Joint entity and relation extraction represent a critical task in knowledge representation, but often suffers from the bottleneck of requiring large amounts of labeled data, which is expensive and laborious to obtain. While Semi-Supervised Learning (SSL) offers a way to leverage unlabeled data, traditional methods face limitations in generating high-quality, diverse augmentations for text. This article introduces a novel framework that synergistically combines SSL with Large Language Models (LLMs) to improve joint entity and relation extraction, especially in low-resource settings. Our approach utilizes LLMs to generate semantically coherent and diverse augmented data from unlabeled samples. These augmented samples, along with limited labeled data, are used within an SSL framework employing consistency regularization and pseudo-labeling to train the extraction model. Crucially, the framework incorporates an iterative refinement mechanism where the performance of the SSL component informs the parameter-efficient fine-tuning of the LLM, leading to progressively better data augmentation and model accuracy. We demonstrate through extensive experiments on four benchmark datasets that our proposed method significantly outperforms existing state-of-the-art approaches, particularly when labeled data are scarce. The framework’s design is adaptable and can be integrated with various existing joint extraction models, showcasing its generalizability and practical utility.

Lower-dimensional temporal knowledge graph embedding (TKGE) models are crucial for practical applications and resource-limited scenarios, although existing models employ higher-dimensional embeddings in training. In this paper, we propose a new framework for distilling TKGE models via an easy to hard pedagogical principle. The framework utilizes a learnable curriculum temperature (CT) module to optimize and guide the knowledge distillation process dynamically, ensuring that the entire procedure adheres to the principle. It also employs a self-adaptive attention mechanism to endeavor to achieve efficient transfer of knowledge from higher-dimensional models to lower-dimensional ones. Evaluation on various TKGE models and datasets demonstrates the proposed approach significantly reduces the model's parameters without noticeably affecting its performance.

Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks . Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework.