Combinatorial Scientific Discovery: Finding New Concept Combinations Beyond Link Prediction



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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.

Please cite our work using the BibTeX below.

title={Combinatorial Scientific Discovery: Finding New Concept Combinations Beyond Link Prediction},
author={Yuchen Zeng and Yelman Khan and Shufan Wang and Veronika Thost and Tengfei Ma},
booktitle={ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing},
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