1. Semantics as a means for efficient information integration Dr. Stijn Verstichel 1st International Summer School on eCare - Integrated, sustainable home care August, 25 th 2014

2. The internet has become ubiquitous and indispensable 2

3. A large tangle of information 3 Webpages Content

4. The knowledge is in the connections 4 Relationships between Information Web 1.0 1990 - 2005 PC 1980 - 1990 2010 - 2020 Web 3.0 2005 - 2010 Web 2.0 The Web The PC The Semantic Web The Internet The Social Web Relationships between Persons

5. The knowledge is in the connections 5 Relationships between Information Web 1.0 1990 - 2005 PC 1980 - 1990 2010 - 2020 Web 3.0 2005 - 2010 Web 2.0 The Web The PC The Semantic Web The Internet The Social Web Relationships between Persons The management of this large amount of information is becoming less efficient

6. Not only the Web is struggling with the problem of an efficient information flow 6

7. A solution: Semantics 7 Syntax: The representation Semantics: The meaning

8. Why Semantics? 8 Capturing the meaning of the information exchanged

9. Next generation Nurse Call System Location Oriented Static Person Oriented Dynamic and Context Aware 9 1 ward Call

10. The ontology cloud Semantic Web Ontology OWL Formal Logics Reasoning RDF SWRL SPARQL 10

11. Ontology – OWL “An ontology is a specification of a conceptualization in the context of knowledge description” Person Disease has_pathology * is_a Neurologic Disorder Age Healthy Person - Person; AND - Not (has_pathology some Disease) 11

12. General Sensor Task Context Medical Profile Role & Competence Generic Core ontologies Cure Task Care Task Cure Role Role Cure Competence Care Competence Cure Doc Care Doc Care Profile Cure Context Cure ontologies Care ontologies Using ontology to support continuous care?

13. Nurse Call Component Localisation Component Domotics Component Using ontology to support continuous care? 13

14. Principles/scope/functionality Ontology =  Not only: Vocabulary/Glossary  But also: Topology, relations between entities  And even:Machine readable, to be used in software and in SOA-environments 14

15. “Layered cake” of the Semantic Web OWL SWRL & SPARQL Reasoning Data triples 15

16. Subtle but Important difference!! ONTOLOGY ≠ DATA-MODEL ONTOLOGY = DOMAIN-MODEL 16

17. Semantics of the data, but what is context? 17

18. Open Linked Data 18 http://linkeddata.org/

19. Nurse Call Component 1. Determine whether a call should be launched 2. Determine the priority of the call 3. Determine to whom the call is to be assigned 4. Determine to whom the appeal should be referred Dynamic Nurse Call System IF patient temperature > 38˚C THEN patient has a fever IF patient has a fever THEN launch call

20. Closed World vs Open World Assumption 20 Does Bart speak French? Is Bart a healthy Person? Yes No Yes Perhaps No

21. Benefits of ontologies 21 LOGIC OntologyRules Application +/- STATIC DYNAMIC REUSE

22. Which typical layers are there? Data Sourc es Legacy Ontology A-Box …. Persistency Relational DB Files Triple Store Reasoning Pellet Hermit … None Rules Jess SWRL … None Appl’on Support Jena OWLAPI Redland … SHARED ONTOLOGY MODEL 22 Ontology Modelling Tool Protégé, Swoop, Top Braid Composer, …

23. Data Integration Problem  Structural heterogeneity  Syntax heterogeneity  Implementation heterogeneity  Semantic heterogeneity  Synonyms  Homonyms  Classification  Structural heterogeneity  Syntax heterogeneity  Implementation heterogeneity  Semantic heterogeneity  Synonyms  Homonyms  Classification Data center 1Data center 2 23

24. Ontology engineering 1. Specification: the purpose and scope of the ontology are recorded. 2. Conceptualization: a conceptual model of the ontology is created. It consists of the different concepts, the relationships and properties that may occur in the domain. 3. Formalization: the conceptual model is translated into a formal model, e.g. for adding axioms that restrict certain interpretations of the model.. 4. Implementation: the formal model is implemented in a particular knowledge representation language, e.g. OWL. 5. Maintenance: the implemented ontology must be constantly evaluated, adjusted and improved. To adapt the ontology, the previous steps can be reused. 24

25. Technical challenges Context Aware Performant Scalable Modular Deriving high-level knowledge from raw meaningless data Large amount of sensor data (frequency 1/s – 1/day) Merge heterogeneous data 25

26. Care specific challenges Context Deriving high-level knowledge from raw meaningless data Profile Aware The right information to the right person at the right time Rapid interventions and alerting Improved communication between patient and (in) formal caregiver Improved monitoring 26

27. WiLAB.t Monitoring Bureau Testlab 27

28. Early Fusion vs. Late Fusion Reasoning Application + Ontology 1 Reasoning Application + Ontology 2 Reasoning Application + Ontology 3 Distributed Reasoning Coordinator 28

29. Very actual and relevant problem  Overburdening the informal caregiver  Need for integrated information  Informal care in the news of June 3rd 2014 (1:26-1:40)

30. O’Care Cloud S Development of a “cloud-based” ICT-platform for the support of care giving and care organisation Collaboration between formal and informal care givers Creation of added value based on information in existing systems Management of all information, e.g. personal care file. Creation of knowledge Offering of suggestions and services in the care network Management of trust relations

32. O'CareCloudS Organizing Home Care Using a Cloud-based Platform  TV TV  Registration Registration  Task management Task management  Trends Trends  Sensor analysis Sensor analysis

33. Data driven approach Care Data MCD MCI MCC MCK Raw data obtained from sensors and other devices Data linked to the information of the patient, time, space, etc. MCD tagged with MCC New knowledge is generated from the combination and / or the processing of MCI and MCK Reasoning Taggen Linken Corresponds to a concept in the ontology

34. Overview MASSIF Semantic communication bus Gateway JSON, tagged Mapping Service JSON, tagged Context Adapter A Context Adapter B … JSON, tagged OWL Data Service AService B…Service Z OWL Data Back-up Restore Journalling Visualisation External services (e.g. Vitalink)

35. A silver bullet? Certainly NOT!  Hybrid approach is NECESSARY  A large toolkit is available  Use the toolkit to the needs of the problem  Situationalise your approach  THE RIGHT TOOL FOR THE CORRECT PROBLEM  Hybrid approach is NECESSARY  A large toolkit is available  Use the toolkit to the needs of the problem  Situationalise your approach  THE RIGHT TOOL FOR THE CORRECT PROBLEM 35

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37. Faculty of Engineering and Architecture Department of Information Technology – Internet Based Communication Networks and Services (IBCN) Next Wednesday – Exercise Session

38. Department of Information Technology – Internet Based Communication Networks and Services (IBCN) 38

39. HomeLab Demo "D:\SVNDocum ents\Research\ eCare\Summer School\A2 posters_v1.0.p df"