Ontologies and Semantic Applications in Earth Sciences

1
Ontologies and Semantic Applications in Earth
Sciences

• Peter Fox (TWC/RPI formerly HAO/NCAR)
• Thanks to many.
• Projects funded by NSF/OCI and NASA/ACCESS/ESTO

2
Background

• Scientists should be able to access a global,
  distributed knowledge base of scientific data
  that
• appears to be integrated
• appears to be locally available
• But data is obtained by multiple means (models
  and instruments), using various protocols, in
  differing vocabularies, using (sometimes
  unstated) assumptions, with inconsistent (or
  non-existent) meta-data. It may be inconsistent,
  incomplete, evolving, and distributed
• And there exist(ed) significant levels of
  semantic heterogeneity, large-scale data, complex
  data types, legacy systems, inflexible and
  unsustainable implementation technology

3
Data-types as service
Limited interoperability

• VOTable
• Simple Image Access Protocol
• Simple Spectrum Access Protocol
• Simple Time Access Protocol

VO App2
VO App3
VO App1
Open Geospatial Consortium Web Feature,
Coverage, Mapping Service Sensor Web
Enablement Sensor Observation, Planning,
Analysis Service use the same approach
VO layer
DBn
DB2
DB3

DB1
4
VO API
Web Serv.
VO Portal
Knowledge as service!
Query, access and use of data

• Mediation Layer
• Ontology - capturing concepts of Parameters,
  Instruments, Date/Time, Data Product (and
  associated classes, properties) and Service
  Classes
• Maps queries to underlying data
• Generates access requests for metadata, data
• Allows queries, reasoning, analysis, new
  hypothesis generation, testing, explanation, etc.

Semantic mediation layer - VSTO - low level
Standard, or not, vocabularies and schema
Metadata, schema, data
DBn
DB2
DB3

DB1
5
Semantic Web Methodology and Technology
Development Process

• Establish and improve a well-defined methodology
  vision for Semantic Technology based application
  development
• Leverage any existing vocabularies

Adopt Technology Approach
Leverage Technology Infrastructure
Science/Expert Review Iteration
Rapid Prototype
Open World Evolve, Iterate, Redesign, Redeploy
Use Tools
Analysis
Use Case
Develop model/ ontology
Small Team, mixed skills
6
E.g. Science and technical use cases

• Find data which represents the state of the
  neutral atmosphere anywhere above 100km and
  toward the arctic circle (above 45N) at any time
  of high geomagnetic activity.
• Extract information from the use-case - encode
  knowledge
• Translate this into a complete query for data -
  inference and integration of data from
  instruments, indices and models
• Provide semantically-enabled, smart data query
  services via a SOAP web for the Virtual
  Ionosphere-Thermosphere-Mesosphere Observatory
  that retrieve data, filtered by constraints on
  Instrument, Date-Time, and Parameter in any order
  and with constraints included in any combination.

7
VSTO - semantics and ontologies in an operational
environment vsto.hao.ucar.edu, www.vsto.org
8
Semantic Web Services
9
Semantic Web Services
OWL document returned using VSTO ontology - can
be used both syntactically or semantically
10
Semantic Web Benefits

• Unified/ abstracted query workflow Parameters,
  Instruments, Date-Time across widely different
  disciplines
• Decreased input requirements for query in one
  case reducing the number of selections from eight
  to three
• Semantic query support by using background
  ontologies and a reasoner, our application has
  the opportunity to only expose coherent queries
  (portal and services)
• Semantic integration in the past users had to
  remember (and maintain codes) to account for
  numerous different ways to combine and plot the
  data whereas now semantic mediation provides the
  level of sensible data integration required, and
  exposed as smart web services
• understanding of coordinate systems,
  relationships, data synthesis, transformations,
  etc.
• returns independent variables and related
  parameters
• A broader range of potential users (PhD
  scientists, students, professional research
  associates and those from outside the fields)
• VSTO http//vsto.hao.ucar.edu,
  http//www.vsto.org

11
http//dataportal.ucar.edu/schemas/vsto_all.owl
(1.0, 2.0 coming)
12
Ingest/pipelines problem definition

