The document discusses developing a Personalized Health Knowledge Graph (PHKG) to support personalized preventative healthcare applications. PHKG integrates medical knowledge and personal health data to provide context-specific and personalized insights. It proposes an architecture with a knowledge graph, rule-based inference engine, and integration of knowledge from ontology catalogs. Challenges include modeling personalization/context, analyzing IoT data, and reusing knowledge from existing health resources. The solution is demonstrated for asthma management using the KHealth dataset and ontologies. Future work includes additional disease cases and dynamic knowledge graph evolution.

1. Personalized Health Knowledge Graph1
Ohio Center of Excellence in Knowledge-Enabled Computing
Amelie Gyrard, Manas Gaur,
Krishnaprasad Thirunarayan, Amit Sheth
Kno.e.sis Research Center
Department of Computer Science and Engineering,
Wright State University, Dayton, Ohio (USA)
Saeedeh Shekarpour
University of Dayton, Ohio

2. Problem
• World Health Organization (WHO) estimates that the number of
people suffering from asthma, obesity and Parkinson’s disease are
respectively 235 million, 650 million, 4-6 million.
=> Need of “Personalized Preventive Coach Applications”
to support physicians in understanding asthma symptoms
2

3. Introduction
DIKW pyramid: Data >
Information > Knowledge >
Wisdom/Actionable Insight
3Sheth, A., Anantharam, P., Henson, C.: Physical-cyber-social computing: An early 21st century approach. IEEE Intelligent
Systems (2013), Slides: Semantic Web: introduction & overview [Sheth 2012]

4. Agenda
• The needs of Digital Health application
̶ Reasoner to derive meaningful information from health and IoT datasets
̶ Contextualization & Personalization
• Limitations of the Related Work
• Research Challenges
• kHealth Project
̶ Use case for Asthma, Obesity and Parkinson diseases
• Our solution: Personalized Healthcare Knowledge Graph
̶ Architecture
̶ Rule-based inference engine
̶ KAO ontology: Integration of knowledge from Ontology Catalogs
• Conclusion and Future Work
4

5. Definitions
• Context-Awareness refers to the use of external
data that can impact the user's situation.
̶ E.g., for Asthma: air pollution, pollen level
• Personalization adjusts the treatment to each
patient's condition.
̶ E.g., for Asthma patient: patient is allergic to a specific
type of pollen
5

6. Literature Survey: Limitations
6
Authors Publication
date
Topic KG ML Health KG
Paulheim et al. 2016 Survey KG Yes No No
Guha et al.
Schema.org
2016 KG Yes No No
Shi et al. 2017 Health KG Yes Yes Yes
Yu et al. 2017 KG TCM
Visualization
Yes No Yes
Ruan et al. 2017 KG TCM Yes No Yes
Weng et al. 2017 KG TCM Yes No Yes
Nickel al. 2016 KG + ML Yes Yes No
Wilcke et al. 2017 KG + ML Yes Yes No
Rotmensch et al. 2017 Health KG + EMR Yes Yes Yes
Dong et al. 2014 Knowledge Vault - Google KG Yes No No
No personalization
No IoT datasets exploited to
understand the surrounding
environment of the patient

7. KHealth Asthma:
kit + cloud-based analysis + visual analytics dashboard
7Source: Augmented Personalized Health - URL (slide 21)
Data Sources
Heterogeneous data and
collection method
Smart Data
Actionable meaningful information
from the data collected
Active and
passive sensing

8. KHealth Dataset
•
8Source: Augmented Personalized Health - 23 parameters - URL (slide 25)

9. Research Challenges (RC)
• (RC1) How to model a knowledge graph for healthcare and
chronic disease management?
• (RC2) How to model personalization and context-awareness to represent
patient's symptoms and derive actionable insights?
• (RC3) How to analyze datasets generated by IoT devices to deduce
information that is actionable?
• (RC4) How to promote reproduceable experiments from previous projects
(e.g., datasets, data models, and reasoning mechanisms)?
• (RC5) How to customize and instantiate relevant knowledge from existing
publicly available health knowledge bases to obtain insights from
health-related social media text?
9
RC
RC 1
RC 2
RC 3
RC 4
RC 5

10. Our Solution: Personalized Healthcare Knowledge Graph
Personalized Healthcare Knowledge Graph (PHKG) is a
solution which explicitly describes and integrates both
medical knowledge and personal health data, and can serve
as a foundation for (deductive and abductive) reasoning.
10

11. PHKG Architecture
•
11

12. Reusing domain knowledge from Ontology Catalogs
• BioPortal for biomedical ontologies [1]
• Linked Open Vocabularies (LOV) [2]
• Linked Open Vocabularies for Internet of Things (LOV4IoT) [3]
[1] https://bioportal.bioontology.org/, [2] https://lov.linkeddata.es/dataset/lov/, [3] http://lov4iot.appspot.com/
RC 1, 2, 4

13. Symptom Ontology
http://bioportal.bioontology.org/ontologies/SYMP/

14. SNOMED-CT
• Knowledge is already
encoded!
• Example: Cough and
Pollen concepts have rich
taxonomies
Pollen: http://purl.bioontology.org/ontology/SNOMEDCT/256259004
Cough: http://purl.bioontology.org/ontology/SNOMEDCT/49727002

