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Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
Semantic Web-based E-Commerce: The GoodRelations Ontology
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Semantic Web-based E-Commerce: The GoodRelations Ontology

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Semantic Web-based E-Commerce: The GoodRelations Ontology …

Semantic Web-based E-Commerce: The GoodRelations Ontology
Presentation at the Semantic Technology Conference, June 15, 2009

http://purl.org/goodrelations/

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  • 1. Semantic Web-based E-Commerce: The GoodRelations Ontology Semantic Technology Conference June 15, 2009 Martin Hepp http://www.unibw.de/ebusiness/ http://purl.org/goodrelations/
  • 2. Part I: E-Commerce on the Web
  • 3. History Lesson: Search for Suppliers 1992: 1 Week 2009: 1 Minute Martin Hepp, 3 mhepp@computer.org
  • 4. But: Search for Suppliers, 2009 Martin Hepp, 4 mhepp@computer.org
  • 5. Limitations of the Web, 2009
  • 6. Specificity vs. Keyword-based Search • Synonyms • Homonyms • Multiple languages • No parametric search Martin Hepp, 6 mhepp@computer.org
  • 7. No Unified View: Jumping Back and Forth Across Data Silos Site Page Page Search Engine Results Search Engine Results 1 1 2 Search Engine Results Search Engine Results Page Page 3 4 Site Page 2 5 Site Page Page Page 3 6 7 8 Martin Hepp, 7 mhepp@computer.org
  • 8. We know the best hits only when done. Site Page Page 1 1 2 Search Engine Results Page Page 3 4 Site Page 2 5 Site Page Page Page 3 6 7 8 Martin Hepp, 8 mhepp@computer.org
  • 9. Limited Ability to Reuse Data Martin Hepp, 9 mhepp@computer.org
  • 10. The Web: A Bottleneck for Sharing Product Data Martin Hepp, 10 mhepp@computer.org
  • 11. Part II: E-Commerce based on a Web of Linked Data: The Vision
  • 12. Web of Linked Data (“Semantic Web”) Martin Hepp, 12 mhepp@computer.org
  • 13. Core Web of Linked Data Technology Pillars • URIs for everything • RDF: A data model for exchanging conceptual graphs based on triples – Triple: (Subject, Predicate, Object) – Exchange syntax: RDF/XML, N3, etc. • RDFS and OWL: Formal languages for that help reduce ambiguity and codify implicit facts – foo:human rdfs:subClassOf foo:mammal • SPARQL: Standardized query language and endpoint interface for RDF data • LOD Principles: Best practices for keeping the current Web and the Web of Data compatible Martin Hepp, mhepp@computer.org 13
  • 14. E-Commerce on the Web of Linked Data Martin Hepp, 14 mhepp@computer.org
  • 15. Goal: A Unified View on Commerce Data on the Web Extraction Arbitrary Query and Reuse Manufacturers Retailers Payment Delivery Product Model Warranty Master Data Shop Spare Parts & Offerings Auctions Consumables Martin Hepp, 15 mhepp@computer.org
  • 16. Use Case 1: Product Search • Find all MP3 players that have a USB interface and a color display, and sort them by weight (lightest first). ...on a Web Scale! Martin Hepp, 16 mhepp@computer.org
  • 17. Use Case 2: Product Model Data Reuse (PIM) World Wide Web World Wide Web Manufacturer Retailer / Web Shop Structured Structured Data on Data on Products Products and Product Specifications: and Services Type of Product, Features etc. Services Martin Hepp, 17 mhepp@computer.org
  • 18. Use Case 3: Fine-grained Affiliate Marketing Offers of computer add-ons that have an USB interface Screenshot from http://en.wikipedia.org/wiki/USB Martin Hepp, 18 mhepp@computer.org
  • 19. Part III: How? The GoodRelations Vocabulary
  • 20. What Do We Need? • Vocabularies • Tools – Product or service • Applications types – Businesses – Offerings • Data Sets – Product model data – Businesses, contact details, opening hours – Offering data Martin Hepp, 20 mhepp@computer.org
  • 21. The GoodRelations Vocabulary • A universal and free Web vocabulary for adding product and offering data to your Web pages. • Compatible with all relevant W3C standards and recommendations – RDF – OWL http://purl.org/goodrelations/ Martin Hepp, 21 mhepp@computer.org
  • 22. GoodRelations: Scope Martin Hepp, 22 mhepp@computer.org
  • 23. GoodRelations Design Principles • Keep simple things Lightweight Heavyweight simple and make Web of Data Web of Data complex things possible LOD OWL DL • Cater for LOD and OWL RDF + a little bit DL worlds • Academically sound • Industry-strength engineering • Practically relevant Martin Hepp, 23 mhepp@computer.org
  • 24. GoodRelations: License • Permanent, royalty-free access for commercial and non-commercial use. http://purl.org/goodrelations/ Martin Hepp, 24 mhepp@computer.org
  • 25. Albert Einstein on Ontology Engineering quot;Make everything as simple as possible, but not simpler.“ Albert Einstein Martin Hepp, 25 mhepp@computer.org
  • 26. What Makes for A Good Ontology? • Main Contribution: Avoiding reclassification of phenomena – Allows for cognitive and computer processing at the level of category membership • Good ontologies provide universally valid yet specific categories • Category membership should remain valid – Over time – Between individuals – Across contexts Martin Hepp, 26 mhepp@computer.org
  • 27. Data, Standards, Ontologies Martin Hepp, 27 mhepp@computer.org
