  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 a cross-modal KG using semantic nodes and numerical proxy nodes. It
captures both types of information by projecting KG into a canonical KG and
utilizes a graph neural network to predict material properties. In this
process, a novel projection prediction loss is proposed to extract semantic
features from numerical information. NR-KG facilitates end-to-end processing of
cross-modal data, mining relationships and cross-modal information in
small-sample datasets, and fully utilizes valuable experimental data to enhance
material prediction. We further propose two new High-Entropy Alloys (HEA)
property datasets with semantic descriptions. NR-KG outperforms
state-of-the-art (SOTA) methods, achieving relative improvements of 25.9% and
16.1% on two material datasets. Besides, NR-KG surpasses SOTA methods on two
public physical chemistry molecular datasets, showing improvements of 22.2% and
54.3%, highlighting its potential application and generalizability. We hope the
proposed datasets, algorithms, and pre-trained models can facilitate the
communities of KG and AI for materials.
