Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction

arXiv:2312.09744v2 Announce Type: replace
Abstract: Using machine learning (ML) techniques to predict material properties is a crucial research topic. These properties depend on numerical data and semantic factors. Due to the limitations of small-sample datasets, existing methods typically adopt ML algorithms to regress numerical properties or transfer other pre-trained knowledge graphs (KGs) to the material. However, these methods cannot simultaneously handle semantic and numerical information. In this paper, we propose a numerical reasoning method for material KGs (NR-KG), which constructs …

abstract algorithms arxiv cond-mat.mtrl-sci cs.lg data datasets gap graph knowledge knowledge graph limitations machine machine learning material ml algorithms modal numerical prediction property reasoning research sample semantic small type