An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper)

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• 关键词：Ontology ; based natural language processing ; Provenance metadata ; Scientific reproducibility ; Named entity recognition
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10033
• 期：1
• 页码：699-708
• 全文大小：482 KB
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• 作者单位：Joshua Valdez (20)
  Michael Rueschman (21)
  Matthew Kim (21)
  Susan Redline (21)
  Satya S. Sahoo (20)
  
  20. Division of Medical Informatics and Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA
  21. Departments of Medicine, Brigham and Women’s Hospital and Beth Israel Deaconess Medical Center, Harvard University, Boston, MA, USA
  
• 丛书名：On the Move to Meaningful Internet Systems: OTM 2016 Conferences
• ISBN：978-3-319-48472-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10033

文摘

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.