Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Context Addict Presentation


Published on

  • Be the first to comment

Context Addict Presentation

  1. 1. Politecnico di Milano Context -ADDICT Context-Aware Data Design,Integration, Customization, Tailoring Context-ADDICT
  2. 2. Motivations and scenarios• Disparate, heterogeneous, independent Data Sources• Semantic schema integration• Context-aware information filtering: Data Tailoring• Common, integrated, semantic access to data• Issues: mobility, data transiency• Multiple scenarios: system adaptability• < add your favourite buzz-word here > Context-ADDICT
  3. 3. Tasks and ChallengesTasks:• Data Source Discovery (later)• Lightweight (Semi)Automatic Data Integration• (Semi)Automatic Semantic Extraction• Context-Aware Data Filtering (focus)• Semantic Distributed Query processingChallenges:• Data Sources: heterogeneous, transient, mobile, unknown at design time• User Mobility• Multiple scenarios: system flexibility and adaptability• Need for high automatism• User Device Constraints (small portable devices) Context-ADDICT
  4. 4. Overall System Architecture Context-ADDICT
  5. 5. Models view Context-ADDICT
  6. 6. Data TailoringData Tailoring, based on the Dimension Tree Instantiation:• Schema Tailoring• Instance Tailoring Context-ADDICT
  7. 7. Data IntegrationDomain ontology - Data source integration:Standard Ontology mapping functionalitiesLightweight, automatic processing (mobile user’s device)Automatic inconsistencies resolution Context-ADDICT
  8. 8. Semantic ExtractionData Source Ontology:• Semantic Extraction: data abstract model + storage model• Supports the query processing• Models isolation (different models can be used separately) Context-ADDICT
  9. 9. Query AnsweringQuery Answering:• Choose an ontology query language (SPARQL, OWL-QL)• Query decomposition• Query translation• Data Fusion• Query Optimization Context-ADDICT
  10. 10. Context-ADDICT projects/thesisWe will managed area­based meeting/presentation:­ Ontology Mapping­ Semantic Extraction ­XML, Relational, Web(crawler)­ Ontology Tailoring­ Query AnsweringAre you interested? (Please rise you hand when asked)We will post the information about the meetings here:feed:// Context-ADDICT
  11. 11. Context-ADDICT projects QuickTime and a TIFF (LZW) decompressor are needed to see this picture.­ Dimension Tree + tailoring ­ ER tool integration­ X­SOM: ­ matching modules (neighborhood, subclass, probabilistic,  H­MATCH integration) ­ Protégé plugin and standalone­ Relational Integration ­ use CLIO (or similar) + automatic feeding by domain  ontology­ Query Answering ­ query language selection (expressivity & al) ­ automatic wrapper generation for Relational and XML­ XML2OWL ­ look at the XSLT based approach and enrich it...­ Relational2OWL ­ advanced features on ER generalization ­ Plugin GUI­ Ontology Extraction ­ semantic completeness + labelling vs querying ­ Web 2 OWL ­ ontology extraction from web sources Context-ADDICT
  12. 12. Context-ADDICT teamProf.ssa Cristiana Bolchini (Prof. Fabio A. Schreiber (Prof.ssa Letizia Tanca (Dott.ssa Elisa Quintarelli (Dott.ssa Rosalba Rossato (Ing. Carlo A. Curino (Ing. Antonio Penta (Ing. Giorgio Orsi ( Context-ADDICT
  13. 13. Conclusions These projects are part of our research so are: Limited, Challenging, Unique, Work-intensive, Team-managed If you want a project you are welcome on board, please contact: orsi@elet.polimi.itOtherwise I’m sorry you have just lost the most challenging and exciting chance you have had in your life! Context-ADDICT