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Artificial intelligence for ocean science data integration: current state, gaps, and way forward
Sagi, T.; Lehahn, Y.; Bar, K. (2020). Artificial intelligence for ocean science data integration: current state, gaps, and way forward. Elem. Sci. Anth. 8(1): 21. https://dx.doi.org/10.1525/elementa.418
In: Elementa Science of the Anthropocene. BioOne: Washington. ISSN 2325-1026, more
Peer reviewed article  

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Keywords
    Aquatic sciences > Marine sciences > Earth sciences > Oceanography
    Artificial intelligence
    Data integration
Author keywords
    Ontologies

Authors  Top 
  • Sagi, T.
  • Lehahn, Y.
  • Bar, K.

Abstract
    Oceanographic research is a multidisciplinary endeavor that involves the acquisition of an increasing amount of in-situ and remotely sensed data. A large and growing number of studies and data repositories are now available on-line. However, manually integrating different datasets is a tedious and grueling process leading to a rising need for automated integration tools. A key challenge in oceanographic data integration is to map between data sources that have no common schema and that were collected, processed, and analyzed using different methodologies. Concurrently, artificial agents are becoming increasingly adept at extracting knowledge from text and using domain ontologies to integrate and align data. Here, we deconstruct the process of ocean science data integration, providing a detailed description of its three phases: discover, merge, and evaluate/correct. In addition, we identify the key missing tools and underutilized information sources currently limiting the automation of the integration process. The efforts to address these limitations should focus on (i) development of artificial intelligence-based tools for assisting ocean scientists in aligning their schema with existing ontologies when organizing their measurements in datasets; (ii) extension and refinement of conceptual coverage of – and conceptual alignment between – existing ontologies, to better fit the diverse and multidisciplinary nature of ocean science; (iii) creation of ocean-science-specific entity resolution benchmarks to accelerate the development of tools utilizing ocean science terminology and nomenclature; (iv) creation of ocean-science-specific schema matching and mapping benchmarks to accelerate the development of matching and mapping tools utilizing semantics encoded in existing vocabularies and ontologies; (v) annotation of datasets, and development of tools and benchmarks for the extraction and categorization of data quality and preprocessing descriptions from scientific text; and (vi) creation of large-scale word embeddings trained upon ocean science literature to accelerate the development of information extraction and matching tools based on artificial intelligence.

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