Ontology alignment:evaluation and
healthcare applications
Ernesto Jimenez-Ruiz
HealthInsight workshop, Oslo, May 20, 2016
1 / 32
Outline
Preliminaries
Ontology Alignment
Healthcare Applications
2 / 32
The presentation in a nutshell
Introduction to ontologies
Ontology alignment
Ontology alignment in practiceSemantic annotation and access of clinical lettersSemantic enrichment of UK BioBank Cardiac cine-MRI ScansShared hypothesis testing in the biomedical domainPistoia alliance mapping project (disease-phenotype domain)Lung Cancer Assistant (LCA)Optique: Scalable End-user Access to Big Data
3 / 32
The presentation in a nutshell
Introduction to ontologies
Ontology alignment
Ontology alignment in practiceSemantic annotation and access of clinical lettersSemantic enrichment of UK BioBank Cardiac cine-MRI ScansShared hypothesis testing in the biomedical domainPistoia alliance mapping project (disease-phenotype domain)Lung Cancer Assistant (LCA)Optique: Scalable End-user Access to Big Data
3 / 32
The presentation in a nutshell
Introduction to ontologies
Ontology alignment
Ontology alignment in practiceSemantic annotation and access of clinical lettersSemantic enrichment of UK BioBank Cardiac cine-MRI ScansShared hypothesis testing in the biomedical domainPistoia alliance mapping project (disease-phenotype domain)Lung Cancer Assistant (LCA)Optique: Scalable End-user Access to Big Data
3 / 32
What is an ontology?
Introduces vocabularyrelevant to a domain
Anatomy
Specifies meaning(semantics) of terms
Heart is a muscular organthat is part of thecirculatory system
Formalised using suitablelogic
Heart SUBCLASSOFMuscularOrgan AND(isPartOf SOMECirculatorySystem)
Borrowed from Ian’s slides: Ontologies and the Semantic Web:The Story So Far. Zhejiang University, April 2010
4 / 32
What is an ontology?
Introduces vocabularyrelevant to a domain
Anatomy
Specifies meaning(semantics) of terms
Heart is a muscular organthat is part of thecirculatory system
Formalised using suitablelogic
Heart SUBCLASSOFMuscularOrgan AND(isPartOf SOMECirculatorySystem)
Borrowed from Ian’s slides: Ontologies and the Semantic Web:The Story So Far. Zhejiang University, April 2010
4 / 32
What is an ontology?
Introduces vocabularyrelevant to a domain
Anatomy
Specifies meaning(semantics) of terms
Heart is a muscular organthat is part of thecirculatory system
Formalised using suitablelogic
Heart SUBCLASSOFMuscularOrgan AND(isPartOf SOMECirculatorySystem)
Borrowed from Ian’s slides: Ontologies and the Semantic Web:The Story So Far. Zhejiang University, April 2010
4 / 32
OWL: Web Ontology Language
World Wide Web Consortium (W3C) standard
The most widely used ontology modelling language
e.g. FMA, NCI, SNOMED CT
Formal underpinning of OWL is based on formal logic
Supported by tools and infrastructure
OWL example axiomsJuvenileArthritis v JuvenileDiseasePolyArthritis ≡ Arthritis u > 5 affects.JointDisease u Joint v ⊥JuvenileIdiopathicArthritis @ “Juvenile Rheumatoid Arthritis”John:Pattient u ∃suffersFrom.JuvenileIdiopathicArthritis
5 / 32
OWL: Web Ontology Language
World Wide Web Consortium (W3C) standard
The most widely used ontology modelling language
e.g. FMA, NCI, SNOMED CT
Formal underpinning of OWL is based on formal logic
Supported by tools and infrastructure
OWL example axiomsJuvenileArthritis v JuvenileDiseasePolyArthritis ≡ Arthritis u > 5 affects.JointDisease u Joint v ⊥JuvenileIdiopathicArthritis @ “Juvenile Rheumatoid Arthritis”John:Pattient u ∃suffersFrom.JuvenileIdiopathicArthritis
5 / 32
What ontologies are good for?
