special library association washington, dc june 15, 2009 · 2009. 6. 15. · special library...

Challenges and Promises of the Semantic Webin Health Care and Life Sciences

Olivier Bodenreider

Lister Hill National Centerfor Biomedical Communications

Bethesda, Maryland - USA

Special Library AssociationWashington, DCJune 15, 2009

Lister Hill National Center for Biomedical Communications 2

Outline

Semantic WebSemantic Web for Health Care and Life SciencesPromisesChallengesSemantic Web and medical libraries

Semantic Web

Quick introduction


Defining the Semantic Web

“The Semantic Web... is an extension of the current web in which... information is given well-defined meaning,... better enabling computers and people to work in cooperation.”

The Semantic WebTim Berners-Lee, James Hendler and Ora

LassilaScientific American, May 2001http://www.scientificamerican.com/article.cfm?id=

the-semantic-web

http://www.scientificamerican.com/article.cfm?id=the-semantic-web

http://www.scientificamerican.com/article.cfm?id=the-semantic-web


From linking documents to linking data

Original WWWWeb of documentsLinks among documentsNo semantics in the linksFor humans

Semantic WebWeb of dataLinks among concepts in resourcesLinks have an explicit semanticsFor humans and machines


Set of enabling technologies and tools

Unambiguous names for resources and entitiesStandard representation for explicit links among entitiesShared conceptualization of the domainStandard tools for querying dataStandard mechanisms for reasoning over data


Semantic Web in 2009 Standards

StandardsUniform Resource Identifiers (URI)Resource Description Framework (RDF)Web Ontology Language (OWL)SPARQL query languageRule Interchange Format (RIF)


Semantic Web in 2009 Datahttp://linkeddata.org

http://linkeddata.org/


Linked data


Linked data

Resources available in RDFUnique, unambiguous identifiers for entitiesExplicit relations among entities

Links across resources (federation)Enabled by

Shared identifiers across resourcesGlobal identifiers, resolvable on the web

Semantic Webfor Health Care and Life Sciences


The original Semantic Web scenario

“Mom needs to see a specialist and then has to have a series of physical therapy sessions. […] I'm going to have my agent set up the appointments.”“The agent promptly retrieved information about Mom's prescribed treatment from the doctor's agent, looked up several lists of providers, and checked for the ones in-plan for Mom’s insurance within a 20-mile radiusof her home and with a rating of excellent or very good on trusted rating services.”…

The Semantic WebTim Berners-Lee, James Hendler and Ora

LassilaScientific American, May 2001


From linked data…http://linkeddata.org

http://linkeddata.org/


…To linked biomedical data[Tim Berners-Lee TED 2009 conference]http://www.w3.org/2009/Talks/0204-ted-tbl/#(1)

http://www.w3.org/2009/Talks/0204-ted-tbl/#(1)


W3C Health Care and Life Sciences IG

http://www.w3.org/2001/sw/hcls/

http://www.w3.org/2001/sw/hcls/


W3C Health Care and Life Sciences IG

Formed in 2005Re-chartered in 2008

Broad industry participationOver 100 members Mailing list of over 600

Get involved!Team contact: Eric Prud’hommeaux <[email protected]>


HCLSIG activities

Document use cases to aid individuals in understanding the business and technical benefits of using Semantic Web technologiesDocument guidelines to accelerate the adoption of the technologyImplement a selection of the use cases as proof-of-concept demonstrationsDevelop high-level vocabulariesDisseminate information about the group’s work at government, industry, and academic events


HCLSIG task forces

BioRDFUse of RDF (and OWL) to represent biomedical data

Clinical Observations InteroperabilityEnable the reuse of clinical researchand clinical practice data

Linking Open Drug DataLinking drug information sources

Translational Medicine OntologyDevelopment of an ontology for biomedical data integration

Scientific DiscourseAnnotate information extracted from the scientific literature

TerminologyUse of SW technologies for representing existing biomedical terminologies

Promises of the Semantic Webfor Health Care and Life Sciences


Biomedical Semantic Web

IntegrationData/InformationE.g., translational research

Hypothesis generationKnowledge discovery

[Ruttenberg, BMC Bioinf. 2007]


