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Balancing Expressivity and Implementability in OWL Ontologies for
Semantic Data Frameworks: The Journey from 2004 to 2009 and Beyond
Peter FoxTetherless World Constellation
RPIAustralia Ontology Workshop 2009
Tetherless World Constellation 2
Outline
• The origins of this effort• Why a framework and not a system?• Semantics in 2004 • The design and development methods• Ontologies and the software and production!• Semantics between 2004 and 2009• Discussion of the expressivity and
implementability balance and one more …• Since it is almost 2010 … what we are up to
3
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 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…
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Origins
• In 2000-2001 the need for capturing and preserving knowledge in science data became very clear but the barriers were high
• In 2004 we started a virtual observatory project based on semantic technologies
• 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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In 2004
• 2004 – OWL was a W3 recommendation!!• Protégé 2.x and the Protégé-Java-OWL API• SWOOP was a viable editor• Jena and the Jena API were in good shape• Pellet worked• SPARQL was still a twinkle in the RDF working
group’s eye• Semantics were still the realm of computer
scientists – luckily we had one of the best
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Frameworks vs. Systems
• Prior to 2005, we built systems• Rough definitions
– Systems have very well-define entry and exit points. A user tends to know when they are using one. Options for extensions are limited and usually require engineering
– Frameworks have many entry and use points. A user often does not know when they are using one. Extension points are part of the design
• You don’t have to agree, this was our view
7
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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Design and Development
• We made a conscious decision only to develop ontologies that were required to answer specific use cases
• We made a conscious effort to use whatever ontologies were available**
• We were pretty sure that rules would be needed
• We ignored query
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, …
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 Other content used too – CISM – Center for Integrated Space Weather Modeling
11
Virtual Observatories
Make data and tools quickly and easily accessible to a wide audience.
Operationally, virtual observatories need to find the right balance of data/model holdings, portals and client software that researchers can use without effort or interference as if all the materials were available on his/her local computer using the user’s preferred language: i.e. appear to be local and integrated
Likely to provide controlled vocabularies that may be used for interoperation in appropriate domains along with database interfaces for access and storage and “smart” tools for evolution and maintenance.
12
Early days of VxOs
… … … …
VO1
VO2 VO3
DB2 DB3DBn
DB1
?
13
The Astronomy approach; data-types as a service
… … … …
VO App1VO App2
VO App3
DB2 DB3DBn
DB1
VOTable Simple
Image Access Protocol Simple Spectrum
Access Protocol
Simple Time Access
ProtocolVO layer
Limited interoperability
Lightweight semantics
Limited meaning, hard coded
Limited extensibility
Under review
OGC: {WFS, WCS, WMS} and
SWE {SOS, SPS, SAS}
use the same approach
14
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.
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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 instrument
Fabry-Perot is-a interferometer
Optical instrument has focal length
Optical instrument is-a instrument
Instrument has instrument operating mode
Instrument has measured parameter
Instrument operating mode has measured parameter
NeutralTemperature is-a temperature
Temperature 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
17 Fox - APAC 2007, Driving e-research:
Grids and Semantics
… … … …
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.
