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- W2890837788 abstract "In this study, we develop a computer-aided material design system to represent and extract knowledge related to material design from natural language texts. A machine learning model is trained on a text corpus weakly labeled by minimal annotated relationship data (~100 labeled relationships) to extract knowledge from scientific articles. The knowledge is represented by relationships between scientific concepts, such as {annealing, grain size, strength}. The extracted relationships are represented as a knowledge graph formatted according to design charts, inspired by the process-structure-property-performance (PSPP) reciprocity. The design chart provides an intuitive effect of processes on properties and prospective processes to achieve the certain desired properties. Our system semantically searches the scientific literature and provides knowledge in the form of a design chart, and we hope it contributes more efficient developments of new materials." @default.
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- W2890837788 date "2018-09-19" @default.
- W2890837788 modified "2023-10-15" @default.
- W2890837788 title "Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity" @default.
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- W2890837788 doi "https://doi.org/10.1080/14686996.2018.1500852" @default.
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