Attribute Value Extraction Mechanism of Constructed Wetlands Information

Nevado Amell, Mauricio Andres, Awais, Muhammad, Ragul, Sowmiya, Brüggemann, Kurt and Avellán, Tamara, (2019). Attribute Value Extraction Mechanism of Constructed Wetlands Information. MethodsX, 1-20

Document type:
Article
Collection:
UNU-FLORES Articles

Metadata
Links
Versions
Statistics
  • Sub-type Journal article
    Author Nevado Amell, Mauricio Andres
    Awais, Muhammad
    Ragul, Sowmiya
    Brüggemann, Kurt
    Avellán, Tamara
    Title Attribute Value Extraction Mechanism of Constructed Wetlands Information
    Appearing in MethodsX
    Publication Date 2019-04-30
    Place of Publication Oxford
    Publisher Elsevier
    Start page 1
    End page 20
    Language eng
    Abstract Constructed Wetlands (CWs) are a nature-based solution for the treatment of wastewater. The CWetlands- the Constructed Wetlands Knowledge Platform - intends to help understand how CWs can support achieving the Sustainable Development Goals. The platform is based on more than 100 attributes of CWs including criteria for design criteria, operation, efficiency, climate and other geographical factors. This study aims at developing an attribute value extraction mechanism tool in R to extract meaningful information from peer-reviewed journal articles in a reliable and fast way. • The tool focuses on the extraction of eighteen different extractable attributes gathered in 4 classes, which describe the main characteristics of CW systems. • The process contains 4 sub-processes: 1-2) the papers are accessed and pre-processed, 3) the attributes are extracted by two data mining techniques: Keyword Match and Web Scrap, and 4) the values are exported to a database. • For the development and testing of the tool, 13 articles were used. The tool achieved a mean success rate of 79% in 30 minutes; less compared with the 480 minutes needed with a manual approach. In further versions, the tool is expected to obtain a higher success rate in all attributes.
    UNBIS Thesaurus DATABASES
    Keyword nature-based solutions
    text mining
    natural processing
    R
    Copyright Holder The Authors
    Copyright Year 2019
    Copyright type Creative commons
    DOI 10.1016/j.mex.2019.04.017
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 939 Abstract Views  -  Detailed Statistics
    Created: Fri, 10 May 2019, 16:10:18 JST by Claudia Matthias on behalf of UNU FLORES