Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

Despite the huge accumulation of scientific literature, it is inefficient and laborious to manually search it for useful information to investigate structure-activity relationships. Here, we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal superalloys. Firstly, the credibility of abstracts is quantified in terms of source timeliness, publication authority and author’s academic standing. Next, eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts, achieving an accuracy of 85.10%. Thirdly, by formulating 12 naming rules for the alloy brands derived from the recognized entities, we extract the target entities and refine them as domain knowledge through the credibility analysis. Following this, we also map out the academic cooperative “Author-Literature-Institute” network, characterize the generations of Nickel-based single crystal superalloys, as well as obtain the fractions of the most important chemical elements in superalloys. The extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.