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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References
Fairsharing. https://fairsharing.org/
Industrial ontology foundry. https://www.industrialontologies.org/
Metal-wikipedia entry. https://en.wikipedia.org/wiki/Metal/
Ncbo bioportal. http://bioportal.bioontology.org/
Nist materials registry. https://materials.registry.nist.gov/
Obo foundry. http://www.obofoundry.org/
Anikin, A., Litovkin, D., Sarkisova, E., Petrova, T., Kultsova, M.: Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research. In: IOP Conference Series: Materials Science and Engineering, vol. 483, page 012074 (2019)
Aronson, A.R.: Effective mapping of biomedical text to the umls metathesaurus: The metamap program (2001)
Bizer, C.: The emerging web of linked data. IEEE Intell. Syst. 24(5), 87–92 (2009)
Blaiszik, B., Chard, K., Pruyne, J., Ananthakrishnan, R., Tuecke, S., Foster, I.: The materials data facility: data services to advance materials science research. JOM 68(8), 2045–2052 (2016)
Cheung, K., Hunter, J., Drennan, J.: MatSeek: an ontology-based federated search interface for materials scientists. IEEE Intell. Syst. 24(1), 47–56 (2009)
Conway, M.C., Greenberg, J., Moore, R., Whitton, M., Zhang, L.: Advancing the DFC semantic technology platform via HIVE innovation. In: Garoufallou, E., Greenberg, J. (eds.) MTSR 2013. CCIS, vol. 390, pp. 14–21. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03437-9_3
Draxl, C., Scheffler, M.: Nomad: the fair concept for big data-driven materials science. MRS Bull. 43(9), 676–682 (2018)
Eisenberg, I.W., et al.: Uncovering the structure of self-regulation through data-driven ontology discovery. Nat. Commun. 10(1), 1–13 (2019)
Greenberg, J.: Philosophical foundations and motivation via scientific inquiry. In: Lee, H.-L., Smiraglia, R. (eds.) Ontology in Knowledge Organization, pp. 5–12. Würzburg : Ergon (2015)
Greenberg, J., Losee, R., Agüera, J.R.P., Scherle, R., White, H., Willis, C.: Hive: Helping interdisciplinary vocabulary engineering. Bull. Am. Soc. Inf. Sci. Technol. 37(4), 23–26 (2011)
Greenberg, J., Zhang, Y., Ogletree, A., Tucker, G.J., Foley, D.: Threshold determination and engaging materials scientists in ontology design. In: Garoufallou, E., Hartley, R.J., Gaitanou, P. (eds.) MTSR 2015. CCIS, vol. 544, pp. 39–50. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24129-6_4
Haendel, M.A., Chute, C.G., Robinson, P.N.: Classification, ontology, and precision medicine. N. Engl. J. Med. 379(15), 1452–1462 (2018)
Himanen, L., Geurts, A., Foster, A.S., Rinke, P.: Data-driven materials science: status, challenges, and perspectives. Adv. Sci. 6(21), 1900808 (2019)
Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition (2020)
Rogers, T.: Everything you need to know about polyethylene (pe), creative mechanisms (2015)
Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. Text Min. Appl. theory 1, 1–20 (2010)
Sansone, S.-A., et al.: Fairsharing as a community approach to standards, repositories and policies. Nat. Biotechnol. 37(4), 358–367 (2019)
Segura-Bedmar, I., Martínez, P., Herrero-Zazo, M.: SemEval-2013 task 9: extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013). In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, Georgia, USA, June 2013, pp. 341–350. Association for Computational Linguistics (2013)
Smith, B., et al.: The obo foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol. 25(11), 1251–1255 (2007)
Tshitoyan, V., et al.: Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571(7763), 95–98 (2019)
Wan, K.: What are Scopus APIs and how are these used? (2019). Accessed 17 Oct 2020
Weston, L., et al.: Named entity recognition and normalization applied to large-scale information extraction from the materials science literature, June 2019
Whetzel, P.L., et al.: Bioportal: enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res. 39(suppl_2), W541–W545 (2011)
White, H., Willis, C., Greenberg, J.: The hive impact: contributing to consistency via automatic indexing. In: Proceedings of the 2012 iConference, pp. 582–584 (2012)
Wilkinson, M.D., et al.: The fair guiding principles for scientific data management and stewardship. Sci. Data 3(1), 1–9 (2016)
Zhang, X., Zhao, C., Wang, X.: A survey on knowledge representation in materials science and engineering: an ontological perspective. Comput. Ind. 73, 8–22 (2015)
Zhao, X., Greenberg, J., Menske, V., Toberer, E., Hu, X.: Scholarly big data: computational approaches to semantic labeling in materials science. In: Proceedings of the Workshop on Organizing Big Data, Information, and Knowledge at JCDL 2020 (2020)
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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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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