail is provided on the methodology with the strong performance of the proposed approach.

Weaknesses:

- The paper restricts itself to text and images. Though this is reasonable given scope, discussions around audio/video modalities and potential future extensions are only briefly mentioned.

- Despite strong performance margins, the lack of statistical testing (e.g., standard deviations) weakens claims of consistency. Including these is crucial to allow the reader to draw meaningful conclusions from the results.

QUESTIONS:
-  Is it possible to provide more detailed statistics, such as error bars?
- Are the methods sensitive to the hyperparameters? Lack of sensitivity analysis.

LIMITATIONS:
The limitations are mentioned in the paper  in the conclusion section.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-24

comments: SUMMARY:
This paper presents LBMKGC, a multimodal knowledge graph completion framework targeting key challenges such as modality imbalance, heterogeneity, and semantic inconsistency. LBMKGC employs the Stable Diffusion XL to augment the imbalanced information across modalities. LBMKGC aligns the multimodal embeddings of entities semantically by using the CLIP. Comprehensive experiments on 21 benchmarks show the effectiveness.

STRENGTHS AND WEAKNESSES:
Strengths:
1. This paper is well writing and easy to read.
2. The perceptual-conceptual dual-branch fusion module is innovative and contributes to improved semantic coherence.

Weaknesses:
1. In the LLM-Based Modality Completion section, it appears that the main contributions largely rely on the existing work of SDXL (Stable Diffusion XL). The authors should more clearly articulate the innovations and improvements this paper makes upon prior work to help readers understand its original contributions.

2. This paper lacks an analysis or empirical evaluation of time complexity, which should be included to better assess the method's efficiency.

3. While the paper emphasizes modality modeling and fusion, it pays relatively little attention to structural modeling of the KG. The influence of graph structure on the completion task is underexplored.

4. The authors have provided a non-anonymous GitHub link in the abstract, which appears to violate the anonymity guidelines. While I do not wish for this paper to be desk rejected because of it, it should be addressed.

QUESTIONS:
See Strengths And Weaknesses.

LIMITATIONS:
See Strengths And Weaknesses.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces LBMKGC, a benchmark and evaluation suite aimed at assessing large language models (LLMs) for knowledge graph completion (KGC) tasks. The framework covers various KGC paradigms—link prediction, triple classification, and query answering—across multiple datasets. It further dissects performance by task format (e.g., prompt-based vs. scoring-based) and by evaluation granularity (e.g., head/tail/generalized split). The authors compare LLMs such as GPT and GLM with traditional structure-based KGC models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\na) Evaluating LLMs for KGC is important as LLMs increasingly replace specialized symbolic systems.\nb) The benchmark breaks down KGC evaluation across multiple axes, which offers useful diagnostic insights.\nc) The authors run consistent experiments comparing LLMs (in zero-shot or few-shot) with structure-based methods.\n\nWeaknesses\na) The paper uses GLM and ChatGPT, but there is no comparison with strong open-source instruction-tuned LLMs (e.g., LLaMA-2-chat, Mistral, Claude) or modern prompting strategies (e.g., CoT, self-consistency).\nb) Current tasks focus heavily on triple completion/classification. However, real-world KGs involve complex reasoning.\nc) Prompt design, sampling strategy, and evaluation conditions (e.g., few-shot template consistency) can strongly affect LLM performance.\n\nQUESTIONS:\na) The paper uses GLM and ChatGPT, but there is no comparison with strong open-source instruction-tuned LLMs (e.g., LLaMA-2-chat, Mistral, Claude) or modern prompting strategies (e.g., CoT, self-consistency).\nb) Current tasks focus heavily on triple completion/classification. However, real-world KGs involve complex reasoning.\nc) Prompt design, sampling strategy, and evaluation conditions (e.g., few-shot template consistency) can strongly affect LLM performance.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose a novel Large model-driven Balanced  Multimodal Knowledge Graph Completion framework, termed LBMKGC. Initially, LBMKGC employs the Stable Diffusion XL (a large language model) to augment the imbalanced information across modalities. Subsequently, to bridge the semantic gap between heterogeneous modalities, LBMKGC aligns the multimodal embeddings of entities semantically by using the CLIP (Contrastive Language-Image Pre-Training) model. Furthermore, by distinguishing between the perceptual and conceptual attributes of entities, LBMKGC learns entity representations through the adaptive fusion of embeddings from various modalities, guided by structural information. The authors show comprehensive experiments and provide source code of their work for reproducibility.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: The problem is well motivated with the sections of (1) The imbalance of inter-entity information across different modalities and (2)The heterogeneity of intra-entity multimodal information, with the contributions clearly highlighted. The problem formulation along with knowledge graph completion is clearly identified. Figure 2 is highly informative. Experiments are comprehensive and show solid performance.\n\nWeakness: It is unclear to me about the novelty of the proposed architecture. It seems that it is a combination of already proposed techniques e.g., Bert, Vision Transformer, MLP etc. The authors need to more clearly highlight this in their work. \n\nThe Related Work is missing a recent work on KGC, which the authors should reference for completeness and recency. \n\n[KDD 2022] Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22). Association for Computing Machinery, New York, NY, USA, 676–686. https://doi.org/10.1145/3534678.3539350\n\nQUESTIONS:\nIt is unclear to me about the novelty of the proposed architecture. It seems that it is a combination of already proposed techniques e.g., Bert, Vision Transformer, MLP etc. The authors need to more clearly highlight this in their work. \n\nThe Related Work is missing a recent work on KGC, which the authors should reference for completeness and recency.\n\nLIMITATIONS:\nThe authors have provided some discussion on this as part of their Conclusions section, though this can be elaborated upon in its own section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}