Title: Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science
This paper explores computational, semantic labeling for scholarly big data in materials science. We report on a baseline comparative analysis involving ontology-based automatic indexing with the Helping Interdisciplinary Vocabulary Engineering (HIVE-4-MAT) application, using the RAKE algorithm, and the MATScholar system, which uses named entity recognition (NER), supported by an RNN (Recursive Neural Network). Results demonstrate that ontology-based automatic indexing requires less preparation time and provides useful output supporting recall; while NER/RNN requires greater preparation, but produces more precise labels that are likely better for deep learning. more »« less
Greenberg, J; Zhao, X; Adair, J; Boone, J; Hu, X.(
, Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science)
Garoufallou, E; Ovalle-Perandones, M.A.
(Ed.)
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.
A significant amount of literature is available on biocorrosion, which makes manual extraction of crucial information such as genes and proteins a laborious task. Despite the fast growth of biology related corrosion studies, there is a limited number of gene collections relating to the corrosion process (biocorrosion). Text mining offers a potential solution by automatically extracting the essential information from unstructured text. We present a text mining workflow that extracts biocorrosion associated genes/proteins in sulfate-reducing bacteria (SRB) from literature databases (e.g., PubMed and PMC). This semi-automatic workflow is built with the Named Entity Recognition (NER) method and Convolutional Neural Network (CNN) model. With PubMed and PMCID as inputs, the workflow identified 227 genes belonging to several Desulfovibrio species. To validate their functions, Gene Ontology (GO) enrichment and biological network analysis was performed using UniprotKB and STRING-DB, respectively. The GO analysis showed that metal ion binding, sulfur binding, and electron transport were among the principal molecular functions. Furthermore, the biological network analysis generated three interlinked clusters containing genes involved in metal ion binding, cellular respiration, and electron transfer, which suggests the involvement of the extracted gene set in biocorrosion. Finally, the dataset was validated through manual curation, yielding a similar set of genes as our workflow; among these, hysB and hydA, and sat and dsrB were identified as the metal ion binding and sulfur metabolism genes, respectively. The identified genes were mapped with the pangenome of 63 SRB genomes that yielded the distribution of these genes across 63 SRB based on the amino acid sequence similarity and were further categorized as core and accessory gene families. SRB’s role in biocorrosion involves the transfer of electrons from the metal surface via a hydrogen medium to the sulfate reduction pathway. Therefore, genes encoding hydrogenases and cytochromes might be participating in removing hydrogen from the metals through electron transfer. Moreover, the production of corrosive sulfide from the sulfur metabolism indirectly contributes to the localized pitting of the metals. After the corroboration of text mining results with SRB biocorrosion mechanisms, we suggest that the text mining framework could be utilized for genes/proteins extraction and significantly reduce the manual curation time.
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 purpose of this study is to address 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 The authors 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. This 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. Findings The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies. Originality/value To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.
Bajaj, Goonmeet; Kursuncu, Ugur; Gaur, Manas; Usha Lokala, Manas; Hyder, Ayaz; Parthasarathy, Srinivasan; Sheth, Amit(
, Studies in health technology and informatics)
d public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for human labor and train models incorporating domain-specific (e.g., drug use) external knowledge to recognize domain specific entities. We capture entities related the drug use and their trends in government epidemiology reports, with an improvement of 8% in F1-score.
Wang, Xuan; Hu, Vivian; Song, Xiangchen; Garg, Shweta; Xiao, Jinfeng; Han, Jiawei(
, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)
Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts. On the other hand, domain-specific ontologies and knowledge bases (KBs) can be easily accessed, constructed, or integrated, which makes distant supervision realistic for fine-grained chemistry NER. In distant supervision, training labels are generated by matching mentions in a document with the concepts in the knowledge bases (KBs). However, this kind of KB-matching suffers from two major challenges: incomplete annotation and noisy annotation. We propose ChemNER, an ontology-guided, distantly-supervised method for fine-grained chemistry NER to tackle these challenges. It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation. It significantly improves the distant label generation for the subsequent sequence labeling model training. We also provide an expert-labeled, chemistry NER dataset with 62 fine-grained chemistry types (e.g., chemical compounds and chemical reactions). Experimental results show that ChemNER is highly effective, outperforming substantially the state-of-the-art NER methods (with .25 absolute F1 score improvement).
Zhao, X. Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science. Retrieved from https://par.nsf.gov/biblio/10185098. IEEEACM Joint Conference on Digital Libraries JCDL .
Zhao, X. Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science. IEEEACM Joint Conference on Digital Libraries JCDL, (). Retrieved from https://par.nsf.gov/biblio/10185098.
Zhao, X.
"Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science". IEEEACM Joint Conference on Digital Libraries JCDL (). Country unknown/Code not available. https://par.nsf.gov/biblio/10185098.
@article{osti_10185098,
place = {Country unknown/Code not available},
title = {Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science},
url = {https://par.nsf.gov/biblio/10185098},
abstractNote = {This paper explores computational, semantic labeling for scholarly big data in materials science. We report on a baseline comparative analysis involving ontology-based automatic indexing with the Helping Interdisciplinary Vocabulary Engineering (HIVE-4-MAT) application, using the RAKE algorithm, and the MATScholar system, which uses named entity recognition (NER), supported by an RNN (Recursive Neural Network). Results demonstrate that ontology-based automatic indexing requires less preparation time and provides useful output supporting recall; while NER/RNN requires greater preparation, but produces more precise labels that are likely better for deep learning.},
journal = {IEEEACM Joint Conference on Digital Libraries JCDL},
author = {Zhao, X.},
}
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