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The internet has become ubiquitous and indispensable

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. The internet has become ubiquitous and indispensable. Webpages. Companies. Emails. Places. Products.

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The internet has become ubiquitous and indispensable

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  1. Semantics as a means for efficient information integrationDr. Stijn Verstichel1st International Summer School on eCare - Integrated, sustainable home careAugust, 25th 2014

  2. The internet has become ubiquitous and indispensable

  3. Webpages Companies Emails Places Products Interests Services Persons Activities Documents Projects Events Multimedia Content A large tangle of information

  4. The knowledge is in the connections Web 3.0 The Semantic Web 2010 - 2020 RelationshipsbetweenInformation The Social Web Web 2.0 2005 - 2010 The Web Web 1.0 1990 - 2005 The PC PC The Internet 1980 - 1990 RelationshipsbetweenPersons

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

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

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

  8. WhySemantics? Capturing the meaning of the information exchanged

  9. Next generation Nurse Call System 1 ward 1 ward Call Person OrientedDynamic and Context Aware Location OrientedStatic 9

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

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

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

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

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

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

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

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

  19. Dynamic Nurse Call System 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 Nurse Call Component 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 Does Bart speak French? Is Bart a healthy Person? Yes Perhaps No Yes No

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

  22. Which typical layers are there? SHARED ONTOLOGY MODEL • Ontology Modelling Tool • Protégé, Swoop, Top Braid Composer, …

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

  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.

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

  26. Care specific challenges Context Aware Profile Deriving high-level knowledge from raw meaningless data 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. EarlyFusion vs. Late Fusion ReasoningApplication + Ontology 3 ReasoningApplication + Ontology 2 ReasoningApplication + Ontology 1 DistributedReasoningCoordinator 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’CareCloudS Development of a “cloud-based” ICT-platformfor the support of care giving and care organisation Creation of addedvaluebasedoninformation in existingsystems Collaborationbetweenformal and informal care givers Management of all information, e.g. personal care file. Offering of suggestions and services in the care network Creation of knowledge Management of trust relations

  31. O'CareCloudSOrganizing Home Care Using a Cloud-based Platform • TV • Registration • Task management • Trends • Sensor analysis

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

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

  34. 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

  35. Next Wednesday – Exercise Session Department of Information Technology – Internet Based Communication Networks and Services (IBCN)

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

  37. HomeLab Demo "D:\SVNDocuments\Research\eCare\SummerSchool\A2 posters_v1.0.pdf"

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