Relation extraction is an important task for understanding relationships between entities, building knowledge graphs, and facilitating knowledge discovery. Pre-trained models can be fine-tuned for relation extraction if a substantial amount of labeled data is available. However, acquiring extensive labeled data is generally challenging. Semi-supervised techniques for low-resource relation extraction, such as self-training, offer a promising solution by leveraging both limited labeled data and vast unlabeled data to mitigate this challenge. Traditional self-training methods use a teacher-student framework, where a student is iteratively trained with pseudo-labels generated by the teacher. This may lead to noisy pseudo-labels and impact performance. To address this limitation, we introduce a new model called RE-AUM-LLM that generates high-quality pseudo-labels using self-training combined with Area Under the Margin (AUM) and Large Language Models (LLMs), such as Llama 3.1. Experimental results on two benchmark datasets show that the proposed approach achieves state-of-the-art results for low-resource relation extraction by comparison with several strong baselines. We will make the code publicly available to enable reproducibility and further research in this area.

Surface water contaminated by fecal bacteria can cause diarrheal illness, threatening human's health (especially among children). In recent years, supervised machine learning (ML) has been used to predict fecal indicator bacteria (FIB) levels. However, training ML models is challenging and, in some cases, even impractical due to sparsity of labeled data in all locations (e.g., in rural areas or low-income countries). In this paper, we introduce the largest water quality dataset available collected from beaches in Chicago and San Diego, USA. We utilized various models to predict historical FIB levels on this dataset establishing strong baseline models for supervised learning and transfer learning. Our models include Random Forest (RF), extreme gradient boosting (XGBoost), and attentionbased tabular deep learning (TabNet) models. Additionally, given the widespread use of large language models (LLMs), we have fine-tuned the LLaMA3-8B model for regression in a tabular-to-text setting. Our results show that supervised and unsupervised domain adaptation methods can enhance transfer learning performance. Specifically, the supervised methods, especially RF, represent a promising solution for FIB level prediction, while domain adaptation could be successfully employed to predict FIB levels in locations where they are rarely measured. Our code and dataset are available on: https://github.com/aliielahi/ONR-WQ.

A knowledge graph focusing on water quality in relation to health risks posed by water activities (such as diving or swimming) is not currently available. To address this limitation, we first use existing resources to construct a knowledge graph relevant to water quality and health risks using KNowledge Acquisition and Representation Methodology (KNARM). Subsequently, we explore knowledge graph completion approaches for maintaining and updating the graph. Specifically, we manually identify a set of domain-specific UMLS concepts and use them to extract a graph of approximately 75,000 semantic triples from the Semantic MEDLINE database (which contains head-relation-tail triples extracted from PubMed). Using the resulting knowledge graph, we experiment with the KG-BERT approach for graph completion by employing pre-trained BERT/RoBERTa models and also models fine-tuned on a collection of water quality and health risks abstracts retrieved from the Web of Science. Experimental results show that KG-BERT with BERT/RoBERTa models fine-tuned on a domain-specific corpus improves the performance of KG-BERT with pre-trained models. Furthermore, KG-BERT gives better results than several translational distance or semantic matching baseline models.