  • 28. Basic Structure of Offers Object or Agent 1 Promise Happening Compensation Transfer of Rights Agent 2 28
  • 29. GoodRelations: One Single Schema for A Consolidated View on E-Commerce Data Extraction Arbitrary Query and Reuse Manufacturers Retailers Payment Delivery Product Model Warranty Master Data Shop Spare Parts & Offerings Auctions Consumables Martin Hepp, 29 mhepp@computer.org
  • 30. On the Shoulders of Giants A Unified View of Data on the Web Martin Hepp, 30 mhepp@computer.org
  • 31. Minimal Example Martin Hepp, 31 mhepp@computer.org
  • 32. The Minimal Scenario • Scope – Business entity – Points-of-sale – Opening hours – Payment options • Suitable for – Every business – E-commerce and brick-and-mortar Martin Hepp, 32 mhepp@computer.org
  • 33. The Simple Scenario • Scope: Minimal scenario plus – Range of products or services – Business functions – Eligible regions or customer types – Delivery options • Suitable for – Any business: E-Commerce and brick-and-mortar – Specific products or services Martin Hepp, 33 mhepp@computer.org
  • 34. The Comprehensive Scenario • Scope: Simple scenario plus – Individual products or services – Product features – Pricing, rebates, etc. – Availability • Suitable for – Any business: E-commerce and brick-and-mortar – Specific products or services – Structured product database Martin Hepp, 34 mhepp@computer.org
  • 35. Product Model Data Scenario • Scope – Individual product models – Quantitative and qualitative features • Suitable for – Manufacturers of commodities Martin Hepp, 35 mhepp@computer.org
  • 36. Part IV: Trends Data Sets & Industry Pick-up
  • 37. Others Do Care: Pick-up in Industry • Smart Information Systems • ebSemantics • Yahoo! SearchMonkey • Virtuoso Sponger Cartridges for Amazon, eBay, and others expected • Major German mail order companies • etc. Martin Hepp, 37 mhepp@computer.org
  • 38. Ping The Semantic Web, May 25 Martin Hepp, 38 mhepp@computer.org
  • 39. Yahoo Enhanced by SearchMonkey Martin Hepp, 39 mhepp@computer.org
  • 40. Yahoo Enhanced SearchMonkey Martin Hepp, 40 mhepp@computer.org
  • 41. GoodRelations Page Views Martin Hepp, 41 mhepp@computer.org
  • 42. Linked Open Commerce Dataspace ...forthcoming Martin Hepp, 42 mhepp@computer.org
  • 43. Part V: Tools and Resources
  • 44. GoodRelations User‘s Guide („Primer“) http://www.heppnetz.de/projects/goodrelations/primer/ Martin Hepp, 44 mhepp@computer.org
  • 45. GoodRelations Annotator http://www.ebusiness-unibw.org/tools/goodrelations-annotator/ Martin Hepp, 45 mhepp@computer.org
  • 46. GoodRelations Validator http://www.ebusiness-unibw.org/tools/goodrelations-validator/ Martin Hepp, 46 mhepp@computer.org
  • 47. RDF2dataRSS Tool http://www.ebusiness-unibw.org/tools/rdf2datarss/ Martin Hepp, 47 mhepp@computer.org
  • 48. osCommerce Extension http://code.google.com/p/goodrelations-for-oscommerce/ Martin Hepp, 48 mhepp@computer.org
  • 49. Joomla/VirtueMart Extension http://code.google.com/p/goodrelations-for-joomla/ Martin Hepp, 49 mhepp@computer.org
  • 50. Part VI: Recommendations What any business should do... ...immediately!
  • 51. Why Should I Bother? • Web Shops: Better visibility in latest generation search engines (e.g. Yahoo) – Same holds for any business that has a Web page, from A as in Amusement Park to Z as in Zoo. • Manufacturers: Allow your retailers to reuse product feature data with minimal overhead at both ends. • Software Developers: Help your customers to use and generate open, linked Web data. It’s easy! Martin Hepp, 51 mhepp@computer.org
  • 52. What Should I Do? • Web Shops: Create a GoodRelations data dump of your range of offers (rather simple) • Vendors of Web Shop Software: Create GoodRelations import and export interfaces (we can help you with that) • Every Business: Ask your webmaster to create at least a basic description of your range of products or services • Entrepreneurs: Invent new business models based on GoodRelations data Martin Hepp, 52 mhepp@computer.org
  • 53. Step-by-Step (1) • Data Sources • Data Delivery Options – Form-based data entry – RDFa: Embedding meta- – RDBMS data in XHTML – XML, e.g. BMEcat – RDF/XML: Extra file – CSV – dataRSS: Yahoo feed – Google CSV, RSS 1.0, format RSS 2.0 • Amount of Detail and Data Model – What shall be included? – How shall the type of products be represented? Martin Hepp, 53 mhepp@computer.org
  • 54. Step-by-Step (2) • Update Mechanism & Data Management – PHP on demand – Script-based data dump • Publishing the Data – Server configuration – Notifying Semantic Web crawlers, Yahoo, … – Semantic Sitemaps • Applications Martin Hepp, 54 mhepp@computer.org
  • 55. Part VII: The Sky Is the Limit Semantics in Affiliate Models, Serendipity, Matchmaking
  • 56. Thank you! http://purl.org/goodrelations/ Prof. Dr. Martin Hepp Chair of General Management and E-Business Universitaet der Bundeswehr University Muenchen Werner-Heisenberg-Weg 39 D-85579 Neubiberg, Germany Phone: +49 89 6004-4217 Fax: +49 89 6004-4620 http://www.unibw.de/ebusiness/ http://purl.org/goodrelations/ mhepp@computer.org Martin Hepp, 56 mhepp@computer.org

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