SADI SWSIP '09 'cause you can't always GET what you want!

’cause you can’t always GET what you want!

Web Services

vs.

Semantic Web

Web Servicesare not “connected” to

the Semantic Web

Why?

Web ServicesXML + XML Schema

Semantic WebRDF + OWL

Web ServicesPOST of SOAP-XML

Semantic WebGET of RDF-XML

Web ServicesNo (rigorous) semantics

Semantic WebRich, flexible semantics

Web Services&

Semantic Web

Fundamentally different technologies!

>1000 X more data in the Deep Web than in Web pages

In bioinformatics this is primarily databases and

analytical algorithms

Web Service output is

critical to success

for the Semantic Web!!

RESEARCH QUESTION

WITHOUT INVENTING A NEW

TECHNOLOGY OR STANDARD

Can we define

frameworks and/or best practices

that make Web Services

“transparent” to the Semantic Web?

Semantic Web?

An information system where machines can receive information from one source, re-interpret it, and correctly use it for a purpose that the source had

not anticipated.

Semantic Web?

If we cannot achieve those two things, then IMO we don’t have a “semantic web”, we only have a distributed (??), linked database… and that isn’t

particularly exciting…

What do we need to do?

Make Web Services exhibit the same “behavior” as GET

Add rich Semantics to Web Services at every level of the WS “stack”

What do we have to work with?(Without inventing new technologies or standards…)

Data

Interface annotations

Founding partner

Semantic Automated Discovery and Integrationhttp://sadiframework.org

History of SADI

Based on observations of the usage and behaviour of BioMoby Semantic Web Services

since 2002

SADI Observation #1:

What does ‘GET’ really do?

What “behaviour” of GET do we need to mimic

in a Web Service?

SADI Observation #1:

GET guarantees the response relates to the input URI in a very precise and predictable way

POST does not…

Web Services have a fundamentally different semantic behaviour than the Semantic Web

SADI Observation #1:

We can fix that!

(without breaking any existing rules or standards!)

SADI Observation #2:

All Web Services in Bioinformatics create implicit biological relationships

between their input and output

SADI Observation #2:

SADI: Core Proposition

Make the implicit explicit

Model all Web Services this way

SADI: Core Proposition

Web Service output must be Triples

SUBJECT URI of the output graph is the same URI

as the SUBJECT URI of the input graphthat was POSTed to the Web Service

Define OWL classes that describe your input and output triples

Consequence(i.e. why SADI works!)

The Semantics of the triples from a Web Service are now

explicit and identical to the Semantics of GET

How do we exploit this?

Current Web Service definition systems such as WSDL, and OWL-S

are missing a crucial annotation element…

…the semantic relationship between input and output

How do we exploit this?

With SADI services, this relationship can be automatically derived by “diff-ing” the input and output OWL classes

…because the SUBJECT node of the input and output graphs is guaranteed to be the same!

How do we exploit this?

We have constructed a simple prototype

Web Services registry that

provides discovery based on

annotation of these biological relationships

Demo #1a plug-in to the

IO-Informatics Knowledge Explorer

The Sentient Knowledge Explorerfrom IO Informatics

A Semantic Integration, Visualization and Search Tool

SADI Plug-in to the Sentient Knowledge Explorer

• The Knowledge Explorer has a plug-ins API that we have utilized to pass data directly from SADI to the KE visualization and exploration system

• Data elements in KE are queried for their “type” (rdf:type); this is used to discover additional data properties by querying the SADI Web Service registry

BACKGROUND of DATASET

NIST toxicity compendium study

8 toxicantsLiver

SerumUrine

Genomic [Affymetrix MA]Metabolomic [LC/MS] data

• conducted under NIST Advanced Technology Program (ATP), Award # 70NANB2H3009 as a Joint Venture between Icoria / Cogenics (Division of CLDA) and IO Informatics.

