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An Open-World Iterative Methodology for the Development and Evaluation of
Semantically-Enabled ApplicationsIAAI - Session 23F
Robert S. Engelmore Award* LectureStanding in for Deborah L. McGuinness*
July 17, 2013
Peter Fox (RPI) [email protected], @taswegianTetherless World Constellation
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Outline
• Origins of this effort and the working premise • Semantics in 2004 • Ontologies and the software and production!• Semantics between 2004 and 2009• The design and development methodology• The expressivity and implementability balance
and one more …• 2009-2013 … • And, a bit about what we are up to and where it
is going…
2Tetherless World Constellation
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Origins (1) …
• In 2000-2001 the need for capturing and preserving knowledge in Earth sciences data settings became very clear but the barriers were high
• In 2004 we started a virtual observatory project based on semantic technologies
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Content: Coupling Energetics and Dynamics of Atmospheric Regions
Community data archive for observations and models of Earth's upper atmosphere and geophysical indices and parameters needed to interpret them. Includes browsing capabilities by periods, instruments, models, …
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Content: Mauna Loa Solar Observatory
Near real-time data from Hawaii from a variety of solar instruments. Source for space weather, solar variability, and basic solar physics
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Working premise
Scientists – actually ANYONE - should be able to access a global, distributed knowledge base of scientific data and information that:• appears to be integrated• appears to be locally available• is accessible in a suitable vocabularyThen BAM - Data intensive – volume, complexity,
mode, scale, heterogeneity, … in an OPEN WORLD
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Origins (2) …
• Use case driven – in solar and solar-terrestrial physics with an emphasis on instrument-based measurements and real data pipelines; we needed implementations
• We knew we also needed integration and provenance (but that came later)
• We aimed to push semantics into our systems to build new ‘prototypes’ but we ‘failed’ ;-)
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Early days of VOs
… … … …
VO1
VO2 VO3
DB2 DB3DBn
DB1
?
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The Astronomy approach; data-types as a service
… … … …
VO App1VO App2 VO App3
DB2 DB3DBn
DB1
VOTableSimple
Image Access
ProtocolSimple Spectrum
Access ProtocolSimple
Time Access
Protocol
VO layer
Lightweight semanticsLimited meaning, hard codedLimited extensibility
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In 2004
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Design and Development
• Only develop ontologies that were required to answer specific use cases
• Use whatever ontologies were available**
• Would rules be needed?• We ignored query*
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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.
Provide semantically-enabled, smart data query services via the 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.
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Use Case example
• Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the non-vertical mode during January 2000 as a time series.
• Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the non-vertical mode during January 2000 as a time series.
• Objects: – Neutral temperature is a (temperature is a) parameter– Millstone Hill is a (ground-based observatory is a) observatory– Fabry-Perot is a interferometer is a optical instrument is a instrument– Non-vertical mode is a instrument operating mode– January 2000 is a date-time range– Time is a independent variable/ coordinate– Time series is a data plot is a data product
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Knowledge representation
• Statements as triples: {subject-predicate-object}interferometer is-a optical instrumentFabry-Perot is-a interferometerOptical instrument has focal lengthOptical instrument is-a instrumentInstrument has instrument operating modeInstrument has measured parameterInstrument operating mode has measured parameterNeutralTemperature is-a temperatureTemperature is-a parameter
• A query*: select all optical instruments which have operating mode vertical
• An inference: infer operating modes for a Fabry-Perot Interferometer which measures neutral temperature
• ISWC paper award 2006, IAAI best paper (2007), Fox et al. 2009 in Computers and Geosciences.
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Fox - APAC 2007, Driving e-research: Grids and Semantics
15… … … …
VO Portal
Web Serv.
VO API
DB2 DB3DBn
DB1
Semantic mediation layer - VSTO - low level
Semantic mediation layer – mid-upper-level
Education, clearinghouses, other services, disciplines, etc.
Metadata, schema, data
Query, access and use of data
Semantic query, hypothesis and inference
Semantic interoperability
Added value
Added value
Added value
Added value
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.
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Fox - APAC 2007, Driving e-research: Grids and Semantics
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Partial exposure of Instrument class hierarchy - users seem to LIKE THIS
Semantic filtering by domain or instrument hierarchy
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Inferred plot type and return required axes data
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Semantic Web Services
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Fox - APAC 2007, Driving e-research: Grids and Semantics
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Semantic Web Services
OWL document returned using VSTO ontology - can be used both syntactically or semantically
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http://escience.rpi.edu/schemas/vsto_all.owl
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Developing ontologies
• Use cases and small team
• Identify classes and minimal properties (leverage controlled vocab.)
• Review, vet, publish • Only code them (in RDF or OWL) when needed
(CMAP, …)• Ontologies: small and modular
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Implications and OWL 1.0
• Lack of numeric support meant that the the rules and procedural logic were implemented in java, i.e. in the code
• On several occasions the tools (not to be named) pushed us into OWL-Full, introduced inconsistencies, etc.
• Finally, they stabilized, and in 2005 (and again in 2006 and twice in 2007) we had stable releases
Tetherless World Constellation 23
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Expressivity VSTO 1.0
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Expressivity VSTO dev. version
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Yikes
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Ontologies and the software
• Protégé 2.x and then 3.x built from our ontology on the web
• Java class generation
• Eclipse as a development environment• Leveraged a portal code base (from the
Earth System Grid project)
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Implementation choices
• Our big challenge was time – in use cases and in the representation– Depending on the level of granularity there were
> 200,000 day-time records, and > 70,000,000 sub-day time intervals – no triple store could handle this**
• Reasoning in finite time does not mean 3-4 secs!
