Zero-Shot Relation Extraction (ZSRE) aims to predict unseen relations for given entity pairs in sentences. Existing methods typically operate from a local perspective, predicting the relation for each entity pair (given its corresponding sentence) in isolation. Consequently, they often fail to distinguish between unseen, semantically similar relations, particularly when the sentence phrasing is ambiguous.To address this limitation, we propose **G-NSR**, a novel ZSRE framework built upon a **G**lobal **N**euro-**S**ymbolic **R**easoner architecture, specifically designed to enable global reasoning across a set of predictions. The key idea is to model the logical relationships among multiple predictions, and perform neuro-symbolic reasoning to ensure logically consistent and more accurate predictions. Specifically, we first introduce Duality Type-Constrained Relation Schemas, which formulate each candidate relation as a pair of complementary positive-negative propositions. These propositions are then synthesized by our designed Neuro-Symbolic Reasoner, which explicitly models their logical interdependencies. By approximating logical rules, the reasoner allows high-confidence predictions to serve as evidence for refining incorrect results, ensuring the final predictions are logically consistent and more accurate. Extensive experiments on widely used datasets demonstrate that our method significantly outperforms existing approaches and establishes new state-of-the-art results across all evaluation settings. Our code is available at https://anonymous.4open.science/r/G-NSR

Zero-shot Relation Triplet Extraction (ZSRTE) aims to extract triplets from the context where the relation patterns are unseen during training. Due to the inherent challenges of the ZSRTE task, existing extractive ZSRTE methods often decompose it into named entity recognition and relation classification, which overlooks the interdependence of two tasks and may introduce error propagation. Motivated by the intuition that crucial entity attributes might be implicit in the relation labels, we propose a Relation-Centric joint ZSRTE method named Re-Cent. This approach uses minimal information, specifically unseen relation labels, to extract triplets in one go through a unified model. We develop two span-based extractors to identify the subjects and objects corresponding to relation labels, forming span-pairs. Additionally, we introduce a relation-based correction mechanism that further refines the triplets by calculating the relevance between span-pairs and relation labels. Experiments demonstrate that Re-Cent achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor.