Text Mining for Materials Science Knowledge Discovery
Explore how large-scale text mining can unveil hidden insights in materials science, converting unstructured data into structured knowledge. Overcoming challenges such as annotating scientific literature, this research harnesses the power of Large Language Models (LLMs) and few-shot learning for semantic text understanding. Applications include dataset synthesis and knowledge graph construction. Stay updated with the evolving AI-ready data workflow and current progress in training models like GPT4.
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Presentation Transcript
Metadata, LLMs and Materials Synthesis Mining Author: Xintong Zhao Drexel University #2118201
SCOPE Our research interest: Discover knowledge of materials science through large scale text mining Convert information in unstructured text to structured knowledge
SCOPE A conceptual map plays an essential role to guide the text mining process.
CHALLENGE Volume of materials literature Scientific annotation is time- consuming and requires significant domain expertise Inter-annotator agreement rate Knowledge adaptation of LLMs
OPPORTUNITIES Large Language Models (LLMs) combined with few-shot learning shows high-potential to address these challenge Good semantic understanding of scientific texts
APPLICATIONS Synthesis dataset generated from large number of scientific outputs Knowledge graph construction Information Retrieval
CURRENT PROGRESS Training GPT4 to teach BERT Precision 87% Lower Recall
#2118201 THANK YOU!
