{"type":"ScholarlyArticle","title":"Rewarding Explainability in Drug Repurposing with Knowledge Graphs","authors":[{"name":"Susana Nunes","slug":"susana-nunes","given":"Susana","family":"Nunes","affiliation":""},{"name":"Samy Badreddine","slug":"samy-badreddine","given":"Samy","family":"Badreddine","affiliation":""},{"name":"Catia Pesquita","slug":"catia-pesquita","given":"Catia","family":"Pesquita","affiliation":""}],"abstract":"Knowledge graphs (KGs) are powerful tools for modelling complex, multi-relational data and supporting hypothesis generation, particularly in applications like drug repurposing. However, for predictive methods to gain acceptance as credible scientific tools, they must ensure not only accuracy but also the capacity to offer meaningful scientific explanations.\r\n\r\nThis paper presents a novel approach REx, for generating scientific explanations based in link prediction in knowledge graphs. It employs reward and policy mechanisms that consider desirable properties of scientific explanation to guide a reinforcement learning agent in the identification of explanatory paths within a KG. The approach further enriches explanatory paths with domain-specific ontologies, ensuring that the explanations are both insightful and grounded in established biomedical knowledge. \r\n\r\nWe evaluate our approach in drug repurposing using three popular knowledge graph benchmarks. The results clearly demonstrate its ability to generate explanations that validate predictive insights against biomedical knowledge and that outperform the state-of-the-art approaches in predictive performance, establishing REx as a relevant contribution to advance AI-driven scientific discovery.","doi":"10.24963/ijcai.2025/515","url":"https://ijcai.org/proceedings/2025/515","pdf":"https://ijcai.org/proceedings/2025/0515.pdf","markdown":"https://ijcai.org/proceedings/2025/515.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/515","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":515,"pages":{"first":4624,"last":4632},"keywords":["Multidisciplinary Topics and Applications: MTA: Bioinformatics","Multidisciplinary Topics and Applications: MTA: Health and medicine","Knowledge Representation and Reasoning: KRR: Semantic Web"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}