18 Fox - APAC 2007, Driving e-research:
Grids and Semantics
Partial exposure of Instrument class hierarchy - users seem to LIKE THIS
Semantic filtering by domain or instrument hierarchy
19
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Inferred plot type and return required axes data
21
Semantic Web Services
22 Fox - APAC 2007, Driving e-research:
Grids and Semantics
Semantic Web Services
OWL document returned using VSTO ontology - can be used both syntactically or semantically
23
Semantic Web Benefits• 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• Semantic query support: by using background ontologies and a reasoner, our
application has the opportunity to only expose coherent query (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.– returns independent variables and related parameters
• A broader range of potential users (PhD scientists, students, professional research associates and those from outside the fields)
http://escience.rpi.edu/schemas/vsto_all.owl
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Semantic Web Methodology and Technology Development Process
Use Case
Small Team, mixed skills
Analysis
Adopt Technology Approach
Leverage Technology
InfrastructureRapid
PrototypeOpen World: Evolve, Iterate,
Redesign, Redeploy
Use Tools
Science/Expert Review & Iteration
Develop model/
ontology
Evaluation
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Developing ontologies• Use cases and small team (7-8; 2-3 domain/ data experts,
2 knowledge experts, 1 software engineer, 1 facilitator, 1 scribe)
• Identify classes and minimal properties (leverage controlled vocab.)– Start with narrower terms, generalize when needed or possible– Adopt a suitable conceptual decomposition (e.g. SWEET) – Import modules when concepts are orthogonal– Add service classes and properties where needed
• Review, vet, publish • Only code them (in RDF or OWL) when needed (CMAP, …)• Ontologies: small and modular
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Species validation
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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)
32
33
2
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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**
• 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 DB
• Reasoning in finite time does not mean 3-4 secs!
35 Fox - APAC 2007, Driving e-research:
Grids and Semantics
VSTO - semantics and ontologies in an operational environment: www.vsto.org
Web Service
Tetherless World Constellation 36
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 37
Evaluation
• Highlights:– Less clicks to data– Auto identification and retrieval of independent variables & plotting support– Faster– Support for finding instruments (without specifying the id includes finding data
from instruments that the user did not know to ask for)
• Questions (potentially with 35 responses) – What do you like about the new searching interface? (9)– Are you finding the data you need? (35: Yes=34, No=1)– What is the single biggest difference? (8)– How do you like to search for data? Browse, type a query, visual? (10,
Browse=7, Type=0, Visual=3)– What other concepts are you interested in using for search, e.g. time of high
solar activity, campaign, feature, phenomenon, others? (5, all of these)– Does the interface and services deliver the functionality, speed, flexibility you
require? (30, Yes=30, No=0)– How often do you use the interface in your normal work? (19, Daily=13,
Monthly=4, Longer=2)– Are there places where the interface/ services fail to perform as desired? (5,
Yes=1, No=4)
Tetherless World Constellation 38
Iteration
• We need the ability to evolve the ontology and not break the framework
• As we broaden re-use of these ontologies and creation of new ones– We needed visual tools like CMAP Ontology Editor– We needed the visual tools to work with the editing/ plugin tools –
they do not– We needed to use natural language forms but this ended up being
sparse but that need will increase– Need tools aimed at software engineers and domain scientists: three-
pronged approach and interoperable:• OWL in editors (e.g. Protégé, SWOOP, etc.)• Visual (e.g. CMAP/COE)• Natural Language (e.g. Rabbit, CL, Peng)
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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, and 2009
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Semantic Web Layers
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Other projects – ontologies for faceted search
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For data integration
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Ontology packaging
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Provenance
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Discussion of E versus I
• We had to expand the balance to now include maintainability (/ evolvability)
• E-M-I briefly– E.g. modularization has become essential to facilitate
ontology packaging -> need to take advantage of OWL 2– Separation of class and instances
• Makes visual development possible• Also facilitates SPARQL end-point approaches
• As tools and applications improve we reconsider our past choices– Adding time** back into VSTO and moving to OWL 2
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2010
• Recently funded to take our developments into a configurable SDF, thus we will push ontology languages and tools on new ways:
• OWL 2 – RL in particular– Annotations– Property chaining
• SPARQL (yawn)• RIF – probably not for a while• However, the tools still lag behind – especially for
visual and natural language development
Tetherless World Constellation 48
Modularization
• One of the primary goals of VSTO 2.0 is to modularize the VSTO ontology, e.g., an instrument module does not require any other classes besides the instrument and maybe an instrument operating mode to substantiate what an instrument is.
• The problem with modularization, however, is that although a subset may substantiate a concept, that concept, especially in VSTO, has a number of relations linking it with other concepts within the ontology, for instance the instrument module may measure a number of parameters in the parameter module, or have a time coverage that would be defined in the time module.