Help identify and resolve disagreements in the domain
Ontology alignment will play a key role
Independence of logical/physical schema
Formulation of queries closer to domain experts
Incomplete and semi-structured data
Integration of heterogeneous sources
Ontology alignment will play a key role
6 / 32
What ontologies are good for?
Help identify and resolve disagreements in the domain
Ontology alignment will play a key role
Independence of logical/physical schema
Formulation of queries closer to domain experts
Incomplete and semi-structured data
Integration of heterogeneous sources
Ontology alignment will play a key role
6 / 32
Outline
Preliminaries
Ontology Alignment
Healthcare Applications
7 / 32
Ontology alignment: motivation
An application domain can be modelled with different pointsof view and purposes
Ontologies with different naming and modellingconventions exist for the same domain
Aligning these ontologies will enable interoperability betweenontology-based information systems
Reusing vocabulary from domain ontologies is a good practicein ontology engineering
8 / 32
Ontology alignment: definition
Ontology alignments or mappings M are sets of tuples〈e1, e2, n, ρ〉
e1, e2 are entities in the input ontologies O1 and O2
n a confidence value between 0 and 1ρ is the semantic relationship between e1 and e2 (e.g.subsumption, equivalence or disjointness)
Formalized as OWL 2 axiomsWhere the semantic relationship ρ is one of {≡,v,w,⊥}Confidence values n are represented as axiom annotationsNo extra semantics
OWL 2 example mappingsO1:Joint ≡ O2:JointO3:Joint structure ≡ O2:JointO3:Joint structure ≡ O2:Articulation
9 / 32
Ontology alignment: definition
Ontology alignments or mappings M are sets of tuples〈e1, e2, n, ρ〉
e1, e2 are entities in the input ontologies O1 and O2
n a confidence value between 0 and 1ρ is the semantic relationship between e1 and e2 (e.g.subsumption, equivalence or disjointness)
Formalized as OWL 2 axiomsWhere the semantic relationship ρ is one of {≡,v,w,⊥}Confidence values n are represented as axiom annotationsNo extra semantics
OWL 2 example mappingsO1:Joint ≡ O2:JointO3:Joint structure ≡ O2:JointO3:Joint structure ≡ O2:Articulation
9 / 32
Ontology alignment: definition
Ontology alignments or mappings M are sets of tuples〈e1, e2, n, ρ〉
e1, e2 are entities in the input ontologies O1 and O2
n a confidence value between 0 and 1ρ is the semantic relationship between e1 and e2 (e.g.subsumption, equivalence or disjointness)
Formalized as OWL 2 axiomsWhere the semantic relationship ρ is one of {≡,v,w,⊥}Confidence values n are represented as axiom annotationsNo extra semantics
OWL 2 example mappingsO1:Joint ≡ O2:JointO3:Joint structure ≡ O2:JointO3:Joint structure ≡ O2:Articulation
9 / 32
Ontology alignment: systems
Given two input ontologies O1 and O2 generate an alignmentM as output.
Large number of available ontology alignment systems
Ontology Alignment Evaluation Initiative (OAEI)
LogMap . . .performs a simple matching but efficient (based on sophisticatedlexical and structural indexes),can efficiently match semantically rich ontologies containinghundreds of thousands of classes (FMA, NCI and SNOMED),incorporates reasoning and repair capabilitiesis one of the top systems in the OAEI evaluation campaign, andis the only system participating in all OAEI 2015 tracks.
Large-scale Interactive Ontology Matching: Algorithms andImplementation. ECAI 2012
LogMap: Logic-based and Scalable Ontology Matching. ISWC’11
10 / 32
Ontology alignment: systems
Given two input ontologies O1 and O2 generate an alignmentM as output.
Large number of available ontology alignment systems
Ontology Alignment Evaluation Initiative (OAEI)
LogMap . . .performs a simple matching but efficient (based on sophisticatedlexical and structural indexes),can efficiently match semantically rich ontologies containinghundreds of thousands of classes (FMA, NCI and SNOMED),incorporates reasoning and repair capabilitiesis one of the top systems in the OAEI evaluation campaign, andis the only system participating in all OAEI 2015 tracks.
