A nonasymptotic risk bound is established that disentangles pretraining representation error from labeled-sample complexity, formally quantifying the benefit of large-scale unlabeled data for downstream knowledge prediction.
Abstract
Knowledge graph learning provides a powerful framework for representing and inferring structured knowledge, with broad practical applications. However, the scarcity of relation-specific labeled triples per entity hinders the training of expressive models, and the ad hoc design of scoring functions limits generalizability and lacks theoretical grounding. We address both issues with a theoretically grounded, end-to-end training framework that extends and subsumes existing methods. Our framework is a two-stage procedure: unsupervised pretraining over heterogeneous corpora followed by supervised learning with multiple relation types. We establish a nonasymptotic risk bound that disentangles pretraining representation error from labeled-sample complexity, formally quantifying the benefit of large-scale unlabeled data for downstream knowledge prediction. Synthetic experiments validate each theoretical component, and real-world experiments confirm the effectiveness of our approach on large-scale knowledge graph benchmarks.
A multimodal model that combines two complementary ideas: a self-supervised method that enables a GNN encoder pretrained on one dataset to operate directly on another dataset with a different node-feature dimensionality, without rebuilding the model or realigning the data is investigated.
Fumiaki Kimino, Ryoma Sato Sokendai, National Institute of Informatics· 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.· Proceedings of the Thirty-Fi...· 0 citations
A pre-extractor model based on a hybrid architecture of rules and neural networks is introduced, which is used to identify long tail entities in the dataset and generate several candidate tail entities through relationships to improve the inference performance of the model.
Er-Zhuo Xu· Poster Volume 0008 The 2026...· 0 citations
SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever, suggests that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.
LUCID, an LLM-guided, interpretable, training-free, and unsupervised community detection method, designed as a four-stage pipeline that achieves state-of-the-art performance and consistently outperforms leading unsupervised and semi-supervised baselines.
Aoting Zeng, Kai Wang, Jianwei Wang et al.· 0 citations
Inductive link prediction in knowledge graphs refers to the task of inferring known relations between entities unseen during training. Most existing approach-es are limited to predicting only known relations and struggle to generalize to un-seen relations, which restricts their utility in dynamic settings. To address this challenge, we propose a novel inductive link prediction approach named RGIILP. Specifically, we construct a relation graph from the source knowledge graph and design a neural network model that enables interactive feature propagation be-tween entities and relations. Furthermore, we introduce the mutual information maximization mechanism between global and local representations to capture the global structural information of the graph. Experiments on several benchmark da-tasets demonstrate that RGIILP outperforms existing state-of-the-art methods for inductive link prediction task.
Hong-Bo Liu· Poster Volume 0008 The 2026...· 0 citations
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