[esip-semantictech] Geospatial Semantics and Ontology sessions at American Association of Geographers (AAG) annual meeting, New Orleans, April 10-14

[Mcgibbney, Lewis J (398M)]

Hi Folks,
With regards to Dalia’s message below I would like to submit the following abstract to the session at AAG. Additionally, I would like to acknowledge and credit ALL folks who have been involved with community involvement/development of SWEET since it came to ESIP.
So far, the names I have are as follows… if I’ve missed you then please let me know either on list or else directly. I think for the time being, we can do without affiliations. If these are required then I will reach out to folks.
Feedback on the abstract is appreciated.
Lewis

Brandon Whitehead, John Graybeal, Carlos Rueda, Pier Buttigieg, Simon Cox, Ruth Duerr, Beth Huffer, Thomas Narrock, Chris Mungall, Douglas Fils, Stian Soiland-Reyes

Title: SWEET Ontology Suite v3.0.0: Development, Alignments and Use Cases

Abstract: Version 3.0.0 of the Semantic Web for Earth and Environmental Terminology (SWEET) Ontology Suite was recently released under new community management and governance by ESIP’s Semantic Technologies Committee. SWEET is a highly modular ontology suite with ~6000 concepts in ~200 separate ontologies covering Earth system science. SWEET consists of nine top-level concepts/ontologies and is considered as a middle-level ontology; most users add a domain-specific ontology which build off of these top-level SWEET components. SWEET ontologies are written in W3C Turtle; the Terse RDF Triple Language and are publicly available under the Apache License v2.0.
This presentation will provide a three-pronged update on (i) advances in SWEET since it’s transition from NASA JPL over to the open source, community driven management and governance structure now overseen by the ESIP Semantic Technologies Committee, and (ii) ongoing alignment activities with existing semantic technology resources such as the hugely popular OBO Foundry collection, W3C SOSA/SSN, W3C PROV-O, CHEBI, etc., and (iii) uses of SWEET across old, new and unexpected domains including building and engineering, provenance of science data product generation, search and information retrieval query expansion, article and text tagging and a whole host more.
This session will be aimed at both new comers and experienced Geospatial and Spatiotemporal Ontology and Semantics enthusiasts. Attendees will leave with a significantly improved understanding of what SWEET currently is, what it will become and how one can use it for a wide variety of scenarios.


    Message: 1
    Date: Wed, 18 Oct 2017 11:28:44 -0500
    From: "Varanka, Dalia" <dvaranka at usgs.gov>
    To: <esip-semanticweb at lists.esipfed.org>
    Subject: [esip-semantictech] Geospatial Semantics and Ontology
    	sessions at American Association of Geographers (AAG) annual meeting,
    	New Orleans, April 10-14
    Message-ID:
    	<CAH4dG4K6vaiqCKgLYY=QUVnvj-0SGhUggD7tNbuCJ4HvCxPDJA at mail.gmail.com>
    Content-Type: text/plain; charset="utf-8"
    
    *Session Title: *Artificial Intelligence and Deep Learning Symposium:
    ?Geospatial and
    
    Spatiotemporal Ontology and Semantics
    
    
    
    *Sponsoring AAG Specialty Groups: *Cyberinfrastructure (CI) Specialty
    Group, Geographic Information Science and Systems
    
    
    
    *Session Organizer*:
    
    Alex Sorokine (*Oak Ridge National Laboratory*) (SorokinA at ornl.gov)
    corresponding
    
    
    
    *Session Co-organizers*:
    
    Chen-Chieh Feng (*National University of Singapore*) (geofcc at nus.edu.sg)
    
    Dalia Varanka (*USGS*) (dvaranka at usgs.gov)
    
    Jeon-Young Kang (*SUNY at Buffalo)* (jeonyoun at buffalo.edu)
    
    
    
    *Abstract Deadline: *Same as AAG Annual Meeting abstract submission deadline
    
    
    
    *AAG Session Id:* 406
    
    
    
    *Call for Papers:*
    
    Capturing and representing geospatial and spatiotemporal dimensions of
    geographic knowledge is a great challenge from both theoretical and applied
    perspectives. Latest advances in knowledge representation and ontology
    engineering have spurred a number of traditional and novel geoprocessing
    applications such as intelligent search and geographic information
    retrieval, image understanding, integration of disparate and
    multi-provenance spatial and spatiotemporal data, knowledge discovery from
    Big Data, visual representation of geographic categories, information
    extraction from unstructured sources, and many others.
    
    Encouraged by well-received special sessions on ontology and semantics for
    several years now, we are again soliciting presentations on both
    theoretical and applied aspects of geospatial and spatiotemporal ontologies
    for a series of special sessions at the 2018 Association of
    American Geographers Annual Meeting. We invite presentations focusing on
    one or more of the following (or other related) areas of ontology research:
    
    
    
    ?     Spatial and spatiotemporal knowledge modeling, analysis,
    formalization, and validation (thesauri, vocabularies and ontologies)
    
    ?     Semantic interoperability across domains, cultures, ethnicities,
    languages and time, spatiotemporal models and reasoning
    
    ?     Semantic annotation methods, provenance, and standards for spatial
    knowledge representation and processing (e.g. XML, RDF, RDFS, OWL)
    
    ?     Semantic extensions to spatial data infrastructures for the IoT,
    sensor networks, VGI, and other application areas
    
    To present a paper or to participate in the session as a discussant, submit
    your abstract through AAG web site and email your PIN to Alex Sorokine (
    SorokinA at ornl.gov). Please follow standard AAG abstract submission
    procedure and guidelines. If you have any questions please forward them to
    one of the organizers.
    
    
    -- 
    Dalia Varanka
    U.S. Geological Survey
    1400 Independence Road
    Rolla, MO 65401 USA
    
    Tel. 573.308.3897
    Email: dvaranka at usgs.gov
    ORCID: 0000-0003-2857-9600