The document discusses the importance of semantics-enhanced interoperability in geoscience, highlighting the challenges and potential benefits of utilizing semantic technologies for data discovery and integration. It emphasizes the need for lightweight semantic strategies to support both large organizations and individual scientists, aimed at improving data accessibility and reducing redundancy. Additionally, it outlines the architectural framework and applications of advanced query languages like SPARQL for effective data retrieval and machine perception in geospatial contexts.

1. Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
Krishnaprasad Thirunarayan and Amit Sheth
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, OH-45435
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2. Outline
• Semantics-empowered Cyberinfrastructure for
Geoscience Applications
–
Approaches, Benefits, and Challenges
(reflecting cost/convenience/pay-off trade-offs)
• Expressive search and integration using
Geospatial information
–
–
SPARQL enhancements
Practical applications using semantic technologies,
sensor data streams, and spatial information
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3. Semantics-empowered
Cyberinfrastructure for Geoscience
applications
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4. Domain Goals and Challenges
Data-driven understanding of the evolution of oceans,
atmosphere, and solid earth over time through physical,
chemical and biological processes.
• Cultural challenges
– Proper protection, control, and credit for sharing data
• Technological challenges
– Computational tools and repositories conducive to easy
exchange, curation, and attribution of data
Data sharing can promote re-analysis/re-interpretation of
extant data, reducing “redundant” data collections.
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5. Category of
Geoscience
Data
Characteristics
Strategy for Reuse
CI Strategy
Short tail
science
data created
by large
organization
s
a n d
projects
Few, large (TB+),
structured, spatially
rich (e.g., remote
sensing), largely
homogeneous,
highly visible,
curated
Planned integration
strategies, could use formal
ontologies / domain models
and vocabularies,
visualization tools and APIs
Data centers / grids
generally using
relational databases
and files, maintained
by people with
significant IT skills
Long tail
science
data created
by individual
scientists
and small
groups
Many, small (GB+),
heterogeneous,
invisible (except via
publications),
poorly curated
Multi-domain and broad
vocabularies (including
community established
ones), create semantic
metadata (annotations) and
optionally publish, search
and download legacy data,
or use an open data
initiative
Web-based easy to
learn and use semantic
tools for annotation,
publication, search and
download that can be
used by individual
scientists without
significant IT skills
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6. Our Thesis
Associating machine-processable semantics
with the long tail of science data and
documents can help overcome challenges
associated with data discovery, integration
and interoperability caused by data
heterogeneity.
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7. What?: Nature of Data
• Structured Data (e.g., relational)
• Semi-structured, Heterogeneous Documents
(e.g., Geoscience publications and technical
specs, which usually include text, numerics, maps
and images)
• Tabular data (e.g., ad hoc spreadsheets and
complex tables incorporating “irregular” entries)
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8. What?: Granularity of Semantics and Associated Applications
• Lightweight semantics: File-level annotation to
enable discovery and sharing of long tail of
science data
• Richer semantics: Document-level annotation and
extraction for semantic search and summarization
• Fine-grained semantics: Data integration,
interoperability and reasoning in Linked Open Data
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9. Why?: Benefits of Lightweight Semantics
• Ease of use by domain experts
– Faster and wider adoption, promoting evolution
• Low upfront cost to support
• Shallow semantics has wider applicability to a
range of documents/data and appeal to a broader
community of geoscientists
• Bottom-line: “Learn to Walk before we Run”
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10. How?: Ingredients for Semantics-based Cyber Infrastructure
• Use of community-ratified controlled vocabularies
and lightweight ontologies (upper-level,
hierarchies)
• Ease self-publishing and discovery
• Data citation index to credit for data sharing
• S e m i - a u t o m a t i c a n n o t a t i o n o f d a t a a n d
documents : Manual + Automatic
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11. Example: Lightweight Semantic Registration of Data
Title of data
Selected from five tier vocabulary
provided Keywords
Type of data
maps, excel files, images, text
Data format
structured or unstructured
Description of data
brief unstructured description of content
Contact information of provider(s)
name of provider(s), email for verification,
lineage
Spatial extent of data and
reference system
location
Temporal extent of data
date range in time or age range if not recent
Date and type of Related
Publication(s)
Journal, Thesis, Agency report, not published
Host site for publication
Journal, Library, Personal computer
Access restrictions
copyright regulations
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12. System Architecture and Components
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13. Deeper Issues: Semantic Formalization
of Tabular Data
Problems and A Practical Approach
(“When rubber meets the road”)
skip
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14. Nature of tables
• Compact structures for sharing information
– Minimize duplication
• Types of Tables
– Regular : Dense Grid with explicit schema
information in terms of column and row
headings => Tractable
– Irregular: Sparse Grid with implicit schema and
ad hoc placement of heading => Hard
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15. Challenges Associated with Typical Spreadsheet/Table
• Meant for human consumption
• Irregular :
– Not simple rectangular grid
• Heterogeneous
– All rows not interpreted similarly
• Complex
– Meaning of each row and each column context
dependent
• Footnotes modify meaning of entries (esp. in materials
and process specifications)
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16. Practical Semi-Automatic Content Extraction
• DESIGN: Develop regular data structures that
can be used to formalize tabular information.