Large-scale Interactive Ontology Matching: Algorithms andImplementation. ECAI 2012
LogMap: Logic-based and Scalable Ontology Matching. ISWC’11
10 / 32
Ontology alignment: resources
UMLS (Unified Medical Language System) Metathesaurus
Integrates more than one hundred thesauri and ontologiesContains more than 6 million entities
BioPortalContains more than 500 ontologiesRepresent a network of ontologiesMore than 10 million mappings are availableAlso includes user-submitted alignments
11 / 32
Ontology alignment: resources
UMLS (Unified Medical Language System) Metathesaurus
Integrates more than one hundred thesauri and ontologiesContains more than 6 million entities
BioPortalContains more than 500 ontologiesRepresent a network of ontologiesMore than 10 million mappings are availableAlso includes user-submitted alignments
11 / 32
Ontology alignment: limitations
Alignment systems, BioPortal and UMLS techniques. . .
are mostly based on lexical algorithms,although they also include user-submitted alignments(BioPortal) and expert assessment (UMLS),may lead to undesired cross-references,may lead to logical errors when considering the semantics of thesources being integrated.
12 / 32
Ontology alignment: limitations
Alignment systems, BioPortal and UMLS techniques. . .are mostly based on lexical algorithms,although they also include user-submitted alignments(BioPortal) and expert assessment (UMLS),may lead to undesired cross-references,may lead to logical errors when considering the semantics of thesources being integrated.
12 / 32
Ontology alignment: limitations
Alignment systems, BioPortal and UMLS techniques. . .are mostly based on lexical algorithms,although they also include user-submitted alignments(BioPortal) and expert assessment (UMLS),may lead to undesired cross-references,may lead to logical errors when considering the semantics of thesources being integrated.
12 / 32
Logic-based Assessment of UMLS
Assessment of the integration of FMA, NCI and SNOMED CTontologies within UMLS
UMLS alignmentsOntologies # Logical errors
FMA-NCI 3,024 655
FMA-SNOMED 9,072 6,179
SNOMED-NCI 19,622 20,944
Ernesto Jimenez-Ruiz, et al. Logic-based assessment of thecompatibility of UMLS ontology sources. J. Biomedical Semantics2(S-1): S2 (2011)
Ernesto Jimenez-Ruiz, Bernardo Cuenca Grau. LogMap:Logic-based and Scalable Ontology Matching. In the 10thInternational Semantic Web Conference (ISWC 2011).
13 / 32
Logic-based Assessment of UMLS
Assessment of the integration of FMA, NCI and SNOMED CTontologies within UMLS
UMLS alignmentsOntologies # Logical errors
FMA-NCI 3,024 655
FMA-SNOMED 9,072 6,179
SNOMED-NCI 19,622 20,944
Ernesto Jimenez-Ruiz, et al. Logic-based assessment of thecompatibility of UMLS ontology sources. J. Biomedical Semantics2(S-1): S2 (2011)
Ernesto Jimenez-Ruiz, Bernardo Cuenca Grau. LogMap:Logic-based and Scalable Ontology Matching. In the 10thInternational Semantic Web Conference (ISWC 2011).
13 / 32
Logic-based Assessment of BioPortal
Identification of error-free sets of alignments
BioPortal alignmentsOntologies # Logical errors
BDO-NCIT 1,636 34,341
CCONT-NCIT 2,097 50,304
EFO-NCIT 2,507 60,347
EP-FMA 78,489 210
EP-NCIT 2,465 14,687
MA-FMA 961 850
OMIM-NCIT 5,178 70,172
SDO-EP 135 44
UBERON-FMA 1,932 4,753
ZFA-EFO 427 913
ZFA-UBERON 724 104
Daniel Faria, Ernesto Jimenez-Ruiz, et al. Towards annotatingpotential incoherences in BioPortal mappings. In the 13thInternational Semantic Web Conference (ISWC 2014).
14 / 32
Logic-based Assessment of BioPortal
Identification of error-free sets of alignments
BioPortal alignmentsOntologies # Logical errors
BDO-NCIT 1,636 34,341
CCONT-NCIT 2,097 50,304
EFO-NCIT 2,507 60,347
EP-FMA 78,489 210
EP-NCIT 2,465 14,687
MA-FMA 961 850
OMIM-NCIT 5,178 70,172
SDO-EP 135 44
UBERON-FMA 1,932 4,753
ZFA-EFO 427 913
ZFA-UBERON 724 104
Daniel Faria, Ernesto Jimenez-Ruiz, et al. Towards annotatingpotential incoherences in BioPortal mappings. In the 13thInternational Semantic Web Conference (ISWC 2014).
14 / 32
Assessment of alignment systems: OAEI
Precision and recall wrt reference alignment or silver/goldstandard |MGS|
Precision (Pre) = |M ∩MGS|/|M|Recall (Rec) = |M ∩MGS|/|MGS|The F-score (F) = (2× Pre× Rec)/(Pre + Rec).
Logical errors ofM wrt O1 and O2.
Computation times are also considered.
Ontology Alignment Evaluation Initiative (OAEI)An annual campaign3 biomedical-themed tracks in 2016: Anatomy, LargeBio,Phenotype-Disease tracks
Ontology Alignment Evaluation Initiative (OAEI):http://www.ontologymatching.org/
15 / 32
Assessment of alignment systems: OAEI
Precision and recall wrt reference alignment or silver/goldstandard |MGS|
Precision (Pre) = |M ∩MGS|/|M|Recall (Rec) = |M ∩MGS|/|MGS|The F-score (F) = (2× Pre× Rec)/(Pre + Rec).
Logical errors ofM wrt O1 and O2.
Computation times are also considered.
Ontology Alignment Evaluation Initiative (OAEI)An annual campaign3 biomedical-themed tracks in 2016: Anatomy, LargeBio,Phenotype-Disease tracks
Ontology Alignment Evaluation Initiative (OAEI):http://www.ontologymatching.org/
15 / 32
Assessment in the OAEI large biomedical track
Ontologies and Reference Alignment
FMA v2.0 (78,989 classes), NCI v.08.05d (66,724 classes) andSNOMED CT v. Jan. 2009 (306,591 classes).Reference alignment based on UMLS
Matching problems
FMA-NCIFMA-SNOMEDSNOMED-NCI
Results
Results are far from perfect when involving SNOMED CT
16 / 32
Assessment in the OAEI large biomedical track
Voting of computed alignments
Alignments computed in the 2011.5, 2012 and 2013 editions.
FMA-NCI: 18 contributing (independent) systemsFMA-SNOMED: 13 contributing (independent) systemsSNOMED-NCI: 9 contributing (independent) systems
Alignments voted (i.e. computed) by at least one system
Maximum recall
Alignments voted (i.e. computed) by all systems
Maximum precision
17 / 32
Assessment in the OAEI large biomedical track
Voting of computed alignments
Alignments computed in the 2011.5, 2012 and 2013 editions.
FMA-NCI: 18 contributing (independent) systemsFMA-SNOMED: 13 contributing (independent) systemsSNOMED-NCI: 9 contributing (independent) systems
Alignments voted (i.e. computed) by at least one system
Maximum recall
Alignments voted (i.e. computed) by all systems
Maximum precision
17 / 32
Assessment in the OAEI large biomedical track
Voting of computed alignments
Alignments computed in the 2011.5, 2012 and 2013 editions.
FMA-NCI: 18 contributing (independent) systemsFMA-SNOMED: 13 contributing (independent) systemsSNOMED-NCI: 9 contributing (independent) systems
Alignments voted (i.e. computed) by at least one system
Maximum recall
Alignments voted (i.e. computed) by all systems
Maximum precision
17 / 32
Assessment FMA-NCI: voting
0
0.2
0.4
0.6
0.8
1.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Sco
re
Nu
mb
er
of
ma
pp
ing
s
Number of votes
19,394
4,908
3,7092,984 2,779 2,572 2,406 2,168
1,642895
461
SizePrecision
RecallF-measure
Max recall: 0.96Max precision: 1.0
Vote ≥5: P=0.89, R=0.91
18 / 32
Assessment FMA-SNOMED: voting
0
0.2
0.4
0.6
0.8
1.0
1 2 3 4 5 6 7 8 9 10 11 12 13
Sco
re
Nu
mb
er
of
ma
pp
ing
s
Number of votes
19,993
9,437
7,633
6,698
5,518
3,698
2,7052,095
1,688 1,503 1,326 1,091751
SizePrecision
RecallF-measure
Max recall: 0.87Max precision: 0.99
Vote ≥3: P=0.89, R=0.76
19 / 32
Assessment SNOMED-NCI: voting
0
0.2
0.4
0.6
0.8
1.0
1 2 3 4 5 6 7 8 9
Sco
re
Nu
mb
er
of
ma
pp
ing
s
Number of votes
30,252
18,281
15,042
13,404
11,620
10,265
8,9697,843
5,423
SizePrecision
RecallF-measure
Max recall: 0.78Max precision: 0.99
Vote ≥3: P=0.82, R=0.67
20 / 32
User involvement in ontology alignment
Due to the limits to the quality of automated alignmentalgorithms.
Requires tool support
“Good” interfaces and servicesNumber of questions must be limitedSystems should make the most of user inputsSystems should consider the profile of the user
Currently evaluated in the OAEI
Simulation of users with different error rates
Zlatan Dragisic, Valentina Ivanova, Patrick Lambrix , Daniel Faria,Ernesto Jimenez-Ruiz, and Catia Pesquita. User validation inontology alignment. Submitted to ISWC 2016
21 / 32
User involvement in ontology alignment
Due to the limits to the quality of automated alignmentalgorithms.
Requires tool support
“Good” interfaces and servicesNumber of questions must be limitedSystems should make the most of user inputsSystems should consider the profile of the user
Currently evaluated in the OAEI
Simulation of users with different error rates
Zlatan Dragisic, Valentina Ivanova, Patrick Lambrix , Daniel Faria,Ernesto Jimenez-Ruiz, and Catia Pesquita. User validation inontology alignment. Submitted to ISWC 2016
21 / 32
User involvement in ontology alignment
Due to the limits to the quality of automated alignmentalgorithms.
Requires tool support
“Good” interfaces and servicesNumber of questions must be limitedSystems should make the most of user inputsSystems should consider the profile of the user
Currently evaluated in the OAEI
Simulation of users with different error rates
Zlatan Dragisic, Valentina Ivanova, Patrick Lambrix , Daniel Faria,Ernesto Jimenez-Ruiz, and Catia Pesquita. User validation inontology alignment. Submitted to ISWC 2016
21 / 32
Outline
Preliminaries
Ontology Alignment
Healthcare Applications
22 / 32
1. Lung Cancer Assistant (LCA)
An ontology-based system which provides decision supportfor lung cancer treatment
LCA exploits the English Lung Cancer Dataset (LUCADA)
LUCADA ontology represents the semantic layer of the LCA,
Required alignment with SNOMED CTto facilitate interoperability with NHS systemspartially done without too support!
We offered LogMap alignment system to
identify the classes in SNOMED CT related to LUCADAextract a lung cancer-specific module of SNOMED CT
M. Berkan Sesen et al. Lung Cancer Assistant: a hybrid clinicaldecision support application for lung cancer care. Journal of theRoyal Society Interface. 2014
23 / 32
1. Lung Cancer Assistant (LCA)
An ontology-based system which provides decision supportfor lung cancer treatment
LCA exploits the English Lung Cancer Dataset (LUCADA)
LUCADA ontology represents the semantic layer of the LCA,
Required alignment with SNOMED CTto facilitate interoperability with NHS systemspartially done without too support!
We offered LogMap alignment system to
identify the classes in SNOMED CT related to LUCADAextract a lung cancer-specific module of SNOMED CT
M. Berkan Sesen et al. Lung Cancer Assistant: a hybrid clinicaldecision support application for lung cancer care. Journal of theRoyal Society Interface. 2014
23 / 32
1. Lung Cancer Assistant (LCA)
An ontology-based system which provides decision supportfor lung cancer treatment
LCA exploits the English Lung Cancer Dataset (LUCADA)
LUCADA ontology represents the semantic layer of the LCA,
Required alignment with SNOMED CTto facilitate interoperability with NHS systemspartially done without too support!
