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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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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Semantics-empowered Cyberinfrastructure for Geoscience

applications

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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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Category of Geoscience

Data

Characteristics Strategy for Reuse CI Strategy

Shor t ta i l s c i e n c e data created b y l a r g e organizations a n d projects

Few, large (TB+), structured, spatially rich (e.g., remote sensing), largely h o m o g e n e o u s , h i g h l y v i s i b l e , curated

P l a n n e d i n t e g r a t i o n strategies, could use formal ontologies / domain models a n d v o c a b u l a r i e s , visualization tools and APIs

Data centers / grids g e n e r a l l y u s i n g relational databases and files, maintained b y p e o p l e w i t h significant IT skills

L o n g t a i l s c i e n c e data created by individual s c i e n t i s t s a n d s m a l l groups

Many, small (GB+), h e t e r o g e n e o u s , invisible (except via p u b l i c a t i o n s ) , poorly curated

Multi-domain and broad vocabularies ( including community establ ished ones), create semantic metadata (annotations) and optionally publish, search and download legacy data, o r use an open da ta initiative

Web-based easy to learn and use semantic tools for annotation, publication, search and download that can be used by individual s c i e n t i s t s w i t h o u t significant IT skills

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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 interoperabi l i ty caused by data heterogeneity.

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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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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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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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How?: Ingredients for Semantics-based Cyber Infrastructure

•  Use of community-ratified controlled vocabularies and l ightweight ontologies (upper- level , hierarchies)

•  Ease self-publishing and discovery

•  Data citation index to credit for data sharing

•  Semi-automat ic annotat ion of data and documents : Manual + Automatic

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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

Example: Lightweight Semantic Registration of Data

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System Architecture and Components

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Problems and A Practical Approach (“When rubber meets the road”)

Deeper Issues: Semantic Formalization of Tabular Data

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skip  

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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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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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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Expressive search and integration using Geospatial information

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Outline

•  Query Language Support for Spatio-Temporal Context: SPARQL-ST (=> GeoSPARQL)

•  Practical Applications that use Spatio-Temporal information for joining Sensor Data to enable Machine Perception

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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

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skipToEg  

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) }

Find all politicians that represent areas within 100 miles of the district represented by Nancy Pelosi.

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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))) }

Find all politicians representing congressional districts within a given geographical area at any time in October 2013

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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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4th Annual Spatial Ontology Community of Practice Workshop (SOCoP) USGS, 12201 Sunrise Valley Drive , Reston VA

December 2, 2011

OGC GeoSPARQL Slides by Matt Perry of Oracle

(also: Kno.e.sis Alumnus)

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OGC ®

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

•  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

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SkipToEg

OGC ®

PREFIX : <http://my.com/appSchema#> PREFIX ogc: <http://www.opengis.net/geosparql#> PREFIX ogcf: <http://www.opengis.net/geosparql/functions#> PREFIX 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

Find the three closest Mexican restaurants

Example Query

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Practical Applications that use Spatio-Temporal information

for joining Sensor Data to enable Machine Perception

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Applications using spatial and/or temporal information

•  Location-aware applications –  Four Squares –  Open Street Maps

•  Spatio-temporal-thematic (STT) context-enhanced data integration, querying, and inferencing (machine perception) –  Semantic Sensor Web (+ SemSOS)

•  Abstract weather sensor data streams to weather features

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•  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

(cont’d)

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Semantic Sensor Observation Service Architecture : Making the Data Smart

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SSW demo with Mesowest data (Machine Perception)

http://archive.knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm

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Implementation of Perception Cycle

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Trusted Perception Cycle Demo

http://www.youtube.com/watch?v=lTxzghCjGgU

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Sensor Discovery on Linked Data Demo

http://archive.knoesis.org/projects/sensorweb/demos/sensor_discovery_on_lod/sample.htm

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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

Kno.e.sis