ViSR-KGC, a visual subgraph reasoning approach for KGC, integrates three complementary capabilities to capture semantic correlations: identifying global topology dependencies via representation learning, analyzing local multimodal evidence using VLMs, and providing necessary commonsense knowledge inherent in pre-trained models.
Abstract
Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images. Traditional representation learning approaches follow the embedding-based paradigm and may struggle when relation-specific evidence is limited. Meanwhile, LLM-based reasoning methods typically linearize graph structures into textual prompts, which obscures structural topology and neglects vital visual information. While vision-language models (VLMs) excel at multimodal reasoning, they cannot natively interpret structured graph topology, particularly when it comes to knowledge graphs where nodes and edges carry complex semantics. To bridge this gap, we propose ViSR-KGC, a visual subgraph reasoning approach for KGC. It integrates three complementary capabilities to capture semantic correlations: identifying global topology dependencies via representation learning, analyzing local multimodal evidence using VLMs, and providing necessary commonsense knowledge inherent in pre-trained models. Based on learned multimodal embeddings, our framework first extracts a compact and query-aware subgraph from the MMKG. Then, this subgraph is transformed into a visually interpretable image using a layout strategy selected through empirical comparison. Finally, the visualized subgraph, entity images, textual descriptions, and candidate answers are combined into a unified prompt, enabling the VLM to infer the missing entity.
Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in real-world deployments make inductive KGC difficult, especially under few-shot and zero-shot settings. Multimodal information and Large Language Model (LLM)-derived priors can enrich sparse relational contexts, but they may also introduce noisy or hallucinated evidence. To address these issues, we propose DuPLeR, a \textbf{Du}al-\textbf{P}ath \textbf{L}LM \textbf{R}easoning framework for multimodal few-shot KGC. DuPLeR builds a calibrated relation graph by combining multimodal LLM-derived type priors with factual support structures, and performs dual-level structural reasoning over the refined relation topology. Moreover, a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation. Experiments on eight inductive variants of two multimodal KG (MMKG) benchmarks show that DuPLeR achieves robust performance in data-scarce KGC scenarios.
GraphVerse is introduced, a unified benchmark that jointly evaluates perception, visual reasoning, and text-based graph reasoning in MLLMs under both single-image and paired-image settings and proposes VGR-Score, a process-sensitive metric that evaluates reasoning quality beyond final-answer accuracy.
Yuan-Fu Sun, Yuanhang Ren, Kang Li et al.· 1 citation· ⚡1
OMG-VLM leverages a pretrained VLM as a shared backbone and introduces structure-aware graph adapters that integrate neighborhood information while remaining compatible with the VLM's native embedding space, enabling effective learning over text-attributed, image-attributed, and multimodal-attributed graphs within a single model.
Jia-Yi Yang, Yi-Fang Chen, Yuan-Fu Sun et al.· arXiv.org· 0 citations
This framework performs LLM knowledge elicitation to extract factual knowledge from the model’s internal representations and transforms sentence-level representations into entity-level representations and aligns them within a unified space.
Deyu Chen, Qiyuan Li, Jinguang Gu et al.· 0 citations
: Knowledge graphs organize real-world facts as structured triples and have become a fundamental resource for search engines, question answering, recommender systems, and knowledge-enhanced large language models. However, real-world knowledge graphs remain highly incomplete, which limits their downstream reasoning ability. Existing pre-trained language model-based knowledge graph completion methods provide strong textual semantic representations, but they usually model graph structure only as shallow auxiliary features and remain weak in distinguishing structurally similar entities and topology-near negative samples. To address this limitation, this paper proposes a Dual-Level Structural Context Collaborative Framework (DSC 2 F) for knowledge graph completion. At the instance level, the framework introduces Structural Neighborhood Context (SNC) to inject local neighborhood evidence into the language model input and Relation-Aware Attention (RAA) to condition structural aggregation on the current relation. At the batch level, it constructs topology-aware training batches with biased random walk with restart, so that in-batch negatives are locally related to positive samples and impose stronger structural discrimination pressure. Experiments on WN18RR, FB15k-237, and Wikidata5M show that DSC 2 F achieves the best mean reciprocal rank and Hits@1 on all three datasets, consistently outperforming strong embedding-based and pre-trained language model-based baselines. Ablation studies and structural configuration analyses further verify that SNC, RAA, and Batch-Level Structural Context provide complementary benefits. These results demonstrate that collaborative modeling of instance-level and batch-level structural context can effectively enhance structure-aware entity representation and improve fine-grained entity prediction.