We offered LogMap alignment system to
identify the classes in SNOMED CT related to LUCADAextract a lung cancer-specific module of SNOMED CT
M. Berkan Sesen et al. Lung Cancer Assistant: a hybrid clinicaldecision support application for lung cancer care. Journal of theRoyal Society Interface. 2014
23 / 32
2. EU Project Optique
Scalable End-user Access to Big DataExposing relational data through ontologies
Table Name
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
Table NameAttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
Table Name
AttributeAttributeAttributeAttribute
Ontologies
Imported
Metadata
BootstrappedManual
MappingsManual
Semantic Data Access
RelationalData
Bootstrapped
AttributeAttributeAttributeAttribute
Table Name
Table Name
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
Table Name
AttributeAttributeAttributeAttribute
Table Name
AttributeAttributeAttributeAttribute
Table Name
Table Name
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
Table NameAttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
Table Name
AttributeAttributeAttributeAttribute
AttributeAttributeAttributeAttribute
Table Name
RDF Data
Export
ApplicationsUsers
E. Jimenez-Ruiz et al. BootOX: Practical Mapping of RDBs toOWL 2. International Semantic Web Conference. 2015
24 / 32
3. UK BioBank Cardiac cine-MRI Scans
Semantic enrichment of free-text annotation of image qualityfor UK BioBank cardiac cine-MRI scansFree text annotations added to a spread-sheetThere is a high lexical and semantic variability in theannotationsOntologies will enhance the processing of the free-textannotationsRequired ontology alignment
to facilitate interoperability with NHS systems (SNOMED CT)and other UK BioBank parts
Ferreira et al. Cardiovascular magnetic resonance artefacts.Journal of Cardiovascular Magnetic Resonance 2013
25 / 32
3. UK BioBank Cardiac cine-MRI Scans
Semantic enrichment of free-text annotation of image qualityfor UK BioBank cardiac cine-MRI scansFree text annotations added to a spread-sheetThere is a high lexical and semantic variability in theannotationsOntologies will enhance the processing of the free-textannotationsRequired ontology alignment
to facilitate interoperability with NHS systems (SNOMED CT)and other UK BioBank parts
Ferreira et al. Cardiovascular magnetic resonance artefacts.Journal of Cardiovascular Magnetic Resonance 2013
25 / 32
3. UK BioBank Cardiac cine-MRI Scans
Semantic enrichment of free-text annotation of image qualityfor UK BioBank cardiac cine-MRI scansFree text annotations added to a spread-sheetThere is a high lexical and semantic variability in theannotationsOntologies will enhance the processing of the free-textannotationsRequired ontology alignment
to facilitate interoperability with NHS systems (SNOMED CT)and other UK BioBank parts
Ferreira et al. Cardiovascular magnetic resonance artefacts.Journal of Cardiovascular Magnetic Resonance 2013
25 / 32
4. Shared hypothesis testing
Some diseases may be evidenced across multiple biologicalscales (e.g., cellular, molecular, organic, behavioral)
Tests (e.g., cell viability, MRI analysis, gait analysis) mayevidence a factor
Factors are linked among each other via a causal relationship
A set of factors and the causality relationship constitute ahypothesis of the progression of the disease
Different specialists may work on different subparts of thehypothesisSpecialists may rely on different domain ontologies and usedifferent modelling/naming conventions
Use ontology alignment techniques
Asan Agibetov, E. Jimenez-Ruiz et al. Towards Shared HypothesisTesting in the Biomedical Domain. Semantic Web Applications andTools for Life Sciences (SWAT4LS). 2015
EU FP7 MultiScaleHuman project
26 / 32
4. Shared hypothesis testing
Some diseases may be evidenced across multiple biologicalscales (e.g., cellular, molecular, organic, behavioral)
Tests (e.g., cell viability, MRI analysis, gait analysis) mayevidence a factor
Factors are linked among each other via a causal relationship
A set of factors and the causality relationship constitute ahypothesis of the progression of the disease
Different specialists may work on different subparts of thehypothesisSpecialists may rely on different domain ontologies and usedifferent modelling/naming conventions
Use ontology alignment techniques
Asan Agibetov, E. Jimenez-Ruiz et al. Towards Shared HypothesisTesting in the Biomedical Domain. Semantic Web Applications andTools for Life Sciences (SWAT4LS). 2015
EU FP7 MultiScaleHuman project
26 / 32
5. Pistoia alliance ontologies mapping project
Currently looking for suitable tool supportDisease and phenotype domain:
Human Phenotype Ontology (HPO)←→ MammalianPhenotype Ontology (MP)Human Disease Ontology (DOID)←→ Orphanet and RareDiseases Ontology (ORDO)
Motivation: matching human inherited diseases withlaboratory studiesThe Pistoia Alliance Ontologies Mapping project co-organisesthe OAEI phenotype track
Pistoia Alliance Ontologies Mapping project: http:
//www.pistoiaalliance.org/projects/ontologies-mapping/
27 / 32
6. Semantic annotation of clinical letters (i)
The output of a visit is known as clinical letter, which servesto
document the patient’s progresscommunicate findings among specialists
Clinical letters are a key source of knowledge (specially forrare diseases)
The letter is typically tagged manually by a specialistresponsible for reading and annotating the interesting terms.