• Data is coming in faster, in greater volumes and
  outstripping our ability to perform adequate
  quality control
• Data is being used in new ways and we frequently
  do not have sufficient information on what
  happened to the data along the processing stages
  to determine if it is suitable for a use we did
  not envision
• We often fail to capture, represent and propagate
  manually generated information that need to go
  with the data flows
• Each time we develop a new instrument, we develop
  a new data ingest procedure and collect different
  metadata and organize it differently. It is then
  hard to use with previous projects
• The task of event determination and feature
  classification is onerous and we don't do it
  until after we get the data

13
(No Transcript)
14
Use cases

• Who (person or program) added the comments to the
  science data file for the best vignetted,
  rectangular polarization brightness image from
  January, 26, 2005 184909UT taken by the ACOS
  Mark IV polarimeter?
• What was the cloud cover and atmospheric seeing
  conditions during the local morning of January
  26, 2005 at MLSO?
• Find all good images on March 21, 2008.
• Why are the quick look images from March 21,
  2008, 1900UT missing?
• Why does this image look bad?

15
(No Transcript)
16
(No Transcript)
17
Provenance

• Origin or source from which something comes,
  intention for use, who/what generated for, manner
  of manufacture, history of subsequent owners,
  sense of place and time of manufacture,
  production or discovery, documented in detail
  sufficient to allow reproducibility
• Knowledge provenance enrich with ontologies and
  ontology-aware tools

18
(No Transcript)
19
(No Transcript)
20
Quick look browse
21
(No Transcript)
22
Visual browse
23
(No Transcript)
24
(No Transcript)
25
Search and structured query
Structured Query
Search
26
Search
27
Data Integration Use Case

• Determine the statistical signatures of both
  volcanic and solar forcings on the height of the
  tropopause

28
Detection and attribution relations
29
(No Transcript)
30
SWEET 2.0
31
Semantic framework indicating how volcano and
atmospheric parameters and databases can
immediately be plugged in to the semantic data
framework to enable data integration.
32
Faceted Search
33
Summary

• Level of ontology encoding relates to use, e.g.
• VSTO
• SPCDIS
• SESDI Data integration needs higher level of
  curation of ontologies and mapping to data
• Languages and tools
• Rapid prototyping (PHP, Semantic MediaWiki)
• Clean and simple (RDFS, Perl and SPARQL)
• Complex and rich (Java, Protégé, Jena, Pellet,
  ELMO, Maven, Eclipse)

34
Modified GEON Solution Framework
Data Discovery
Data Integration
Level 1 Data Registration at the Discovery
Level, e.g. Volcano location and activity
Level 2 Data Registration at the Inventory
Level, e.g. list of datasets by, types, times,
products
Level 3 Data Registration at the Item
Detail Level, e.g. access to individual quantities
Earth Sciences Virtual Database A Data Warehouse
where Schema heterogeneity problem is Solved
schema based integration
Ontology based Data Integration
A.K.Sinha, Virginia Tech, 2006
35
Spare material
36
Example 1 Registration of Volcanic Data

• Location Codes
• U - Above the 180 turn at Holei Pali (upper
  Chain of Craters Road)
• L - Below Holei Pali (lower Chain of Craters
  Road)
• UL - Individual traverses were made both above
  and below the 180 turn at Holei Pali
• H - Highway 11

SO2 Emission from Kilauea east rift zone -
vehicle-based (Source HVO)
Abreviations t/dmetric tonne (1000 kg)/day,
SDstandard deviation, WSwind speed, WDwind
direction east of true north, Nnumber of
traverses
37
Registering Volcanic Data (2)

• No explicit lat/long data
• Volcano identified by name
• Volcano ontology framework will link name to
  location

38
Registering Atmospheric Data (2)
39
Building blocks

• Data formats and metadata IAU standard FITS,
  with SoHO keyword convention, JPeG, GIF
• Ontologies OWL-DL and RDF
• The proof markup language (PML) provides an
  interlingua for capturing the information agents
  need to understand results and to justify why
  they should believe the results.
• The Inference Web toolkit provides a suite of
  tools for manipulating, presenting, summarizing,
  analyzing, and searching PML in efforts to
  provide a set of tools that will let end users
  understand information and its derivation,
  thereby facilitating trust in and reuse of
  information.
• Capturing semantics of data quality, event, and
  feature detection within a suitable community
  ontology packages (SWEET, VSTO)