• We descoped our effort to delay use cases such as: find all neutral temperature data around the summer solstice for the last decade
• We chose a minimal time encoding in the ontology and delegated that to a relational DBTetherless World Constellation 28
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Fox - APAC 2007, Driving e-research: Grids and Semantics
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VSTO - semantics and ontologies in an operational environment: www.vsto.org
Web Service
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Semantic Web Benefits (1)
Unified/ abstracted query workflow: Parameters, Instruments, Date-Time
Decreased input requirements for query: in one case reducing the number of selections from eight to three
Generates only syntactically correct queries: which was not always insurable in previous implementations without semantics
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Semantic Web Benefits (2)
Semantic query support: only expose coherent query (portal and services)
Semantic integration: mediated understanding of coordinate systems, relationships, data
synthesis, transformations. returns independent variables and related parameters
A broader range of potential users (PhD scientists, students, professional research associates and those from outside the fields)
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Evaluation• Formative and Summative
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Iteration
• We needed the ability to evolve the ontology and not break the framework
• As we broadened re-use of these ontologies and creation of new ones
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Maintenance
• Support for collaborative feedback, evolution
• Change management• Support for ‘comments’ and
‘annotations’, i.e. self-documentation• Package management: creation,
dependency, consistency checking
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Semantics between 2004 and 2009
• Ontologies were needed for data integration and provenance and mediation for data mining
• Protégé 3.x and then 4.0 came out• SWOOP development was interrupted• Cmap added OWL predicate support*• SPARQL became a recommendation• Triple stores exploded in use and capability• Linked Open Data started to take off• Pellet 2.0 came out• We invaded OWLED 2006, 2007, 2009, (2010)
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Semantics approach
• Use cases• Stakeholders• Distributed
authority• Access control• Ontologies• Maintaining
Identity
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Working with knowledge
Expressivity
Maintainability/ Extensibility
Implement-ability
Query
Rule execution
Inference
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Expressivity/ Implementation
Declarative Procedural
Linked open dataURI/http/RDF *
Ontology encoded
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Semantics between 2009 and 2013
• Semantic data frameworks (SeSF)• Substantial knowledge provenance work• Data quality, uncertainty and bias
representations and applications – Multi-sensor advisor**
• Applications:– Sea Ice, Carbon Observatory, Integrated
Ecosystem Assessments, globalchange.gov, ocean.data.gov, energy.data.gov ….
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Anomaly Example: South Pacific Anomaly
Anomaly
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MODIS Level 3 dataday definition leads to artifact in correlation
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…is caused by an Overpass Time Difference
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RuleSet Development
[DiffNEQCT:(?s rdf:type gio:RequestedService),(?s gio:input ?a),(?a rdf:type gio:DataSelection),(?s gio:input ?b),(?b rdf:type gio:DataSelection),(?a gio:sourceDataset ?a.ds),(?b gio:sourceDataset ?b.ds),(?a.ds gio:fromDeployment ?a.dply),(?b.ds gio:fromDeployment ?b.dply),(?a.dply rdf:type gio:SunSynchronousOrbitalDeployment),(?b.dply rdf:type gio:SunSynchronousOrbitalDeployment),(?a.dply gio:hasNominalEquatorialCrossingTime ?a.neqct),(?b.dply gio:hasNominalEquatorialCrossingTime ?b.neqct),notEqual(?a.neqct, ?b.neqct)->(?s gio:issueAdvisory giodata:DifferentNEQCTAdvisory)]
Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.
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Advisor Knowledge Base
44Advisor Rules test for potential anomalies, create
association between service metadata and anomaly metadata in Advisor KB
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Semantic Advisor Architecture
RPI
Multi-sensor Data Synergy Advisor (NASA), Leptoukh, Lynnes, Zednik, et al.
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Advisory Report
• Tabular representation of the semantic equivalence of comparable data source and processing properties
• Advise of and describe potential data anomalies/bias
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Advisory Report (Dimension Comparison Detail)
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Going forward…• No surprise – lots of application
– Water quality, environmental conditions– Global change information system; traceable
accounts– Deep Carbon Observatory– And … maturing ontologies
• Discrete and Continuous Additive effects (in Event Calculus) – driven by the desire to explore physical processes in Earth’s atmosphere when a volcano erupts
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RPI Tetherless World Constellation tw.rpi.edu
Themes
Lots of RDFWeb Infrastructure
Scaling and DistributedQuery and Reasoning
AI, Rule reasoningVisualizing SW ‘data’Social SW, SMWiki
Policy LangOntologies/Tools, …
Future Web•Web Science
•Policy•Social
Xinformatics•Data Science•Semantic eScience•Data Frameworks
Semantic Foundations•Knowledge Provenance
•Inference•Trust
Hendler
Fox
McGuinness+ ~ 40 = Post-doc, Staff, Grad, UGrad
• Government Data• Health care/Life Sciences• Environmental Informatics
Luciano, Erickson
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Respect vocabularies!
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Discovering new data
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Core and Framework Semantics - Multi-tiered interoperability
used by
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High-level architecture
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Summary (thanks to AI)• In 2004 we set out to build a prototype and ended
up with a production semantic data framework– Languages and tools served us well (standards)
• Even with modest expressivity we challenged the tools of the time and made many compromises
• All along the way, we evaluated our ontology developments and implementations to gauge the benefits of semantics
• Maintainability, esp. modularization drove new expressivity needs
• Xinformatics and a repeatable methodology is the key (information models) - we continue to need to bridge computer science and application communities (“It’s the language stupid”, i.e. semantics)
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Ontology Spectrum
Catalog/ID
SelectedLogical
Constraints(disjointness,
inverse, …)
Terms/glossary
Thesauri“narrower
term”relation
Formalis-a
Frames(properties)
Informalis-a
Formalinstance Value
Restrs.
GeneralLogical
constraints
Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness.Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html
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Semantic Web Layers
http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/