15. Kno.e.sis Alchemy API
•
15
RC 2, 5
http://wiki.knoesis.org/index.php/Knoesis_Alchemy_of_Healthcare

16. KAO Ontology
• KAO: KHealth Asthma Ontology [1]
• Integration of several ontologies relevant to interpret kHealth datasets
̶ W3C SOSA/SSN
̶ Asthma from BioPortal
̶ Weather and smart home ontologies [Staroch 2013] [Kofler et al. 2011]
• More and more ontologies to integrate:
̶ SNOMED-CT from BioPortal (e.g., Cough, Pollen concepts)
̶ Symptoms Ontology
̶ Etc.
[1] http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

17. KAO Ontology Evaluation
• How to evaluate an ontology?
̶ So many tools to evaluate ontologies
̶ PerfectO [1] a platform to assist ontology developers in evaluating and improving
ontologies
• Set of ontology best practices executed with kao [2]
[1] http://perfectsemanticweb.appspot.com/?p=ontologyValidation
[2] http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

18. KAO Ontology Quality with PerfectO
•
http://perfectsemanticweb.appspot.com/documentation/SemanticWebBestPracticesForDummies.pdf
http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

19. KAO Ontology Quality with PerfectO
•
http://perfectsemanticweb.appspot.com/documentation/SemanticWebBestPracticesForDummies.pdf
http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

20. KAO Ontology Quality with PerfectO
•
http://perfectsemanticweb.appspot.com/documentation/SemanticWebBestPracticesForDummies.pdf
http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

21. KAO Ontology Quality with PerfectO
•
http://perfectsemanticweb.appspot.com/documentation/SemanticWebBestPracticesForDummies.pdf
http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

22. KAO Ontology Quality with PerfectO
•
http://perfectsemanticweb.appspot.com/documentation/SemanticWebBestPracticesForDummies.pdf
http://wiki.knoesis.org/index.php/KHealthAsthmaOntology

23. KHealth Reasoner
23
Raw
kHealth
Dataset
kHealth
Reasoner
kHealth ontologies
Semantic Dataset
(RDF Triple Store)
Example:
pollen=11
rdf:type AO:Pollen
rdf:type kao:HighPollenLevel
3) Update the triple store
with more contextually
relevant triples
2) Executing the
reasoner1) Semantic
Annotation
Example:
Asthma dataset
pollen=11
Example: knoesis Asthma Ontology (kAO)
Example:
rdf:type kao:HighPollenLevel
RC 2, 3

24. KHealth Reasoner
24
RC 2, 3

25. Rule Example for Air Quality
25
• Rule implementation compliant with:
̶ Jena inference engine
̶ Ontologies: kao, W3C SOSA/SSN, etc
• Code example:
<= Usage of W3C SOSA/SSN ontology
<= Usage of kao ontology

26. Rule Example for Pollen Level
26
=>Change the thresholds with other parameters to
achieve the personalization

27. Rule Example for Heart Beat
27
=>?measurement = heart beat patient data
Personalization

28. Demo: Health Reasoner
28
=> 6 scenarios supported by the health reasoner (pollen
data, air quality, outside humidity, heart beat, room
temperature and peak expiratory flow).
http://linkedopenreasoning.appspot.com/

29. Demo: Health Reasoner
29
=> Enriched information
=> Automatic
Semantic
Annotation
http://linkedopenreasoning.appspot.com/

30. Completeness and Correctness of the KG
30
http://sensormeasurement.appspot.com/documentation/NomenclatureSensorData.pdf
● Correctness means that are no incompatibility with other rules
● Completeness means that all sensor values are covered by an high level
information

31. Overall Challenge Research Impact
31
Knowledge
Extraction
Internet of
Things
Natural
Language
Processing
Web of
Things
Medical
Text
Analysis
Semantic
Web
Knowledge
Graphs

32. Conclusion & Future Work
● Context-specific and personalized knowledge
● Reasoning mechanisms to deduce high-level abstractions from data
and replace domain experts
● Challenge of integrating heterogeneous data from:
○ Healthcare, IoT data, Web of Data
• Future work:
̶ More disease use cases (e.g., asthma, obesity, Parkinson)
̶ Dynamic PKG (continuous evolution based on incoming streams of
observations)
32

33. Acknowledgments
● This work is partially funded by:
○ Hazards SEES NSF Award EAR 1520870
○ KHealth NIH 1 R01 HD087132-01.
● Thanks to the kHealth team for fruitful discussions and feedback.
● The opinions expressed are those of the authors and do not reflect those
of the sponsors.
33

34. Questions
34

35. Personalized Health Knowledge Graph
Ohio Center of Excellence in Knowledge-Enabled Computing
Amelie Gyrard, Manas Gaur,
Krishnaprasad Thirunarayan, Amit Sheth
Kno.e.sis Research Center
Department of Computer Science and Engineering,
Wright State University, Dayton, Ohio (USA)
Saeedeh Shekarpour
University of Dayton, Ohio

36. Annexe: Modeling the Patient
36
● Modeling the Person/Patient
● Body Mass Index Scenario: weight, height, age
● Challenges:
○ Property vs. class
○ Incomplete information

37. Annexe: Modeling the Weight - BioPortal
37
• Results from BioPortal when looking
for weight property

38. Annexe: Modeling the Weight - LOV
38
• Query the LOV ontology catalog to find a weight property to describe
the patient, sometimes concepts and not properties, decide among
148 results

39. Annexe: Modeling the Weight - Schema.org
39
• Schema.org provides the Person concept, and
its properties such as height, weight, but not
the property age that would we need for
computer BMI and since we do not have the
birthdate for privacy reasons.
• https://schema.org/weight

40. Annexe: Modeling the Weight - DBpedia
40
• Weight description from DBpedia, notice that
range must be in kilogram
• http://dbpedia.org/ontology/Person/weight