Combinatorial Scientific Discovery: Finding New Concept Combinations Beyond Link Prediction

Yuchen Zeng, Yelman Khan, Shufan Wang, Veronika Thost, Tengfei Ma

Keywords: scientific discovery, graph, link prediction, hypergraph

TL;DR: We propose a new task and new methods for generating new research ideas by predicting combinations of more than two concepts.

Abstract: As the number of publications is growing tremendously, it is more and more a challenge for researchers to read all related literature to find the "white space" in a specific research domain. Automatic scientific discovery has been proposed to help researchers identify new research ideas, but it has generally been limited to finding new combinations of concept pairs using link prediction in a knowledge graph. In this paper, we propose the combinatorial scientific discovery task: predicting combinations of more than two concepts. We standardize the task by providing benchmark datasets and initial models. Our solutions demonstrate the challenge but also the value of the task to find new, meaningful scientific ideas and its advantage over simple link~prediction.