XInformatics; bridging the gap between science and discipline neutral cyberinfrastructure with...

XInformatics; bridging the gap between science and discipline neutral

cyberinfrastructure with semantics: The Journey from 2004 to 2010 and Beyond

Peter FoxTetherless World Constellation, RPI

Marine Biology Lab 2010

Tetherless World Constellation 2

Outline

• The origins of this effort, putting the X in• 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 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…


Origins and a preview

• 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’ ;-)

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

7

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.

8

Early days of VxOs

… … … …

VO1

VO2 VO3

DB2 DB3DBn

DB1

?

9

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

10

Mind the Gap!

• There is/ was still a gap between science and the underlying infrastructure and technology that is available

• Cyberinfrastructure is the new research environment(s) that support advanced data acquisition, data storage, data management, data integration, data mining, data visualization and other computing and information processing services over the Internet.

Informatics - information science includes the science of (data and) information, the practice of information processing, and the engineering of information systems. Informatics studies the structure, behavior, and interactions of natural and artificial systems that store, process and communicate (data and) information. It also develops its own conceptual and theoretical foundations. Since computers, individuals and organizations all process information, informatics has computational, cognitive and social aspects, including study of the social impact of information technologies. Wikipedia.

11

Progression after progression

IT CyberInfrastructure

Cyber Informatics

Core Informatics

Science Informatics

Science, Societal Benefit Areas

Informatics

Example:

•CI = OPeNDAP server running over HTTP/HTTPS, wiki, databases,

•Cyberinformatics = Data (product) and service ontologies, triple stores

•Core informatics = Reasoning engine (Pellet), OWL, and much more

•Science (X) informatics = Use cases, science domain terms/ vocabularies, concepts in an ontology

Requirements


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


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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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


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

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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.

– 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

19 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.


Grids and Semantics

Partial exposure of Instrument class hierarchy - users seem to LIKE THIS

Semantic filtering by domain or instrument hierarchy

22

Inferred plot type and return required axes data

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Semantic Web Services


Grids and Semantics

Semantic Web Services

OWL document returned using VSTO ontology - can be used both syntactically or semantically

25

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

Semantic Web Methodology and Technology Development Process

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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


Species validation


Expressivity VSTO 1.0


Expressivity VSTO dev. version


Yikes


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)


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!


Grids and Semantics

VSTO - semantics and ontologies in an operational environment: www.vsto.org

Web Service

http://vsto.hao.ucar.edu/

http://vsto.hao.ucar.edu/


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


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)


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)


Maintenance

• Support for collaborative feedback, evolution• Change management• Support for ‘comments’ and ‘annotations’, i.e.

self-documentation• Package management: creation, dependency,

consistency checking


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 (2010 papers

went in yesterday)

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Semantic Web Layers


Other projects – ontologies for faceted search


For data integration


Ontology packaging


Provenance


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


So far in 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 but we like Jena and

SWRL a lot!• However, the tools still lag behind – especially for visual

and natural language development


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.


Modularization

• 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.


‘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.


‘Interfaces’ or ‘Extensions’

• 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.


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)



• OWL 2 RL Synopsis:– focused on ontology interoperability with rule extended

DBMSs where scalable reasoning over large datasets is the most important task

• Likely current 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


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 have aggregated our efforts into a:

Semantic eScience Framework (SESF)*– Configurable, i.e. ontology loadable and driven


High-level architecture


Provenance aware faceted search


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…


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

http://linkeddata.org/


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

• Xinformatics is the key - we continue to need to bridge the computer science and application communities


Further Information

• http://tw.rpi.edu/portal/SESF• Contacts:

– [email protected], http://tw.rpi.edu/wiki/Peter_Fox

http://tw.rpi.edu/portal/SESF

mailto:[email protected]

mailto:[email protected]

http://tw.rpi.edu/wiki/Peter_Fox

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