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

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Metadata and Semantic Research (MTSR 2020)

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.

Supported by NSF Office of Advanced Cyberinfrastructure (OAC): #1940239.

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Acknowledgment

The research reported on in this paper is supported, in part, by the U.S. National Science Foundation, Office of Advanced Cyberinfrastructure (OAC): Grant: #1940239. Thank you also to researchers in Professor Steven Lopez’s lab, Northeastern University, and Semion Saiki, Kebotix for assistance in developing the entity set for organic materials.

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Correspondence to Jane Greenberg .

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Greenberg, J., Zhao, X., Adair, J., Boone, J., Hu, X.T. (2021). HIVE-4-MAT: Advancing the Ontology Infrastructure for Materials Science. In: Garoufallou, E., Ovalle-Perandones, MA. (eds) Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science, vol 1355. Springer, Cham. https://doi.org/10.1007/978-3-030-71903-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-71903-6_28

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