An exploratory analysis: extracting materials science knowledge from unstructured scholarly data

What is it about?

Purpose: The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The research reported in this paper addresses this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science. Design/methodology/approach: We conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER)application. The paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach. The discussion considers current knowledge extractions challenges in materials science. Findings: The results indicate three key needs for researchers to consider for advancing knowledge extraction: 1) the need for materials science focused corpora, 2) the need for researchers to define the scope of the research being pursued, and 3) the need to understand the trade-offs among different knowledge extraction methods. The paper also points to future material science research potential with relation extraction and increased availability of ontologies. Originality: To the best of our knowledge there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.

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