论文标题
MATKG：材料科学中最大的知识图 - 实体，关系和通过图表示学习的链接预测
MatKG: The Largest Knowledge Graph in Materials Science -- Entities, Relations, and Link Prediction through Graph Representation Learning
论文作者
论文摘要
本文介绍了MATKG，这是一个新颖的图表数据库，涉及材料科学的关键概念，涵盖了传统的材料结构 - 构建范式。 MATKG是通过基于变压器的大型语言模型自主生成的，并通过统计共发生映射生成伪本体学模式。目前，MATKG包含来自80,000个实体的200万多个独特的关系三元。这允许以独特的分辨率和规模对材料知识进行策划的分析，查询和可视化。此外，知识图嵌入模型用于学习图中节点的嵌入表示形式，用于下游任务，例如链接预测和实体歧义。当用作知识库时，MATKG允许数据快速传播和同化数据，同时可以在培训为嵌入模型时发现新关系。
This paper introduces MatKG, a novel graph database of key concepts in material science spanning the traditional material-structure-property-processing paradigm. MatKG is autonomously generated through transformer-based, large language models and generates pseudo ontological schema through statistical co-occurrence mapping. At present, MatKG contains over 2 million unique relationship triples derived from 80,000 entities. This allows the curated analysis, querying, and visualization of materials knowledge at unique resolution and scale. Further, Knowledge Graph Embedding models are used to learn embedding representations of nodes in the graph which are used for downstream tasks such as link prediction and entity disambiguation. MatKG allows the rapid dissemination and assimilation of data when used as a knowledge base, while enabling the discovery of new relations when trained as an embedding model.
