TL;DR
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.
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.
Large Language Models (LLMs) show considerable potential for materials-science question answering. However, LLM responses may still be affected by unsupported parametric associations, while dense Retrieval-Augmented Generation (RAG) can fragment relational evidence across text chunks. Moreover, general graph-based retrieval does not necessarily preserve the hierarchical relations and factual attributes required to resolve implicit material constraints. To address these limitations, we propose MCTD-KG, a multi-source heterogeneous knowledge graph integrated with a Knowledge-Enhanced RAG framework for complex material question answering. MCTD-KG adopts a Classification–Term–Data ontology to connect disciplinary taxonomies, domain concepts, semantic relations, and empirical records from toolbooks and the scientific literature. Through LLM-assisted knowledge extraction, entity normalization, and multi-source integration, the resulting graph contains more than 530,000 entities across three layers, including 61,768 text-extracted Term-layer entities. During inference, Dual-Channel Retrieval jointly retrieves query-relevant relational paths and associated material attributes, while an explicit semantic filtering stage screens candidate evidence against the query constraints. Evaluation on an expert-validated benchmark of 1577 questions shows that the proposed framework achieves an overall accuracy of 68.48%, compared with 17.40% for the zero-shot Pure LLM, 24.79% for the best Vanilla RAG setting, and 44.96% for GraphRAG. It also achieves 45.22% accuracy on four-hop questions, compared with 39.49% for GraphRAG. These results indicate that integrating multi-source domain knowledge with relation-preserved retrieval and attribute-supported filtering provides more focused and inspectable evidence, thereby supporting more accurate complex material question answering.
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 is one of the core tasks in the field of knowledge graphs, which predicts missing links through inference of existing facts. With the advancement of deep learning technology, utilizing end-to-end deep learning models for knowledge graph completion has become a cutting-edge research direction. However, the performance of current knowledge graph completion models is still limited by text quality and incomplete structure. To address this issue, this paper proposes a method of using large models for data augmentation to improve the inference performance of the model. Specifically, we first introduced a pre-extractor model based on a hybrid architecture of rules and neural networks, which is used to identify long tail entities in the dataset and generate several candidate tail entities through relationships. Then, we use this data to have the Large Language Model infer the most factual triplet. Finally, we use the enhanced dataset for predictive inference. SAGE achieves better results on three standard KGC datasets. For instance, on the FB15K-237 dataset, compared to the SimKGC baseline model, SAGE improves Hits@1 by 1%, Hits@3 by 0.9%, and Hits@10 by 1.6%.
Integrating structured knowledge graphs (KGs) with Large Language Models (LLMs) is essential for trustworthy, knowledge intensive conversational systems. However, existing Retrieval Augmented Generation (RAG) methods typically rely on a retrieval-as-context paradigm that linearizes structured subgraphs into unstructured prompt tokens. This approach not only flattens rich structural dependencies but also leads to context inflation and evidence attenuation in multi-turn dialogues. To address these limitations, we propose KGA-LM, a framework that integrates external knowledge via representation-level grounding. Rather than treating retrieved evidence as transient input artifacts, KGA-LM encodes compact multi-hop subgraphs using a Graph Transformer and fuses them into the LLM decoder through a compatibility-aware latent interface. This design aligns the heterogeneous latent spaces of the graph encoder and the LLM, while a dual-gated fusion mechanism dynamically regulates the influence of non-parametric graph evidence across turns. Experiments on multiple conversational benchmarks demonstrate that KGA-LM significantly improves factual accuracy and reduces hallucination compared to prompt-linearized baselines. Crucially, by decoupling knowledge injection from prompt length, our approach mitigates retrieval signal decay under long contexts, offering a superior trade-off between grounding quality and inference efficiency.
Few-shot knowledge graph completion (FKGC) aims to enhance knowledge reasoning for newly emerging relations using only a limited number of supporting triples. Given the inherent growth and evolution of knowledge graphs (KGs), FKGC faces greater challenges in continual learning (CL) scenarios, as models tend to rely more heavily on historical knowledge in few-shot settings. Existing studies have mainly focused on preserving old knowledge to mitigate catastrophic forgetting. However, these strategies often overlook that not all knowledge is worth retaining. Some prior knowledge may conflict with newly acquired knowledge, either structurally or semantically, distorting entity representations and relation patterns and weakening few-shot inference for new relations. To bridge this research gap, we propose a novel FKGC framework named CSKU, which achieves dynamic knowledge adaptation by selectively unlearning structural and semantic conflicts. Specifically, to preserve entity representation consistency, we design a graph-structured dynamic progressive unlearning mechanism that adaptively mitigates structural conflicts during training. Meanwhile, we introduce a semantic-guided selective unlearning mechanism that dynamically attenuates the impact of conflicting relations. Experimental results on multiple real-world FKGC datasets demonstrate that CSKU significantly outperforms state-of-the-art baselines in both forgetting mitigation and few-shot completion, validating the effectiveness of the proposed selective knowledge unlearning strategy. Our code and datasets are publicly available. https://anonymous.4open.science/r/CSKU.