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Spatial Relation Predicates for National Topographic Ontologies

Spatial Relation Predicates for National Topographic Ontologies. Dalia Varanka and Holly Caro Cognitive and Linguistic Aspects of Geographical Space in the Age of Cyber-infrastructure Las Navas Del Marques, Avila, Spain July 5, 2010. Data models of the 1990s. Coordinate location in space

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Spatial Relation Predicates for National Topographic Ontologies

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  1. Spatial Relation Predicates for National Topographic Ontologies Dalia Varanka and Holly Caro Cognitive and Linguistic Aspects of Geographical Space in the Age of Cyber-infrastructure Las Navas Del Marques, Avila, Spain July 5, 2010

  2. Data models of the 1990s • Coordinate location in space • Geometric distance relations • Overlay operations; union, intersection

  3. Relations Build Context, Identity, and Specificity – Key Innovations of the Semantic Web

  4. Objectives • Identify and apply relation terms that reflect and respond to cognitive and linguistic structures of diverse topographic data users • Express those terms in formal logic • Design reasoning algorithms to enable queries reflecting those relations

  5. Data Conversion

  6. SHP2N3.py – Spatial RDFizer • Simple Python script • Shapefiles -> Notation3 (N3) • N3 is an “RDF serialization” Triples that can be loaded into a triple store • Easier to work with than RDF

  7. Attribute conversion builds mapping capability, but lacks flexibility • :_1 • rdf:type :areas ; • rdfs:label "1"^^xsd:string ; • :oBJECTID "63959"^^xsd:integer ; • :aREA "110282240.41500001000"^^xsd:double ; • :pERIMETER "93513.12615000000"^^xsd:double ; • :bCZONE_ "1121.00000000000"^^xsd:double ; • :bCZONE_ID "2.00000000000"^^xsd:double ; • :zONE_ "XLG"^^xsd:string ; • :fP "0"^^xsd:integer ; • :nLNA "0"^^xsd:integer ;

  8. Some standard spatial relation terms

  9. Geographic Semantics forThe National Map • Topographic Ontology Modules • Terrain, Surface Water, Ecological Regime, Built-up areas, Divisions, Events Ontology ‘Patterns’ for re-use • Properties: • Locator, (coordinates, topology, mereology, region) • Generator, (processes) • Descriptor, (shape, quality)

  10. Open Geospatial Consortium Draft GeoSPARQL Specification • 9-Intersection model provides relation semantics • GeoSPARQL will link RDF/OWL to these spatial relations (J. Herring, X. Lopez, and M. Perry) • USGS will provide a test bed for the GeoSPARQL standard

  11. Spatial Relation Predicates • Bridge: Manmade structure carrying a trail, road, or other transportation system across a body of water or depression (causeway, overpass, trestle). (from Geonames.gov) • = ‘carryingACROSS’

  12. Tools for Spatial Relation Analysis • Initial method involved pulling out the 'verbs' (active or passive) and corresponding spatial relation • Performed an initial assessment by reading the definitions and recording the verb/spatial relation term in Excel, then used Concordance to determine overarching trends within the data

  13. USGS / Partnership standards • USGS Digital Line Graph (DLG) standards • Spatial Data Transfer Standard (SDTS) • Geographic Names Information System (GNIS) of the U.S. Board on Geographic Names • Feature types and their definitions were added from various sources for ontological completion (such as, Wikipedia and Google Dictionary)

  14. Tools for Analysis • Excel • Spreadsheet • Allowed easy sorting of the completed database • Resembled the actual formation of triples

  15. Tools for Analysis • Concordance • Allowed for an overview analysis of textual documents using a target word • Does not lemmatize words, or allow the user to sort words by lemmatization • Word permutations, like plurals or conjugation, were sought out individually for analysis

  16. Tools for Analysis • Native speaker analysis • In the context (STDS) • AIRPORT • A facility, either on land or water… • = ‘located atop’ • CRATER • Circular-shaped depression at the summit of a volcanic cone or on the surface of the land. • = ‘in’ • FAULT • A fracture In the Earth's crust with displacement on one side of the fracture relative to the other. • = ‘located at’ • FISHING_GROUND • A water area in which fishing is frequently carried on. • = ‘participated in’

  17. Problems & Solutions • Many definitions lacked verbs entirely • SEA : The great body of salt water of the oceans. • Some lacked substantial spatial predicate relationship • TREE : A woody perennial plant, having a self-supporting main stem or trunk. • Some items are not defined by location • BACKWATER • An area of calm water unaffected by the current of a stream. (composition, quality) • PARK • A place or area set aside for recreation or preservation of a cultural or natural resource. (purpose) • SEA • The great body of salt water of the oceans. (composition)

  18. Problems & Solutions • As always, the fuzzy logic problem • PENINSULA • A body of land jutting out into and nearly surrounded by water. • not surroundedBY, but nearly_surroundedBY • Many other small discrepancies inherent within natural language

  19. Verb predicates and related prepositions indicating topological and patronymic relations

  20. Verb predicates and related prepositions indicating human agency or natural process relations

  21. Findings • A predominant class of spatial relation terms functioned well as descriptor properties • Another predominant class of spatial relation terms functioned well as generator properties • 9-Intersection model relations are classes and can be applied to feature classes, but apply well to feature representations coincident at locations at the instance level

  22. Summary Regarding the Method • As a small-scale analysis: • Provides an overview of issues to be expected in the development of a formal ontology from text standards to RDF definition • Demonstrates discrepancies between practical use of language and machine representation • Could be used as a starting point for further investigation into interactive components of a semantic web technology (i.e., search capabilities)

  23. User Interface to Develop Non-Standard Term Candidates Controlled vocabulary Vernacular language Develop an interactive interface for non-standard terms Collect terms through prompted input that the public finds relevant and uses. • Standard terms are based on the vocabulary of a relatively small and centralized group of users.

  24. Thank you! For further information, please contact us via email or Internet home page: • dvaranka@usgs.gov • usery@usgs.gov • http://cegis.usgs.gov/ontology.html

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