Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of
  Cross-modal Knowledge Graph for Material Property Prediction
Guangxuan Song Dongmei Fu Zhongwei Qiu Zijiang Yang Jiaxin Dai Lingwei Ma Dawei Zhang
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.