Chapter title |
An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper)
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Chapter number | 43 |
Book title |
On the Move to Meaningful Internet Systems: OTM 2016 Conferences
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Published in |
On the move to meaningful Internet systems ... : CoopIS, DOA, and ODBASE : Confederated International Conferences, CoopIS, DOA, and ODBASE ... proceedings. OTM Confederated International Conferences, January 2016
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DOI | 10.1007/978-3-319-48472-3_43 |
Pubmed ID | |
Book ISBNs |
978-3-31-948471-6, 978-3-31-948472-3
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Authors |
Joshua Valdez, Michael Rueschman, Matthew Kim, Susan Redline, Satya S. Sahoo |
Editors |
Christophe Debruyne, Hervé Panetto, Robert Meersman, Tharam Dillon, eva Kühn, Declan O'Sullivan, Claudio Agostino Ardagna |
Abstract |
Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility. Reproducibility of results reported by previous research studies is a foundational component of scientific advancement. This is highlighted by the recent initiative by the US National Institutes of Health called "Principles of Rigor and Reproducibility". In this paper, we describe an effective approach to extract provenance metadata from published biomedical research literature using an ontology-enabled NLP platform as part of the Provenance for Clinical and Healthcare Research (ProvCaRe). The ProvCaRe-NLP tool extends the clinical Text Analysis and Knowledge Extraction System (cTAKES) platform using both provenance and biomedical domain ontologies. We demonstrate the effectiveness of ProvCaRe-NLP tool using a corpus of 20 peer-reviewed publications. The results of our evaluation demonstrate that the ProvCaRe-NLP tool has significantly higher recall in extracting provenance metadata as compared to existing NLP pipelines such as MetaMap. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 35 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 10 | 29% |
Student > Ph. D. Student | 6 | 17% |
Student > Master | 5 | 14% |
Student > Doctoral Student | 4 | 11% |
Student > Bachelor | 3 | 9% |
Other | 4 | 11% |
Unknown | 3 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 8 | 23% |
Medicine and Dentistry | 8 | 23% |
Engineering | 5 | 14% |
Agricultural and Biological Sciences | 3 | 9% |
Biochemistry, Genetics and Molecular Biology | 2 | 6% |
Other | 4 | 11% |
Unknown | 5 | 14% |