A Justification-based Semantic Framework for Representing, Evaluating and Utilizing Terminology Mappings
1. A Justification-based Semantic
Framework for Representing, Evaluating
and Utilizing Terminology Mappings
EHR4CR, Open PHACTS, SALUS and W3C collaboration
Sajjad Hussain1, Hong Sun2, Gokce Banu Laleci Erturkmen3, Mustafa Yuksel3,
Charlie Mead4, Alasdair Gray5, Kerstin Forsberg6
19-Oct-2014
EHR4CR: 1INSERM UMRS 1142, Paris, France; AstraZeneca, 6R&D Information, Mölndal Sweden
Open PHACTS: 5School of Mathematical and Computer Sciences, Heriot-Watt University
SALUS: 3Software Research, Development and Consultancy, Ankara, Turkey,
2Advanced Clinical Applications Research Group, Agfa HealthCare, Gent, Belgium
W3C: 4Health Care and Life Sciences IG,
1
Workshop on Context, Interpretation and Meaning (CIM2014)
http://slideshare.net/kerfors/CIM2014
Version 1.0
2. Objective
• Enable a more collaborative semantic landscape
with providers and consumers of terminology
mappings.
– A framework built upon existing terminology
mappings to:
• Infer new mappings for different use cases.
• Present provenance of the together with the
justification for the mappings.
• Perform mapping validation in order to show that
inferred mappings can be erroneous.
• Discuss a Justification Vocabulary for
Mapping Terminology Concepts/Terms
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3. Semantic landscape 1(3)
Consumers and, somewhat
reluctant, creators of mappings
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4. Semantic landscape 2(3)
Consumers and, somewhat
reluctant, creators of mappings
Providers of terminology mappings,
some examples
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5. Semantic landscape 3(3)
5
Consumers and, somewhat
reluctant, creators of mappings
Providers of terminology mappings,
some examples
Providers of terminologies,
some examples
Workshop on Context, Interpretation and Meaning (CIM2014)
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6. Example Scenario
• Challenging nature of mapping utilization, or
“How hard can it be?”
– Appear to the uninitiated as a simple exercise like “this
term in this terminology is the same as that term in that
terminology”
6
9. Example Scenario 3(3)
9
matches
matches
matches
Defined
Mappings
Inferred
Mappings
matches
Problematic
Mappings
10. “It’s complicated”. So, we often become, somewhat
reluctant, creators of our own mappings
• Availability of up-to-date information to assess the suitability
of a given terminology for a particular use case.
• Difficulty of correctly using complex, rapidly evolving
terminologies.
• Differences in granularity between the source and target
terminologies.
• Lack of semantic mappings in order to completely and
unambiguously define computationally equivalent semantics.
• Lack of provenance information, i.e. how, when and for what
purposes the mappings were created.
• Time and effort required to complete and evaluate mappings.
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11. 11
Objective: A more collaborative
semantic landscape
Value adding providers
of terminologies
Workshop on Context, Interpretation and Meaning (CIM2014)
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Informed consumers
of terminology mappings
Value adding providers
of terminology mappings
12. Framework
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13. Mapping Strategies
• Lexical Mappings (LOOM) generated by performing lexical comparison between
preferred labels and alternative labels of terms. These mappings are represented via
skos:closeMatch property.
• Xref OBO Mappings Xref and Dbxref are properties used by ontology developers to
refer to an analogous term in another vocabulary. These mappings are represented
via skos:relatedMatch property.
• CUI Mappings from UMLS are extracted by utilizing the same Concept Unique
Identifier (CUI) annotation as join point of similar terms from different vocabularies.
These mappings are represented via skos:closeMatch property.
• URI-based Mappings are generated identity mappings between term concepts in
different ontologies that are represented by the same URI. These mappings are
represented via skos:exactMatch property.
Workshop on Context, Interpretation and Meaning (CIM2014) 13
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15. Collaborative semantic landscape
15
Informed consumers
of terminology mappings
Value adding providers
of terminology mappings
Value adding providers
of terminologies
Enabled by applications
of RDF
16. 16
Application of RDF to
represent mappings
Enabled by applications
of the RDF standard
17. 17
Application of RDF to
represent provenance
Enabled by applications
of RDF
18. Applications of RDF for packaging assertions
18
(e.g. mappings) with provenance
Enabled by applications
of RDF
19. 19
Applications of RDF for describing
datasets and linksets with justifications
Enabled by applications
of RDF
20. Example Scenario
20
matches
matches
matches
Defined
Mappings
Inferred
Mappings
matches
Problematic
Mappings
22. matches
Defined
Mappings
Inferred
Mappings
22
SKOS/RDF to represent the mappings
ICD9CM:999.4 skos:exactMatch SNOMEDCT:21332003
SNOMEDCT:21332003 skos:exactMatch MedDRA:10067113
ICD9CM:999.4 skos:exactMatch MedDRA:10067113
23. ICD9CM:999.4 skos:exactMatch MedDRA:10067113
matches
Defined
Mappings
Inferred
Mappings
Nanopublication/RDF for packaging
mappings together with their provenance and justification
23
Assertion
Justification trace generated from EYE reasoning engine
24. Justification Vocabulary for
Mapping Terminology Concepts/Terms
24
??
Workshop on Context, Interpretation and Meaning (CIM2014)
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25. 25
Applications of RDF for describing
datasets and linksets with justifications
Enabled by applications of
the RDF standard
Workshop on Context, Interpretation and Meaning (CIM2014)
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28. Linksets: Justification Vocabulary Terms 3(3)
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29. Acknowledgments
• Session chair
• CIM2014 organizers
• SALUS team: Hong Sun, Ali Anil Sinaci, Gokce Banu Laleci Erturkmen
– Support from the European Community’s Seventh Framework Programme
(FP7/2007–2013) under Grant Agreement No. ICT-287800, SALUS Project
(Scalable, Standard based Interoperability Framework for Sustainable
Proactive Post Market Safety Studies).
• EHR4CR team: WP4, WPG2, WP7 members
– Support from the Innovative Medicines Initiative Joint Undertaking under
grant agreement n° [No 115189]. European Union's Seventh Framework
Programme (FP7/2007-2013) and EFPIA companies
• Open PHACTS team: Alasdair Gray
• W3C HCLS team: Eric Prud’Hommeaux, Charlie Mead
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30. Reference material
• Projects/organisations of the authors of this paper
• Example
– Mapping Representation using SKOS
– Mapping Provenance Representation
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31. EHR4CR
Electronic Healthcare Record For Clinical Research
http://www.ehr4cr.eu/
• IMI (Innovative Medicine Initiative)
– Public-Private Partnership between EU and EFPIA
• ICT platform: using EHR data for supporting
clinical research
• Protocol feasibility
• Patient recruitment
• Clinical trial execution: Clinical Research Forms (eCRF)/
Individual Case Safety Reports (ICSR) prepopulation
• 33 European academic and industrial partners
– 11 pilot sites from 5 countries – 4 millions patients
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32. Open PHACTS
Open Pharmacology Space
http://www.openphacts.org/
• IMI (Innovative Medicine Initiative)
• 31 partners: 10 pharma – 21 academic / SME
• The Challenge - Open standards for
drug discovery data
– Develop robust standards for solid
integration between data sources via
semantic technologies
– Implement the standards in a semantic
integration hub (“Open Pharmacological Space”)
– Deliver services to support on-going drug
discovery programs in pharma and
public domain
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33. SALUS
Sustainable Proactive Post Market Safety Studies
http://www.salusproject.eu/
• European Commission (STREP)
• ICT platform : using EHRs data to improve post-market
safety activities on a proactive basis
• Semi-automatic notification of suspected adverse events
• Reporting adverse events (Individual Case Safety Reports
(ICSR) prepopulation)
• Post Marketing safety studies
• 8 European academic and industrial partners
– 2 pilot sites
• Lombardia Region (Italy) and Eastern Saxony (Germany)
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34. W3C
Semantic Web Health Care and Life Sciences Interest
Group (HCLS IG)
http://www.w3.org/2001/sw/
• ..
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Presentation Title: A Justification-based Semantic Framework for Representing, Evaluating and Utilizing Terminology Mappings
Timeslot 19- Tuesday, Sep. 2nd. 10:30 - 12:00 in room 04-Haskoy as a Full paper.
Track: Natural Language Processing
Session: Natural Language Processing 2
In this paper we show the challenging nature of mapping utilization among different terminologies.
The introduced framework has been built upon existing terminology mappings to
infer new mappings for different computable semantic interoperability use cases,
present provenance of the mappings together with the context information—an important problem for term mapping utilization, and
perform mapping validation in order to show that inferred mappings can be erroneous.
The framework enables a more collaborative semantic landscape with providers and consumers of terminology mappings.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
Using the nanopublication schema, the inferred mapping are represented as an RDF triple in the Assertion graph.
The provenance information related to the mapping assertions are recorded into two categories:
Attribution, where meta-data and context about the mapping can be represented;
Supporting, where the justification behind obtaining the recorded mapping assertion are represented. In this case, the Supporting graph includes a ‘meta-level’ justification trace generated from EYE reasoning engine.
Using the nanopublication schema, the inferred mapping are represented as an RDF triple in the Assertion graph.
The provenance information related to the mapping assertions are recorded into two categories:
Attribution, where meta-data and context about the mapping can be represented;
Supporting, where the justification behind obtaining the recorded mapping assertion are represented. In this case, the Supporting graph includes a ‘meta-level’ justification trace generated from EYE reasoning engine.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Largest public-private partnership to date with the goal to tie interoperability aspects
The IMI EHR4CR project will run over 4 years (2012-2015) and involve 33 European academic and industrial partners
Comprehensive business model for governance, acceptance, adoption and sustainability