– Provide a natural expression of data
– Provide semantics to data, thereby removing potential
ambiguities
– Enable automatic translation
• USE: Manual population of regular tables and
automatic translation into LOD
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17. Expressive search and integration
using Geospatial information
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18. Outline
• Query Language Support for SpatioTemporal Context: SPARQL-ST
(=> GeoSPARQL)
• Practical Applications that use SpatioTemporal information for joining Sensor
Data to enable Machine Perception
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19. Overview : SPARQL-ST
• SPARQL
– W3C recommended query language for RDF data (as of
Jan. 15, 2008)
– Graph pattern-based queries (subgraph match)
• SPARQL-ST
– Spatial variables
– Temporal variables
– Spatial filter expressions
– Temporal filter expressions
skipToEg
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20. Find all politicians that represent areas within 100 miles of the
district represented by Nancy Pelosi.
SELECT ?n
WHERE {
?p foaf:name ?n .
?p usgov:hasRole ?r .
?r usgov:forOffice ?o .
?o usgov:represents ?q .
?q stt:located_at %g .
?a foaf:name “Nancy Pelosi” .
?a usgov:hasRole ?b .
?b usgov:forOffice ?c .
?c usgov:represents ?d .
?d stt:located_at %h .
SPATIAL FILTER (distance(%g, %h) <= 100 miles)
}
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21. Find all politicians representing congressional districts within a
given geographical area at any time in October 2013
SELECT ?p
WHERE {
?p usgov:hasRole ?r #t1 .
?r usgov:forOffice ?o #t2 .
?o usgov:represents ?c #t3 .
?c stt:located_at %g #t4 .
SPATIAL FILTER (inside(%g, GEOM(POLYGON ((
-75.14 40.88, -70.77 40.88, -70.77 42.35,
-75.14 42.35, -75.14 40.88))) ))
TEMPORAL FILTER (
anyinteract(intersect (#t1, #t2, #t3, #t4),
interval(10:01:2013, 10:31:2013, MM:DD:YYYY)))
}
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22. Summary of SPARQL-ST
• Relationship-centric nature of the RDF data model
extended for querying STT data
• Querying
– Supports spatial and temporal relationships in graph
pattern queries
– Integrates well with current standards
• Implementation
– Good scalability on large synthetic/real-world data
– Only system for spatial and temporal RDF
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23. OGC GeoSPARQL
Slides by Matt Perry of Oracle
(also: Kno.e.sis Alumnus)
4th Annual Spatial Ontology Community of Practice Workshop (SOCoP)
USGS, 12201 Sunrise Valley Drive , Reston VA
December 2, 2011
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24. What Does GeoSPARQL Give Us?
• Vocabulary for Query Patterns
– Classes
• Spatial Object, Feature, Geometry
– Properties
• Topological relations
• Links between features and geometries
– Datatypes for geometry literals
• ogc:WKTLiteral, ogc:GMLLiteral
SkipToEg
• Query Functions
– Topological relations, distance, buffer, intersection, …
• Entailment Components
– RIF rules to expand feature-feature query into geometry query
– Gives a common interface for qualitative and quantitative systems
OGC
®
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25. Example Query
Find the three closest Mexican restaurants
PREFIX
PREFIX
PREFIX
PREFIX
: <http://my.com/appSchema#>
ogc: <http://www.opengis.net/geosparql#>
ogcf: <http://www.opengis.net/geosparql/functions#>
epsg: <http://www.opengis.net/def/crs/EPSG/0/>
SELECT ?restaurant
WHERE { ?restaurant rdf:type
:Restaurant .
?restaurant :cuisine
:Mexican .
?restaurant :pointGeometry ?rGeo .
?rGeo
ogc:asWKT
?rWKT }
ORDER BY ASC(ogcf:distance(“POINT(…)”^^ogc:WKTLiteral,
?rWKT, ogc:KM))
LIMIT 3
OGC
®
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26. Practical Applications
that use Spatio-Temporal information
for joining Sensor Data to enable
Machine Perception
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27. Applications using spatial and/or temporal information
• Location-aware applications
– Four Squares
– Open Street Maps
• Spatio-temporal-thematic (STT) contextenhanced data integration, querying, and
inferencing (machine perception)
– Semantic Sensor Web (+ SemSOS)
• Abstract weather sensor data streams to
weather features
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28. (cont’d)
• Applications supporting expressive queries
– Human comprehensible vs machine processable
•
Geonames (LOD) ↔ Lat-long, GPS data
–
What is the current temperature or traffic delay at Dayton
International Airport?
– Knowledge-based query expansion/reasoning
• Bridging vocabulary mismatches in the queries and the data,
e.g., using semantic relationships between regions and
landmark locations
– Find schools in OH
– Find schools near Wright State University
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29. Semantic Sensor Observation Service Architecture :
Making the Data Smart
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30. SSW demo with Mesowest data (Machine Perception)
http://archive.knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm
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31. Implementation of Perception Cycle
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32. Trusted Perception Cycle Demo
http://www.youtube.com/watch?v=lTxzghCjGgU
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33. Sensor Discovery on Linked Data Demo
http://archive.knoesis.org/projects/sensorweb/demos/sensor_discovery_on_lod/sample.htm
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34. Kno.e.sis
thank you, and please visit us at
http://knoesis.org/
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
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