MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowle...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Mullick, Ankan, Ghosh, Akash, G Sai Chaitanya, Ghui, Samir, Nayak, Tapas, Lee, Seung-Cheol, Bhattacharjee, Satadeep, Goyal, Pawan
Format: Article
Language:English
Subjects:
Construction materials
Electric potential
Encoders-Decoders
Knowledge bases (artificial intelligence)
Voltage
Online Access:Get full text
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Summary:Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder-decoder framework, to jointly extract entities and relations from material science articles as a triplet (\(entity1, relation, entity2\)). Specifically, we target the battery materials and identify five relations to work on - conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig 1. The material information is extracted from material science literature in the form of entity-relation triplets using MatSciRE.
ISSN:2331-8422
DOI:10.48550/arxiv.2401.09839