• Microarray studies were conducted under NIEHS contract # N01-ES-65406. • The Alcohol study was conducted under NIAAA contract # HHSN281200510008C in

collaboration with Bowles Center for Alcohol Studies (CAS) at UNC. • This work was also made possible through contributions from members of IO

Informatics’ Working Group on “Semantic Applications for Translational Research”.

DEMOKnowledge Explorer

Plug-in

For more information about the Knowledge Explorer surf to:http://io-informatics.com

Call SADI to provide discovery of predicates based on data-type

SADI fills-in the “Encodes” property from the discovered Web Service(s)

Discover services that provide the hasGOTerm property for Protein Sequence datatype

DEMOKnowledge Explorer

Plug-in

For more information about the Knowledge Explorer surf to:http://io-informatics.com

SADI Demo #2

Imagine there is a “virtual graph” connecting every conceivable input for every conceivable Web Service

to their respective outputs

How do we query that graph?

“SHARE”

Semantic Health And Research EnvironmentSADI client application

What pathways does UniProt protein P47989 belong to?

PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>PREFIX ont: <http://ontology.dumontierlab.com/>PREFIX uniprot: <http://lsrn.org/UniProt:>SELECT ?gene ?pathway WHERE {

uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway .

}

Recapwhat we just saw

A standard SPARQL query was entered into SHARE – a SADI-based query engine

The query was interpreted to extract the properties being queried and these were passed to SADI for Web

Service discovery

SADI searched-for, found, and accessed the databases and/or analytical tools capable of

generating those properties

Recapwhat we just saw

We posed, and answered a complex database query

WITHOUT A DATABASE

(in fact, the data didn’t even have to exist...)

RDF graph browsing and querying is useful…

…but where’s the semantics??

Let’s bring in some ontologies!

My Definition of Ontology (for this talk)

Ontologies explicitly define the things that exist in “the world”

based on what properties each kind of thing must have

Ontology Spectrum

Catalog/ID

SelectedLogical

Constraints(disjointness,

inverse, …)

Terms/glossary

Thesauri“narrowerterm”relation

Formalis-a

Frames(Properties)

Informalis-a

Formalinstance

Value Restrs.

GeneralLogical

constraints

Semantic Web?

An information system where machines can receive information from one source, re-interpret it, and correctly use it for a purpose that the source had

not anticipated.

Dynamic

Discovery

Distributed

Interpretation

Re-interpretation

Powered by SADI

Prototype SADI-enabled SWS Client

Data exhibits “late binding”

Late binding:

“purpose and meaning” of the data is

not determined untilthe moment it is required

Benefitof late binding

Data is amenable toconstant re-interpretation

How do we achieve this?

DO NOT PRE-CLASSIFY DATA!

just hang properties on it

Catalog/ID

SelectedLogical

Constraints(disjointness,

inverse, …)

Terms/glossary

Thesauri“narrowerterm”relation

Formalis-a

Frames(Properties)

Informalis-a

Formalinstance

Value Restrs.

GeneralLogical

constraints

These are indexing/annotation systems

These are axioms that enable classification

Design OWL-DL ontologies describing cardiovascular concepts

Ontologies are in the “Frames+” area of the Ontology spectrum, and therefore can leverage SADI and be

“executed” as workflows

What the…???

Did you just say “execute ontologies as workflows?!”

Another interesting consequence of the SADI architecture

Ontology = Query = Workflow

WORKFLOW

QUERY:

SELECT images of mutations from genes in organism XXX that share homology to this gene in

organism YYY

Concept:

“Homologous Mutant Image”

As OWL Axioms

HomologousMutantImage is owl:equivalentTo {

Gene Q hasImage image P

Gene Q hasSequence Sequence Q

Gene R hasSequence Sequence R

Sequence Q similarTo Sequence R

Gene R = “my gene of interest” }

Those axioms combine to create an OWL Class:

homologous mutant images

QUERY:

Retrieve owl:homologous

mutant images for gene XXX

Demo #3Discover instances of OWL classes

from data that doesn’t exist…

Show me patients whose creatinine level is increasing over time, along with their latest BUN and creatinine levels.