• Each observatory that the VSTO integrates data for will import only the modules that are appropriate for the observatory's domain.
• There are also some modules that will always be required, regardless of the domain, like the instrument, parameter, and time modules. Each observatory ontology has its own way of linking these modular concepts, which will be called link properties.
• This presents a problem, as the VSTO portal may not know which link property to use to associate an instrument with a set of parameters or a time coverage, as it becomes the responsibility of the ontology for the respective observatory to define the link properties.
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‘Interfaces’ or ‘Extensions’
• This is where the VSTO interface ontology comes in. It doesn't have to be called the VSTO interface, it could be VSTO link properties, or anything for that matter.
• The purpose of this ontology is to define a few link properties that will be required for navigation to data in the VSTO portal. For instance, the guided workflows as they work now, would require a number of link properties. E.g. the Start by Instrument Workflow, the VSTO interface would require an instrument and time coverage link property to get from step 1 to step 2 in the workflow.
• In the case that an instrument of the CEDAR observatory is selected in step 1, this link property could be created in a rule-based logic as…
– ( Instrument_1 hasInstrumentOperatingMode IOM_1 ^ IOM_1 hasDataset Dataset_1 ^ Dataset_1 hasTimeCoverage TimeInterval_1 ) => Instrument_1 hasTimeCoverage TimeInterval_1
• Of course, this would have to be done for all instrument operating modes and all datasets associated with those operating modes to determine the full time coverage of an instrument.
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OWL 2 considerations• What's good?:
– new syntactic sugar to simplify ontology– ability to compare numerics
• OWL 2 QL Synopsis:– focused on ontology interoperability with database systems where scalable reasoning
and query answering over large numbers of instances is most important task• Why is it a good match?:
– synopsis above, query answering over a large number of time instances will have to be performed
• Why isn't it a good match?:– does not support enumerations, a feature required by some concepts in VSTO– does not support functional properties, a feature required by some properties in VSTO– does not support property inclusions involving property chains, a feature we hope to
utilize to define rules for VSTO– does not support keys, a feature we hope to add when Protege 4.1 released (along with
support for creation of keys)
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OWL 2 considerations
• OWL 2 RL Synopsis:– focused on ontology interoperability with rule extended DBMSs where
scalable reasoning over large datasets is the most important task
• Likely final choice:– supports all OWL features currently required by VSTO, including
enumerations and functional properties– supports property inclusions involving property chains, so potential for
rules can be addressed, namely for reasoning over time intervals– supports keys
Tetherless World Constellation 52
Back to Semantic Data Frameworks
• With the substantial adoption of semantics in science data applications– There is a need for a higher level of application/
tool infrastructure– Others are experiencing the same lessons with
ontology and application development• We are aggregating our efforts into a:
Semantic eScience Framework (SESF)*– Configurable, i.e. ontology loadable and driven
Tetherless World Constellation 53
Inference vs. Query
• The real power of semantic web in science is likely to lay in the ability to balance implementation choices between inference (RDFS and OWL) and query (even SPARQL)
• It is clear to us that the effect upon expressivity and maintainability will be an essential consideration– Recall the OWL-QL – OWL RL findings
• Also depends on how dynamic the KB is…
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I.e. SDF vs LOD
• Linked open data – RDFS and SPARQL– http://linkeddata.org
• Emergent ontology versus, well, an engineered one– Current chaos due to owls:sameas– Dynamic content
• One of the present challenges for us is to accommodate the web of data into emerging needs for federated search and access as SDFs are curated..
• And yes, there is RDFS 2.0 to consider
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Summary
• We set out to build a prototype and ended up with a production semantic data framework– Language and tools served us well
• 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 is driving new expressivity needs
• We continue to need to bridge the computer science and application communities
Tetherless World Constellation 56
Further Information
• http://tw.rpi.edu/portal/SESF• Contacts: