Presentation by Christophe Debruyne to the 6th International Workshop on Knowledge representation for Health Care (KR4HC 2014), Vienna, Austria. July 21, 2014. Authors: Oya Beyan, Ciara Breathnach, Sandra Collins, Christophe Debruyne, Stefan Decker, Dolores Grant, Rebecca Grant, and Brian Gurrin. Presentation also available at http://www.slideshare.net/dri_ireland/linked-vitalregistrationdatalongitudinalhealthhistories Paper available at: https://www.researchgate.net/publication/264200941_Towards_Linked_Vital_Registration_Data_for_Reconstituting_Families_and_Creating_Longitudinal_Health_Histories?ev=prf_pub ABSTRACT: The Irish Record Linkage 1864-1913 project aims to create a knowledge base containing historical birth-, marriage-and death records encoded into RDF to reconstitute families and create longitudinal health histories. The goal is to interlink the different persons across these records as well as with supplementary datasets that provide additional context. With the help of knowledge engineers who will create the ontologies and set up the platform and the digital archivist who will curate, ingest and maintain the RDF, the historians will be able to analyse reconstructed "virtual" families of Dublin in the 19th and early 20th centuries, allowing them to address questions about the accuracy of officially reported maternal mortality and infant mortality rates. In the longer term, this plat-form will allow researchers to investigate how official historical datasets can contribute to modern-day epidemiological planning.

1. Towards Linked Vital Registration Data for
Reconstituting Families and Creating
Longitudinal Health HistoriesLongitudinal Health Histories
Oya Beyan, Ciara Breathnach, Sandra Collins,
Christophe Debruyne, Stefan Decker, Dolores Grant,
Rebecca Grant, and Brian Gurrin
21st of July 2014 – KR4HC Workshop – Vienna, Austria21st of July 2014 – KR4HC Workshop – Vienna, Austria

2. Irish Record Linkage, 1864-1913
• Developing a platform applying semantic
technologies to historical birth-, death andtechnologies to historical birth-, death and
marriage certificates.
• Answering questions such as: “How accurate are
historic maternal mortality rates (MMR) and
infant mortality rates (IMR) for Dublin?”
• Team consists of researchers (historians), digital
archivists, and knowledge engineers.
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3. Data: General Office Records
• Vital registration data
– Birth-certificates– Birth-certificates
– Death-certificates
– Marriage records
• Digitised TIFF images of
hardcopy indexes and
registers.
• 2 TB of data• 2 TB of data
• Database describing the
digitised records allowing
searches on some fields.
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©General Records Office of Ireland 2014

4. Challenges
• Certified causes of death that can be attributed to maternal
death
– Within 42 days after labour – before (1864) it was 12– Within 42 days after labour – before (1864) it was 12
– Septicemia (blood poisoning), Fever, …
– “Corresponding” birth certificate?
• Death certificates with no corresponding birth certificate
• “Gaps” in sibship interval, even though no birth- or death
certificates can be found.
• The terminology used pre-1900. E.g., “debile” to denote• The terminology used pre-1900. E.g., “debile” to denote
weak or a failure to thrive.
• Capturing the socio-economical status of the families via,
for instance, the professions, ranks of fathers.
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5. Conceptual Architecture
Digital Archivist
SPARQL endpoint /
Linked Data Server
Updates
GRO records
as RDF
LinksLinker UpdaterRepository
Triple-
store
Linked Data Server
Analytics
Researcher
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DATA ANALYTICSPRESERVATION
Links to external datasets: e.g., Logainm – a database of Irish historical and
contemporary place names to provide additional context.

6. Development of 2 ontologies
Triplestore 2 Data Analysis
CONCERNSSEPARATIONOFCONCERNS
Obviously, due to
the sensitive
nature of the
data, data
protection is key.
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GRO Triplestore
Transformation from one model to another
• SPIN – SPARQL Inference
• SWRL / RuleML
• SPARQL Construct
• …
SEPARATION
protection is key.

7. Development of 2 ontologies
• 2 ontologies were developed – separation of concerns
• First ontology for describing the contents of records
– OWL 2 shallow, “flat ontology”
• Second ontology for data analysis
– OWL 2 + rules
– Rules to capture background and domain knowledge– Rules to capture background and domain knowledge
– Developed by having the historians formulate competency
questions (Grüninger and Fox)
– Captured graphically using Object Role Modelling
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8. Graphical Representation in ORM
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9. ### Prefixes ommitted …
irl:Record a owl:Class ;
rdfs:label "Record" ; .
irl:Certificate a owl:Class ;
rdfs:label "Certificate" ;
rdfs:subClassOf irl:Record; .rdfs:subClassOf irl:Record; .
irl:BirthRecord a owl:Class ;
rdfs:label "Birth Record" ;
rdfs:subClassOf irl:Certificate ; .
irl:DeathRecord a owl:Class ;
rdfs:label "Death Record" ;
rdfs:subClassOf irl:Certificate ; .
irl:MarriageRecord a owl:Class ;
rdfs:label "Marriage Record" ;rdfs:label "Marriage Record" ;
rdfs:subClassOf irl:Record ; .
irl:Return a owl:Class ;
rdfs:label "Return" ; .
…
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10. Conclusions
• Presented the problem and highlighted the
challengeschallenges
• Developed two ontologies
– Encoding contents of digitized GRO records for
long-term digital preservation DRI
– Data analytics to answer the researchers’
question – in this case a historianquestion – in this case a historian
• Data exploration and annotation of the
records started on a subset of the dataset
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