1 / 26

How to De-engineer a Semantic Web:

How to De-engineer a Semantic Web:. Some thoughts on Linking Archaeological Data. Leif Isaksen, Graeme Earl & Kirk Martinez University of Southampton. The Problem. Summary data. Fragments of Meaning. Instance data. Type. Relationship. Mixing Models. Type. Relationship. Mixing Models.

sanaa
Download Presentation

How to De-engineer a Semantic Web:

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. How to De-engineer a Semantic Web: • Some thoughts on Linking Archaeological Data Leif Isaksen, Graeme Earl & Kirk Martinez University of Southampton

  2. The Problem

  3. Summary data

  4. Fragments of Meaning

  5. Instance data

  6. Type Relationship Mixing Models

  7. Type Relationship Mixing Models

  8. The representation is not the data!

  9. Approaches

  10. TRANSLATION(TRansparent Negotiation and Sharing of Local Application Terminologies, Instances and ONtologies) Peer-to-peer Server framework

  11. Problems with the TRANSLATION approach • Closed • An engineers-eye view • ‘Internet thinking’ not ‘web thinking’ • The network effect is created by Web 2.0 not Internet 2.0!

  12. Linked Data http://linkeddata.org

  13. GeoNames

  14. Subgraphs Canonical Types Instance Data

  15. Extension/Intension • What is ‘Dressel 20’? • Defined by attributes (Intension -> a priori analysis)? • Defined by examples (Extension -> a posteriori analysis)?

  16. Quantity vs. uncertainty

  17. Quantity vs. uncertainty

  18. Quantity vs. uncertainty

  19. Interface - ease of use • Archaeological data is distributed in fragments amongst (generally) non-tech literate professionals with no money and less time! • Conversion must be • Simple • Fast • Beneficial

  20. Implementation

  21. De-engineering the data • 4 Steps • Define Ontology • Define Canonical Types • Map local terms • Generate RDF

  22. Two Ontologies

  23. Linked Datasets SKOS Schemes SKOS Schemes archvocab.net Additional RDF archaeology.rkbexplorer.com Port Networks Triplestore + Port Networks partner sites Instance data

  24. archvocab.net/amphora

  25. archaeology.rkbexplorer.com • Project-specific data at class level • class equivalence using SKOS (skos:exactMatch) • class origin (e.g. Dressel 20 :hasOrigin Baetica) • other info • Helps separate vocabulary (archvocab.net) from politics!

  26. Demo

More Related