HCLS mashup of biomedical sources

NeuronDB

BAMS

NC Annotations

Homologene

SWAN

Entrez Gene

Gene Ontology

Mammalian Phenotype

PDSPki

BrainPharm

AlzGene

Antibodies

PubChem

MeSH

Reactome

Allen Brain Atlas

Publications

http://esw.w3.org/topic/HCLS/HCLSIG_DemoHomePage_HCLSIG_Demo


Shared identifiers Example

GO


HCLS mashup NeuronDB

Protein (channels/receptors)NeurotransmittersNeuroanatomyCellCompartmentsCurrents

BAMSProteinNeuroanatomyCellsMetabolites (channels)PubMedID

NC Annotations

Genes/ProteinsProcessesCells (maybe)PubMed ID

Allen Brain Atlas

GenesBrain imagesGross anatomy -> neuroanatomy

Homologene

GenesSpeciesOrthologiesProofs

SWAN

PubMedIDHypothesisQuestionsEvidence

Genes

Entrez GeneGenesProtein

GOPubMedID

Interaction (g/p)Chromosome

C. location

GO

Molecular functionCell components

Biological processAnnotation gene

PubMedID

Mammalian Phenotype

Genes Phenotypes

DiseasePubMedID

ProteinsChemicals

Neurotransmitters

PDSPki

BrainPharmDrug

Drug effectPathological agent

PhenotypeReceptorsChannelsCell typesPubMedIDDisease

AlzGene

Gene Polymorphism

PopulationAlz Diagnosis

AntibodiesGenes Antibodies

PubChem

NameStructurePropertiesMeSH term

MeSHDrugsAnatomyPhenotypesCompoundsChemicalsPubMedIDPubChem

Reactome

Genes/proteinsInteractionsCellular locationProcesses (GO)


HCLS mashup NeuronDB

Protein (channels/receptors)NeurotransmittersNeuroanatomyCellCompartmentsCurrents

BAMSProteinNeuroanatomyCellsMetabolites (channels)PubMedID

NC Annotations

Genes/ProteinsProcessesCells (maybe)PubMed ID

Allen Brain Atlas

GenesBrain imagesGross anatomy -> neuroanatomy

Homologene

GenesSpeciesOrthologiesProofs

SWAN

PubMedIDHypothesisQuestionsEvidence

Genes

Entrez GeneGenesProtein

GOPubMedID

Interaction (g/p)Chromosome

C. location

GO

Molecular functionCell components

Biological processAnnotation gene

PubMedID

Mammalian Phenotype

GenesPhenotypes

DiseasePubMedID

ProteinsChemicals

Neurotransmitters

PDSPki

BrainPharmDrug

Drug effectPathological agent

PhenotypeReceptorsChannelsCell typesPubMedIDDisease

AlzGene

GenePolymorphism

PopulationAlz Diagnosis

AntibodiesGenesAntibodies

PubChem

NameStructurePropertiesMeSH term

MeSHDrugsAnatomyPhenotypesCompoundsChemicalsPubMedIDPubChem

Reactome

Genes/proteinsInteractionsCellular locationProcesses (GO)


HCLS mashups

Based on RDF/OWLBased on shared identifiers

“Recombinant data” (E. Neumann)

Ontologies used in some casesSupport applications (SWAN, SenseLab, etc.)

Journal of Biomedical Informaticsspecial issue on Semantic bio-mashups[J. Biomedical Informatics 41(5) 2008]


Semantic bio-mashupsBio2RDF: Towards a mashup to build bioinformatics knowledge systemsIdentifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledgeSchema driven assignment and implementation of life science identifiers (LSIDs)The SWAN biomedical discourse ontologyAn ontology-driven semantic mashup of gene and biological pathway information: Application to the domain of nicotine dependenceTowards an ontology for sharing medical images and regions of interest in neuroimagingyOWL: An ontology-driven knowledge base for yeast biologistsDynamic sub-ontology evolution for traditional Chinese medicine web ontologyOntology-centric integration and navigation of the dengue literatureInfrastructure for dynamic knowledge integration—Automated biomedical ontology extension using textual resourcesAn ontological knowledge framework for adaptive medical workflowSemi-automatic web service composition for the life sciences using the BioMoby semantic web frameworkCombining Semantic Web technologies with Multi-Agent Systems for integrated access to biological resources

[J. Biomedical Informatics 41(5) 2008]


Bio2RDF

“Semantic web atlas of postgenomic knowledge”65M “triples”34 biomedical sourcesPublicly availableDistributed environment


Another mashup example

gene

GO

PubMed

Gene name

OMIM

Sequence

InteractionsGlycosyltransferase

Congenital muscular dystrophyLink between glycosyltransferase activity and

congenital muscular dystrophy?


…

APP(GeneID: 351)

amyloid beta A4 protein

has_protein_name


APP (geneid-351) amyloid beta A4 protein

eg:has_protein_reference_name_E

subject predicate object

RDF triple Gene property


RDF graph Connecting several genes

PARK1 Parkinson disease

has_associated_disease

MAPT Parkinson disease

MAPT Pick disease

TBP Parkinson disease

TBP Spinocerebellar ataxia


Parkinson diseaseMAPT

Pick disease

Parkinson diseaseTBP

Spinocerebellar ataxia


MAPT Pick disease

TBP Spinocerebellar ataxia


From glycosyltransferaseto congenital muscular dystrophy

MIM:608840 Muscular dystrophy, congenital, type 1D

GO:0008375

has_associated_phenotype

has_molecular_function

EG:9215LARGE

acetylglucosaminyl-transferase

GO:0016757glycosyltransferase

GO:0008194isa

GO:0008375 acetylglucosaminyl-transferase

GO:0016758


Summary of promises

FlexibilityEasier to federate than relational databasesInherently distributed

Based on standardsTooling available

Platform for data integrationEnabling technology for semantic interoperability, hypothesis generation and knowledge discovery

Challenges of the Semantic Webfor Health Care and Life Sciences


Challenging issues

Permanent identifiers for biomedical entitiesBridges across ontologiesOther issues

Availability of biomedical datasetsDiscoverabilityFormalismOntology integrationScalability

Challenging issues

Permanent identifiers for biomedical entities


Identifying biomedical entities

Multiple identifiers for the same entity in different ontologiesBarrier to data integration in general

Data annotated to different ontologies cannot “recombine”Need for mappings across ontologies

Barrier to data integration in the Semantic WebMultiple possible identifiers for the same entity

Depending on the underlying representational scheme (URI vs. LSID)Depending on who creates the URI


Possible solutions

PURL http://purl.orgOne level of indirection between developers and usersIndependence from local constraints at the developer’s end

The institution creating a resource is also responsible for minting URIs

E.g., URI for genes in Entrez Gene

Guidelines: “URI note”W3C Health Care and Life Sciences Interest Group

Shared names initiativeIdentify resources vs. entities

[http://sharedname.org/]

http://purl.org/

Challenging issues

Bridges across ontologies


Trans-namespace integration

Addison Disease(D000224)

Addison's disease (363732003)

Biomedicalliterature

MeSH

Clinicalrepositories

SNOMED CT

Primary adrenocortical insufficiency(E27.1)

ICD 10


(Integrated) concept repositories

Unified Medical Language Systemhttp://umlsks.nlm.nih.govNCBO’s BioPortalhttp://www.bioontology.org/tools/portal/bioportal.htmlcaDSRhttp://ncicb.nci.nih.gov/NCICB/infrastructure/cacore_overview/cadsr

Open Biomedical Ontologies (OBO)http://obofoundry.org/

http://umlsks.nlm.nih.gov/

http://www.bioontology.org/tools/portal/bioportal.html

http://ncicb.nci.nih.gov/NCICB/infrastructure/cacore_overview/cadsr

http://obofoundry.org/


Integrating subdomains


MeSH

Genomeannotations

GOModelorganisms

NCBITaxonomy

Geneticknowledge bases

OMIM


SNOMED CTOthersubdomains

…

Anatomy

FMA

UMLS


Integrating subdomains


Genomeannotations

Modelorganisms



Othersubdomains

Anatomy


Trans-namespace integration

Genomeannotations

GOModelorganisms

NCBITaxonomy


OMIMOther

subdomains

…

Anatomy

FMA

UMLSAddison Disease (D000224)

Addison's disease (363732003)


MeSH


SNOMED CT

UMLSC0001403


Mappings

Created manually (e.g., UMLS)PurposeDirectionality

Created automatically (e.g., BioPortal)Lexically: ambiguity, normalizationSemantically: lack of / incomplete formal definitions

Key to enabling semantic interoperabilityEnabling resource for the Semantic Web

Challenging issues

Other issues


Availability

Publicly available datasetsIs a de facto prerequisite for Semantic Web applicationsIs a requirement from some funding agencies (“sharing plan”)

Many datasets are freely availableModel organisms databases annotated to the Gene OntologyNCBI knowledge bases in EntrezOpen Access journalsPublic archives (e.g., PubMedCentral)Ontologies from the Open Biomedical Ontology (OBO) family

Limited availabilityIntellectual property issues

Some ontologies and terminologiesConfidentiality and privacy issues

Personally identifiable medical information


Discoverability

No universal repositories for biomedical datasetsSome datasets made available through portals (NCBI, EBI, NCBO)

Ontology repositoriesUMLS: 152 source vocabularies(biased towards healthcare applications)NCBO BioPortal: ~141ontologies(biased towards biological applications)Limited overlap between the two repositories

Need for discovery servicesMetadata for ontologies and biomedical datasets


Formalism

Several major formalism for ontologiesWeb Ontology Language (OWL) – NCI ThesaurusOBO format – most OBO ontologiesUMLS Rich Release Format (RRF) – UMLS, RxNorm

Biomedical datasetsRDF/OWLLegacy: free text, Excel spreadsheets, PowerPoint

Conversion mechanismsOBO to OWLLexGrid (import/export to LexGrid internal format)


Ontology integration

Post hoc integration , form the bottom upUMLS approachIntegrates ontologies “as is”, including legacy ontologiesFacilitates the integration of the corresponding datasets

Coordinated development of ontologiesOBO Foundry approachEnsures consistency ab initioExcludes legacy ontologies


Scalability

Billions of triples for raw data in biomedicineNeed for query systems to access distributed repositories (“data cloud”)

Technical solutions have emergedTraditional vendors have embraced RDF/OWLNew vendors/products

Semantic Weband medical libraries


Why additional library services?

Biomedical information is growing at an increasingly faster pace

High-throughput approach to knowledge processingInformation retrieval is the starting point, not the end of the journey for the researcher

Towards “computable” knowledgeIntegration between literature and other resources is insufficient

Adequate for navigation purposesInsufficient for knowledge processing


What additional services?

Refined information retrievalIndexing on relations in addition to conceptsFind articles asserting that IL-13 inhibits COX-2

Multi-document summarizationExtract and visualize facts from the literatureSummarize the top 300 papers on panic disorder

Question answeringClinical and biological questionsWhat drugs interact with imipramine?

Knowledge discoveryReasoning with facts from heterogeneous resourcesFrom MEDLINE and UMLS together


Normalized and integrated knowledge

Normalized knowledgeCommon formatCommon identification mechanism

Integrated knowledgeSingle repositorySeamless environmentPhenotype and genotype information together

Biomedical Knowledge Repository


Sources of knowledge

Biomedical literaturePredications extracted from MEDLINE abstracts and full-text publicly available articles using text mining techniquesOther corpora (e.g., ClinicalTrials.gov)

Terminological knowledgeUMLS

Structured knowledge basesNCBI resources (e.g., Entrez Gene)Functional annotations from model organism databases…

Contributed knowledgeThe repository is open to collaborators outside NLM


Formalism Triples

FactsAssertionsRelationsSemantic predicationsRDF triples

Imipramine Panic Disorder

treats

APP Alzheimer disease

has_associated_disease

concept1 concept2

relationship


Annotated knowledge Metadata

Provenance informationSource (e.g., PMID)Extraction mechanismTimestamp

Frequency informationRedundancy

Collaborative annotation“Was this information useful?”Context of use/usefulness

BiomedicalKnowledgeRepository

InformationRetrieval

QuestionAnswering

KnowledgeDiscovery

DocumentSummarization

Sourceselection(PubMed,

annotations)

UMLSTerminological

Knowledge

GO

EntrezGene Structured

Knowl. Bases

ContributedKnowledge

MEDLINE BiomedicalLiteratureCT.gov

Advanced Library Services A research project at NLM


Semantic Medline Prototypehttp://skr3.nlm.nih.gov/SemMedDemo/

Courtesy of Tom Rindflesch (NLM)

Wrapping up


Take home points

Biomedical Semantic WebData integrationEnable applications

Hypothesis generationKnowledge discovery

Paradigm shiftMigration towards data-driven research(as opposed to hypothesis-driven research)Translational research

MedicalOntologyResearch

Olivier Bodenreider

Lister Hill National Centerfor Biomedical CommunicationsBethesda, Maryland - USA

Contact:Web:

[email protected]

special library association washington, dc june 15, 2009 · 2009. 6. 15. · special library...

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