Open Relation Extraction (OpenRE) aiming to extract relational facts from open-domain cor-pora is a sub-task of Relation Extraction and a crucial upstream process for many other NLPtasks. However various previous clustering-based OpenRE strategies either confine themselves to unsupervised paradigms or can not directly build a unified relational semantic space henceimpacting down-stream clustering. In this paper we propose a novel supervised learning frame-work named MORE-RLL (Metric learning-based Open Relation Extraction with Ranked ListLoss) to construct a semantic metric space by utilizing Ranked List Loss to discover new rela-tional facts. Experiments on real-world datasets show that MORE-RLL can achieve excellent performance compared with previous state-of-the-art methods demonstrating the capability of MORE-RLL in unified semantic representation learning and novel relational fact detection.