Semantic application: how to integrate information

1. A semantic application
for Healthcare
Peter Scholten

2. How to build a semantic application
• What is the goal of a semantic application.
• Not only focused on known requirements,
but also anticipate on unknown…’future’
settings.

3. Goal of semantic application
• Social medium (twitter, hyves, facebook etc
Communication
• Discussion platform (Linkedin..)
Business oriented
• Information medium
Questions like….

4. Semantic web for Healthcare
What
where to find

5. Benefits of the semantic web
• Finding resources more quickly and easily
• Storing corporate knowledge
• To generate new knowledge
• Improve the Clinic’s ability to use patient data for
generating new knowledge to improve future patient care
through outcomes-based and longitudinal clinical research.
• Cross sectional data analysis

7. Problems on internet
• Format
• Language
– Homograph: group of words that share the same spelling but have
different meanings
– Homonym: group of words that share the same spelling or pronunciation
(or both) but have different meanings
– Synonym: different words with identical or at least similar meanings
– Polysemy: the capacity for a word to have multiple meanings

8. Need for new semantic
“functions” for
information and
knowledge processing

9. Example
 Internet is collection documents with data mostly
represented in tabular form with different formats
and dimension.
 How to integrate information

10. Health Care Civilians
How to define
 Relation care takers and care need
 Relation care takers and care need
depending living place
 Relation care takers and care need of
older people depending living place
Age
Living place
Age

11. Geographic distribution for care need

12. Geographic distribution for care need older then 65 years

13. Relation cardiologist and care takers older then 65 years

14. Relation family doctor and care takers region Brabant

15. • Find models on the web
RFD/XML files
• Direct access to selected documents
Special Google search
• Built a model from scratch
SQL versus relational database
Use of inferencing

16. • Find models on the web
RFD/XML files
• Direct access to selected documents
Special Google search
• Built a model from scratch
SQL versus relational database
Use of inferencing

17. Selected search internet: Demency

18. • Find models on the web
RFD/XML files
• Direct access to selected documents
Special Google search
• Built a model from scratch
SQL versus relational database
Use of inferencing

19. Inferencing
Semantic web !

20. Example inferencing
x
zy

22. An Ontology
• Defines
– a common vocabulary
– a shared understanding
– re-use of domain knowledge.
• Is an explicit description of a domain:
– Concepts (classes, subclasses and superclasses)
– properties and attributes of concepts
– constraints on properties and attributes
– Individuals (often, but not always)

23. joints
drugs

24. Health care informationmodel

25. Health care ontology
Metadata
(individuals)
Metadata
(individuals)
Metadata
(individuals)
Metadata
(individuals)
Metadata
(individuals)

26. Define Classes and the Class Hierarchy

27. Description of domain by RDF
RDF: Resource Description Framework
is a data model for representing metadata
(information about Resources = URI)
in the World Wide Web.

28. Protégé: an ontology editor
• RDF
• RDFS
• OWL
• SPARQL

30. A typical relational database table for books

31. The rows represent the things you are storing
information about

32. The columns represent the properties or
attributes of those things

33. the book has a title with value "Javascript"

34. the book has a title with value "Javascript"
subject has a property with object "value" (s,p,o)
This is the essence of RDF: the (s,p,o) triple
Any expression in RDF is a collection of triples

35. Relations Between Entities

36. RDF names things with URLs
Create different URLs to name different things

37. Any RDF can be merged with any other RDF

38. Storage of RDF’s in an XML document with the tag rdf:RDF
The content of an XML document is a number of descriptions, which use
rdf:Description tags.
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:mydomain="http://www.mydomain.org/my-rdf-ns">
<rdf:Description
rdf:about="http://www.cit.gu.edu.au/~db">
<mydomain:site-owner
rdf:resource=“#David Billington“/>
</rdf:Description>
</rdf:RDF>

39. rdfs
RDFS is a vocabulary description language, using
– Classes and Properties
– Class Hierarchies and Inheritance
– Property Hierarchies
OWL/OWL2:
A richer ontology language, disjointness, cardinality, characteristics of properties
(SymmetricProperty, TransitiveProperty, and inverseOf, FunctionalProperty,
InverseFunctional-Property, sameAs.)

41. Some RDFS inference rules
• (X R Y), (R subPropertyOf Q) (X Q Y)
• (X R Y), (R domain C) (X type C)
• (X type C), (C subClassOf D) (X type D)

42. (X type C), (C subClassOf D) (X type D)
Doctor
Surgeon Anaesthesist
rdfs: subClassOfrdfs: subClassOf
Rdf:type
If ?p rdf:type ?Surgeon
If ?Surgeon rdfs: subClassOf ? Doctor
Then ?p rdf:type ?Doctor

43. (X R Y), (R subPropertyOf Q) (X Q Y)
worksFor
freeLancesTo isEmployedBy
rdfs: subPropertyOfrdfs: subPropertyOf
?p
If ?p freeLancesTo ?Hospital
If freeLancesTo rdfs: subPropertyOf worksFor
Then ?p worksFor ?Hospital

44. domain range
If P(PROPERTY) rdfs: domain D and x P Y then x rdf: type D
If P(PROPERTY) rdfs: range R and x P Y then y rdf: type R
?Hospital hasSpecialism ?Physician
?Physician hasCompetences ?Competences

45. Terminology transfer
Physician Specialism
equivalent
? Physician rdfs: subClassOf ? Specialism

47. SPARQL

48. SPARQL (Query Language for RDF)
SELECT ?hospital ?Physician
WHERE { ?hospital rdf:value ?distance.
?physician category ?cardiologist.
FILTER (?distance<=40). }

50. Searching internet
Input: symptoms
Output: Url’s for description symptoms

51. Searching internet
Input: symptoms
Output: Url’s for description symptoms

52. Searching internet
Input: diseases or medicine
Output: Url’s for description medicine and diseases

53. Searching internet
Input: diseases or medicine
Output: Url’s for description medicine and diseases

54. Searching internet
Input: professional or institute
Output: address

55. Searching internet
Input: professional or institute
Output: address

56. Searching internet
Input: assistive device disabled persons
Output: description and Url’s of assistive devices

57. Searching internet
Input: assistive device disabled persons
Output: description and Url’s of assistive devices
What
Where to find
description
detailed

58. Searching internet
Input:
assistive need for older or disabled persons
• Aids for low-vision or blind persons
• Aids for motor disabilities
• Persons hard of hearing
• Demency
• COPD
• Chronic diseases
• Home care
• Emergency service
Output:
description and Url’s of assistive advice