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CHEAR (Child Health Environmental Assessment Resource) Ontology

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Presentation on theme: "CHEAR (Child Health Environmental Assessment Resource) Ontology"— Presentation transcript:

1 CHEAR (Child Health Environmental Assessment Resource) Ontology
Team Members: Yue Liu (Robin) Zhicheng Liang (Jason) Anirudh Prabhu John Sheehan Week 11 - Progress Report

2 CHEAR Ontology Structure
Resource for new study questionnaire Compare different study questionnaire Body Measure & Calculation Asthma Symptom Monitoring Study Questionnaire Encoding Subjects and Topics Combing the 2 - enrich for new study builders as resources Compare the 2 different study questionaire Next step:

3 SPARQL Query Demos

4 Example #1 What Body measures are relevant to the diagnosis of asthma?
Answer : Waist circumference, fat free mass, height, weight, and bioimpedance. PREFIX chear:< PREFIX chear_individuals:< PREFIX skos: < PREFIX rdfs: < PREFIX rdf: < select distinct ?s0 ?s2 ?s3 where { {?s0 chear:hasPositiveRelation chear_individuals:Asthma . ?s0 rdf:type ?p1 . ?p1 rdfs:subClassOf chear:AnthropometryMeasure .} UNION {?s chear:hasPositiveRelation chear_individuals:Asthma . ?s chear:calculatedFrom ?s1 . ?s1 chear:calculatedFrom ?s2 . ?s2 chear:calculatedFrom ?s3 . ?s3 rdf:type ?p2 . ?p2 rdfs:subClassOf chear:BodyMeasure . }UNION {?s chear:hasPositiveRelation chear_individuals:Asthma . ?s2 rdf:type ?p3 . ?p3 rdfs:subClassOf chear:AnthropometryMeasure . }

5 How the Ontology is used
The Ontology contains a detailed description of how the body measure are related to each other and what can be derived from each body measure. Also, every concept that has a positive relation to a specific disorder like asthma will be mapped together. So for the above example, the initial data retrieved would be the concept positively influence asthma diagnosis. These include Percent Body Fat, BMI Percentile, Waist to Height Ratio and Waist Circumference. Since Waist Circumference is already a body measure it can be directly used for the answer. While the other 3 concepts are just calculations that have been derived from other body measures, we consider the body measures they have been derived from as the answer for the question asked.

6 Example #2 Along with the characteristics used to diagnose Asthma in the “Measures of Obesity Associated with Asthma Diagnosis in Ethnic Minority Children” Study, what other characteristic could be added to get better/different results? PREFIX chear:< PREFIX chear_individuals:< PREFIX skos: < PREFIX rdfs: < PREFIX rdf: < SELECT distinct ?c1 WHERE { chear_individuals:StressWheezeAssociationStudy chear:hasResource ?obj1. ?obj1 chear:hasSubject ?obj. FILTER NOT EXISTS { chear_individuals:ObesityAsthmaAssociationStudy chear:hasResource ?obj2. ?obj2 chear:hasSubject ?obj.} ?obj rdf:type ?c2 . ?c2 skos:prefLabel ?c1 . }

7 How the Ontology is used
Answer : Questions about House, Postnatal Environmental Tobacco Smoke Exposure, Dry Cough and Education Level could be considered in the study to find out if the they have any relationship with the Asthma being diagnosed in Kids from New York. How the Ontology is used : The Subject for each study is checked (based on the hasSubject property that maps questions to the respective subject), so we can find the subjects each study has taken into account for example Age, SocioEconomic Status , Race and Gender, and so find that the other study has used an additional characteristic being Prenatal and Postnatal Stress Scores.

