HIVE-4-MAT: Advancing the Ontology Infrastructure for Materials Science

Abstract

This paper introduces Helping Interdisciplinary Vocabulary Engineering for Materials Science (HIVE-4-MAT), an automatic linked data ontology application. The paper provides contextual background for materials science, shared ontology infrastructures, and knowledge extraction applications. HIVE-4-MAT’s three key features are reviewed: 1) Vocabulary browsing, 2) Term search and selection, and 3) Knowledge Extraction/Indexing, as well as the basics of named entity recognition (NER). The discussion elaborates on the importance of ontology infrastructures and steps taken to enhance knowledge extraction. The conclusion highlights next steps surveying the ontology landscape, including NER work as a step toward relation extraction (RE), and support for better ontologies.

The Intersection Between Semantic Web and Materials Science

“…These methods particularly benefit from detailed and rich semantic vocabularies. In this context, Greenberg et al [ 51 ] presented the “automatic” linked data ontology application HIVE‐4‐MAT (Helping Interdisciplinary Vocabulary Engineering for Materials discovery). The application demonstrates the power of the combined use of ontological resources with NLP by extracting knowledge and rules from unstructured text in a structured way.…”

“…In total, ontology-based applications were observed in 14 of the selected papers. [28][29][30][32][33][34][35]37,38,40,41,[50][51][52] These applications include the reuse, combination, and extension of existing ontologies and semantic resources (Figure 3b,i,ii). An example is given by Li et al [33] demonstrating the extension of two nanotechnology ontologies with new concepts and axioms in a two-step procedure.…”

Documenting Information Processes and Practices: Paradata, Provenance Metadata, Life‐Cycles and Pipelines

“…The different strands of work do also seem to demonstrate somewhat different disciplinary influences. Explanatory research is more often stemming from knowledge organisation and information‐modelling (e.g., Greenberg et al, 2021; Leipzig et al, 2020; Mayer et al, 2013b) background whereas in descriptive research it is possible to trace influences of heritage studies, anthropology and science and technology studies (e.g. Huvila, 2020; Sköld, 2015; Trace & Karadkar, 2017).…”

“…In critical operational settings such as the energy and aviation industries, keeping a meticulously structured logbook is a crucial safety measure (Schmidt & van Hoof, 2012). Significant progress has also been made in computational semantic labeling of data (e.g., Greenberg et al, 2021; Zhao et al, 2020). In the context of data‐intensive scientific work, calls for reproducibility of research and the reusability of research data have contributed to a comparable interest in formalising the documentation of work procedures.…”

“…Many of them are well-documented, for instance, in earlier knowledge organisation and information behaviour research (e.g. Greenberg et al, 2021;Huvila, 2020;Sköld, 2017;Thomer et al, 2018;Trace, 2020), others in the work stemming from science and technology studies (STS) and in the growing body of information science research that combines information and STS perspectives (e.g. Borgman et al, 2015;Borgman et al, 2019;Gregory et al, 2020;Huvila, 2016;Huvila et al, 2021).…”