Common practice in the NHS: Genomics England, OCMR
28 / 32
6. Semantic annotation of clinical letters (i)
The output of a visit is known as clinical letter, which servesto
document the patient’s progresscommunicate findings among specialists
Clinical letters are a key source of knowledge (specially forrare diseases)
The letter is typically tagged manually by a specialistresponsible for reading and annotating the interesting terms.
Common practice in the NHS: Genomics England, OCMR
28 / 32
6. Semantic annotation of clinical letters (ii)
Manual annotation of clinical lettersRequires exhaustive input from a qualified medical professionalTime consumingLeads to the relatively limited use of clinical lettersLoss of potentially important informationSuitable software support is required to assist the clinician
S. Kudama et al. Towards Enabling the Semantic Access ofPhenotypic Information in Clinical Letters. Semantic WebApplications and Tools for Life Sciences (SWAT4LS). 2015
29 / 32
6. Semantic annotation of clinical letters (ii)
Manual annotation of clinical lettersRequires exhaustive input from a qualified medical professionalTime consumingLeads to the relatively limited use of clinical lettersLoss of potentially important informationSuitable software support is required to assist the clinician
S. Kudama et al. Towards Enabling the Semantic Access ofPhenotypic Information in Clinical Letters. Semantic WebApplications and Tools for Life Sciences (SWAT4LS). 2015
29 / 32
6. Semantic annotation of clinical letters (ii)
Manual annotation of clinical lettersRequires exhaustive input from a qualified medical professionalTime consumingLeads to the relatively limited use of clinical lettersLoss of potentially important informationSuitable software support is required to assist the clinician
S. Kudama et al. Towards Enabling the Semantic Access ofPhenotypic Information in Clinical Letters. Semantic WebApplications and Tools for Life Sciences (SWAT4LS). 2015
29 / 32
6. Semantic annotation of clinical letters (iii)
Special interest on HPO terms, however. . .
Text annotators may rely on different vocabulariesSome domains may require specific vocabulary as well (headinjuries vocabulary)UMLS vocabulary is richer than HPO vocabulary
Coordinated vocabularies:Exploit (and validate) HPO cross-references to UMLSExploit references of HPO to other (BioPortal) ontologiesUse ontology alignment techniques
30 / 32
6. Semantic annotation of clinical letters (iii)
Special interest on HPO terms, however. . .
Text annotators may rely on different vocabulariesSome domains may require specific vocabulary as well (headinjuries vocabulary)UMLS vocabulary is richer than HPO vocabulary
Coordinated vocabularies:Exploit (and validate) HPO cross-references to UMLSExploit references of HPO to other (BioPortal) ontologiesUse ontology alignment techniques
30 / 32
6. Semantic annotation of clinical letters (iv)
Coordinated vocabularies:
31 / 32
Questions?
Thank you for your attention
Acknowledgements:
EU project Optique and the EPSRC project DBOnto.
32 / 32