Threshold Determination and Engaging Materials Scientists in Ontology Design

Greenberg, Jane; Zhang, Yue; Ogletree, Adrian; Tucker, Garritt J.; Foley, Daniel J.

doi:10.1007/978-3-319-24129-6_4

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Overall, existing ontology infrastructure and knowledge extraction approaches are applicable to materials science. In fact, biology and biomedical ontologies are useful for materials science research, and researchers have been inspired by these developments to develop materials science ontologies [7,11,17]. Related are nascent efforts developing shared metadata and ontology infrastructures for materials science.…”

Section: Ontologies: Shared Infrastructure and Knowledge Extraction A...mentioning

confidence: 99%

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

Greenberg

Zhao

Adair

et al. 2021

Metadata and Semantic Research

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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.

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Section: Ontologies: Shared Infrastructure and Knowledge Extraction A...mentioning

confidence: 99%

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

Greenberg

Zhao

Adair

et al. 2021

Metadata and Semantic Research

Self Cite

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“…Some researchers J. Greenberg et al compare four open source algorithms for keyword extraction, RAKE, Tagger, Kea, and Maui [12]. According to their experiments, RAKE produce 98.57% unique words (69 of 70 unique words of 70 extracted words).…”

Section: Introductionmentioning

confidence: 99%

Complaint Analysis in Indonesian Language Using WPKE and RAKE Algorithm

Wongso

Hanafiah

Hartanto

et al. 2018

IJECE

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<span>Social media provides convenience in communicating and can present two-way communication that allows companies to interact with their customer. Companies can use information obtained from social media to analyze how the communities respond to their services or products. The biggest challenge in processing information in social media like Twitter, is the unstructured sentences which could lead to incorrect text processing. However, this information is very important for companies’ survival. In this research, we proposed a method to extract keywords from tweets in Indonesian language, WPKE. We compared it with RAKE, an algorithm that is language independent and usually used for keyword extraction. Finally, we develop a method to do clustering to groups the topics of complaints with data set obtained from Twitter using the “komplain” hashtag. Our method can obtain the accuracy of 72.92% while RAKE can only obtain 35.42%.</span>

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Threshold Determination and Engaging Materials Scientists in Ontology Design

Cited by 2 publications

References 8 publications

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

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

Complaint Analysis in Indonesian Language Using WPKE and RAKE Algorithm

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