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Context aware processing of ontologies in mobile environments

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Paper review of context aware processing of ontologies

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Context aware processing of ontologies in mobile environments

  1. 1. Context-Aware Processing of Ontologies in Mobile Environments By : Gunther Specht and Timo Weithoner University of Ulm 89069 Ulm, Germany
  2. 2. Agenda <ul><li>Ontology </li></ul><ul><li>MobileOntoDB project </li></ul><ul><li>Summary </li></ul>
  3. 3. Ontology <ul><li>An ontology is a controlled vocabulary of well defined terms </li></ul><ul><li>with specified relationships between them </li></ul><ul><li>capable of interpretation by both computers and humans </li></ul>
  4. 4. Reasoner <ul><li>a reasoner , is a piece of software able to infer logical consequences from a set of asserted facts or axiom </li></ul><ul><li>A logic allows the axiomatization of the domain information, and the drawing of conclusions from that information. </li></ul><ul><li>Syntax </li></ul><ul><li>Semantics </li></ul><ul><li>Logical inference = reasoning </li></ul>
  5. 5. MobileOntoDB <ul><li>Goal: develop a context-aware, database based ontology reasoner for mobile devices </li></ul><ul><li>Limitations: mobile resources are restricted </li></ul><ul><li>Approach: “meta mapping” </li></ul><ul><ul><li>Scale down the database reasoner to work on mobile devices </li></ul></ul><ul><ul><li>Add dynamic context awareness </li></ul></ul>
  6. 6. Motivation
  7. 7. Context Awareness <ul><li>Mobile devices know some context </li></ul><ul><ul><li>Location (GPS) </li></ul></ul><ul><ul><li>Time (Schedule) </li></ul></ul><ul><ul><li>People around (Bluetooth connection) </li></ul></ul><ul><ul><li>But our definition of context is broader </li></ul></ul><ul><ul><li>Absolute and relative location of the user </li></ul></ul><ul><ul><li>Time, date and even schedule of the user </li></ul></ul><ul><ul><li>Situation and current activity of the user </li></ul></ul><ul><ul><li>Availability of networks and network-services </li></ul></ul><ul><ul><li>Availability of persons (detected via Bluetooth) and resources </li></ul></ul><ul><ul><li>Further sensor data: weather, health conditions, etc. </li></ul></ul>
  8. 8. Representation of Context <ul><li>Extend the ontology language with special elements to declare context </li></ul><ul><ul><li>Drawback: extended reasoners would need to be developed </li></ul></ul><ul><li>Integrate context into ontologies using existing language elements </li></ul>
  9. 9. Database supported Reasoning <ul><li>Relational Reasoner based on 2 step mapping mechanism </li></ul><ul><ul><li>First step: convert an ontology into a logic program (OWL LP) </li></ul></ul><ul><ul><li>Second step: convert logic program into relational database like SQL (LP SQL) </li></ul></ul>
  10. 10. OWL LP <ul><li>OWL to LP can be achieved by a “Direct Mapping” or a “Meta Mapping” approach </li></ul><ul><li>Direct Mapping approach </li></ul><ul><ul><li>Intersection of DL with LP (called DLP) covering RDF schema and a subset of OWL </li></ul></ul><ul><ul><li>Has scalability and representational issues </li></ul></ul><ul><ul><li>Meta Mapping Approach </li></ul></ul><ul><ul><li>Maps LP subset of OWL into a higher representational level resulting in lower computational complexity and more representational flexibility. </li></ul></ul>
  11. 11. OWL LP SQL
  12. 12. Architecture <ul><li>Backend servers/Mobile devices both hold part of ontology </li></ul><ul><li>Homogenous storage and reasoning environment on server and mobile client </li></ul>DBMS Relational Reasoner Server Mobile DBMS Relational Reasoner Mobile Device Mobile DBMS Relational Reasoner Mobile Device Mobile DBMS Relational Reasoner Mobile Device <ul><li>Open Issues </li></ul><ul><li>Is there a way to delegate parts of the reasoning dynamically from a mobile device to a central reasoning server? </li></ul><ul><li>Transmission vs. replication? </li></ul>
  13. 13. Summary <ul><li>Meta Mapping of ontologies into logic programs has </li></ul><ul><ul><li>Higher expressivity </li></ul></ul><ul><ul><li>Better performance </li></ul></ul><ul><li>Extended by context awareness of reasoning system </li></ul><ul><ul><li>Architecture & open issues </li></ul></ul>
  14. 14. APPENDIX
  15. 15. OWL <ul><li>A family of knowledge representation languages for authoring ontologies endorsed by the World Wide Web Consortium. They are characterized by formal semantics and RDF/XML-based serializations for the Semantic Web. OWL has attracted both academic, medical and commercial interest. </li></ul>
  16. 16. RDF <ul><li>Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax formats. </li></ul><ul><li>&quot;The sky has the color blue&quot; in RDF is as the triple: a subject denoting &quot;the sky&quot;, a predicate denoting &quot;has the color&quot;, and an object denoting &quot;blue&quot;. </li></ul>
  17. 17. Description Logic <ul><li>Description Logic, are decidable fragments of First Order Logic . For a particular task, a logic is decidable if it is possible to design an algorithm that will terminate in a finite number of steps (i.e., the algorithm is guaranteed not to run forever). </li></ul><ul><li>provide a logical formalism for Ontologies and the Semantic Web. </li></ul>
  18. 18. Logic Programs <ul><li>a backwards reasoning theorem-prover applied to declarative sentences in the form of implications: </li></ul><ul><li>If B 1 and … and B n then H </li></ul><ul><li>treats the implications as goal-reduction procedures: </li></ul><ul><li>to show/solve H, show/solve B 1 and … and B n . </li></ul><ul><li>formalised in the Prolog notation </li></ul><ul><li>H :- B 1 , …, B n . </li></ul>

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