8 Example #3 Are there any similar questions about night cough both asked in the obesity-asthma association study and the stress- wheeze association study? If yes, what are they? PREFIX rdfs: < PREFIX chear: < PREFIX chear_individuals: < ASK WHERE { chear_individuals:ObesityAsthmaAssociationStudy chear:hasResource ?q chear_individuals:StressWheezeAssociationStudy chear:hasResource ?q ?q1 chear:hasSubject chear_individuals:NightCough . ?q2 chear:hasSubject chear_individuals:NightCough . } PREFIX rdfs: < PREFIX chear: < PREFIX chear_individuals: < SELECT DISTINCT ?c1 ?c2 WHERE { chear_individuals:ObesityAsthmaAssociationStudy chear:hasResource ?q chear_individuals:StressWheezeAssociationStudy chear:hasResource ?q ?q1 chear:hasSubject chear_individuals:NightCough . ?q2 chear:hasSubject chear_individuals:NightCough . ?q1 rdfs:comment ?c ?q2 rdfs:comment ?c2 . }

9 Query Result How the Ontology is used
Example #3 Query Result c1 c2 "Does (CHILD’S NAME) ever cough at night when she does not have a "During the past 12 months did your child have dry cough during the night, besides any cough associated with colds or respiratory How the Ontology is used The question “Does (CHILD’S NAME) ever cough at night when she does not have a cold?”, owned by study instance “ObesityAsthmaAssociationStudy”, hasSubject “NightCough”. The question “During the past 12 months did your child have dry cough during the night, besides any cough associated with colds or respiratory infections?”, owned by study instance “StressWheezeAssociationStudy”, hasSubject “DryCough” and “NightCough”.

10 Example #4 In the questionnaires about asthma symptoms in “Measures of Obesity Associated with Asthma Diagnosis in Ethnic Minority Children” and “Prenatal and postnatal stress and wheeze in Mexican children Sex-specific differences”, what other terms are used to represent a respiratory problem such as “Wheezing”? PREFIX rdfs: < PREFIX rdf: < PREFIX chear: < SELECT ?subject ?predicate ?symptom WHERE { ?subject rdfs:subClassOf chear:RespiratoryProblem }

11 How the Ontology is used
Answer : Cough, Chest Tightening and Shortness of Breath are also used to indicate a respiratory problem in addition to Wheezing. How the Ontology is used : We use rdfs:subClassOf chear:RespiratoryProblem to show all terms representing respiratory problems across both questionnaires.

12 Example #5 In the questionnaires about smoking in “Measures of Obesity Associated with Asthma Diagnosis in Ethnic Minority Children” and “Prenatal and postnatal stress and wheeze in Mexican children Sex-specific differences”, what other terms are used to represent exposures such as “Second-hand smoke”? Answer : Passive Smoking, PreNatal and PostNatal ETS Exposure

13 Example #5 In the questionnaires about smoking in “Measures of Obesity Associated with Asthma Diagnosis in Ethnic Minority Children” and “Prenatal and postnatal stress and wheeze in Mexican children Sex-specific differences”, what other terms are used to represent exposures such as “Second-hand smoke”? Answer : Passive Smoking, PreNatal and PostNatal ETS Exposure How the Ontology is used : We use the rdfs:subClassOf chear:Exposure to find other terms which are used to represent second-hand smoke across both questionnaires.

14 Example #5 PREFIX rdfs: < PREFIX rdf: < PREFIX chear: < PREFIX rdf: < SELECT ?subject WHERE { ?subject rdfs:subClassOf chear:Exposure } Expected Answers: subject chear:PassiveSmoking chear:PostnatalETSExposure chear:PrenatalETSExposure

15 Merge Plan & Modeling Choices
Instead of using external ontology as a direct import in this round, based on our discussion with Prof. McGuinness. We would come up with a plan on transferring our concepts in Body Mass and Subjects/Topics into CHEAR properties as an enrichment/extension and eventually merge our ontology into the big CHEAR ontology. CHEAR Ontology:

16 Remaining Modeling Issue
Merge with the CHEAR Ontology Considerable changes in the ontology necessary Change the subject classes to object properties This helps reusability Also helps to add information about new studies

17 Results of Oops! Testing

18 Changes made Removed the redundant classes mentioned.
MedicalField Chemical Added domains and ranges for properties which were missing them. Added License to both the Base and Individuals ontology.

19 Results of Protege Ontology Evaluation
The results from ontology evaluation tab in Protege give us a series of statistic data about our ontology, such as naming conventions, class hierarchy, property hierarchy, property restrictions, similar concepts, and so on. From these data, we can better evaluate our ontology in details and find existing errors or problems.

20 Thank You.


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