PREFIX regress: <http://sadiframework.org/examples/regression.owl#> PREFIX patients: <http://sadiframework.org/ontologies/patients.owl#> PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creat FROM <http://sadiframework.org/ontologies/patients.rdf>WHERE {

?patient patients:creatinineLevels ?collection . ?collection regress:hasRegressionModel ?model . ?model regress:slope ?slope

FILTER (?slope > 0) .?patient pred:latestBUN ?bun . ?patient pred:latestCreatinine ?creat .

}

Show me patients whose creatinine level is increasing over time, along with their latest BUN and creatinine levels (now as an OWL class ElevatedCreatininePatient)

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patients: <http://sadiframework.org/ontologies/patients.owl#> PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE {

?patient rdf:type patients:ElevatedCreatininePatient .?patient pred:latestBUN ?bun . ?patient pred:latestCreatinine ?creat .

}

Start burrowing through the OWL class find that we need aregression model OWL class

Regression models have features like slopes and intercepts… and so onThe class is completely decomposed until a set of required Services are discoveredcapable of creating all these necessary properties

Successful decomposition of the OWL class to discover the need for a LinearRegression Web Service, and so on

VOILA!

Demo #3Discover instances of OWL classes

from data that doesn’t exist…

SADI and CardioSHARE

OWL Class restrictions converted into workflows

SPARQL queries converted into workflows

Reasoning happening at the same time as queries are being executed

(AFAIK)

This is a completely novel behaviour!

Ontologies are reasoned

Workflows are executed

Queries are resolved

so what do we call what this client does??

“Reckoning”Dynamic discovery of instances of OWL classes through synthesis and invocation of a Web Service

workflow capable of generating data described by the OWL Class restrictions, followed by reasoning to classify the data into that ontology

But OWL Classes can be (and often are) completely

“made-up”

What does that mean in the context of SADI and CardioSHARE?

Current Research

We believe that ontologies and hypotheses are, in some ways, the same “thing”…

…simply assertions about individuals that may or may not exist

If an instance of a class exists, then the hypothesis is “validated”

Current Research Constructing OWL classes that represent patient categories mimicking clinical outcomes research experiments done on the BC Cardiac Registry

The original experiments have already been published, and therefore act as a gold-standard

We attempt to use “Reckoning” to automatically discover instances of these patient Classes and compare our results with those of the clinical researchers

CardioSHARE architecture: Increasingly complex ontological layers organize data into richer concepts, even hypotheses

Blood Pressure

Hypertension

Ischemia

Hypothesis

Database 1 Database 2 AnalysisAlgorithmXX

SADIWeb

“agents”

Recap

SADI Semantic Web Services generate triples; the predicates of those triples are indexed... Period!!

For a given query, determine which properties are available, and which need to be

discovered/generated

Discovery of services through index queries + on-the-fly “classification” of local data against the

service interface OWL Classes

Semantic Web

An information system where machines can receive information from one source, re-interpret it, and correctly use it for a purpose that the source had

not anticipated.

What SADI + SHARE supports

Re-interpretation :

The SADI data-store simply collects properties, and matches them up with OWL Classes in a SPARQL query and/or

from individual service provider’s Web Service interface

Novel re-use:

Because we don’t pre-classify, there is no way for the provider to dictate how their

data should be used. They simply add their properties into the “cloud” and

those properties are used in whatever way is appropriate for me.

What SADI + SHARE supports

Data remains distributed – no warehouse!

Data is not “exposed” as a SPARQL endpoint greater provider-control over

computational resources

Yet data appears to be a SPARQL endpoint… no modification of SPARQL or

reasoner required.

Important “wins”

And all this because we simply require

that the input URI

is the same as the output URI

Join us!

SADI and CardioSHARE are Open-Source projects

Come join us – we’re having a lot of fun!!

http://sadiframework.org

Fin