Integrated knowledge graphs form the foundation of numerous data-driven applications, including search engines, conversational agents, and e-commerce solutions. Declarative link discovery frameworks utilize link specifications to define the conditions necessary for establishing a link between knowledge graphs’ resources. Despite domain expertise, defining such link specifications remains challenging due to their intricate syntax, threshold tuning, and the need to precisely express complex linking logic. To address this challenge, we propose NL2LS, a novel language-driven approach that leverages large language models to automatically translate natural language (NL) into link specifications (LSs), enabling domain experts and practitioners to express correct and complex linking rules more effectively. NL2LS employs three distinct training paradigms to handle the complexity of link specifications: zero-shot learning, one-shot learning and supervised fine-tuning. We evaluated NL2LS using different large language model architectures in comparison with a rule-based baseline model on different multi-lingual datasets. Our evaluation using BLEU, METEOR, ChrF++, and TER metrics demonstrates that NL2LS effectively translates natural language into link specifications, lowering the technical barrier and assisting users in specifying link rules more intuitively.