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KRDB2010-GoodRelations

  1. 1. The GoodRelations Ontology for E-Commerce 3rd KRDB School on Trends in the Web of Data (KRDB-2010) Brixen-Bressanone, Italy, 17-18 September 2010 Prof. Dr. Martin Hepp Professur für Allgemeine BWL, insbesondere E-Business
  2. 2. Part 1: Why bother? 18.09.2010 2
  3. 3. 6. Upcoming Research Challenges
  4. 4. Part 1: Why bother? 18.09.2010 4
  5. 5. Matchmaking in Market Economies 18.09.2010 5
  6. 6. Macroeconomic Impact Transaction Costs: > 50 % of the US GDP (1970) John Joseph Wallis and Douglas C. North: Measuring the Transaction Sector in the American Economy, 1870 – 1970 (1986) 18.09.2010 6
  7. 7. Key Driver of Search Costs: Specificity How much you loose when you can‘t use a good for what it was designed.
  8. 8. Growth in Specificity 1920: 5168 Types of Goods 18.09.2010 8
  9. 9. Examples 2010 18.09.2010 9
  10. 10. Examples 2010 18.09.2010 10
  11. 11. Examples 2010 18.09.2010 11
  12. 12. Specificity Increases the Search Space 18.09.2010 12
  13. 13. WWW: Dramatic Reduction of Search Effort 1993 2010 Lower search costs per search than ever before in history. 18.09.2010 13
  14. 14. But ….
  15. 15. The WWW: A Giant Data Shredder Source: Recipient: Structured Data Unstructured Text 18.09.2010 15
  16. 16. What is Linked Data Linked loves Susi Martin 1 2 3 4 18.09.2010 16
  17. 17. What is Special About E-Commerce Data? 1 2 RDBMS 3 4 $$$ 18.09.2010 17
  18. 18. GoodRelations: A Global Schema for 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 18.09.2010 18
  19. 19. On the Shoulders of Giants A Unified View of Commerce Data on the Web 18.09.2010 19
  20. 20. GoodRelations Deployment: Small Data Packets Inside Your Page (RDFa) 18.09.2010 20
  21. 21. Valuable Types of Links: Product - Product Model Photo credits: Flickr.com, available under CC BY 2.0 by bsabarnowl Ford T Data- gr:hasMakeAndModel sheet Often via strong, non-URI identifiers like EAN/UPC 18.09.2010 21
  22. 22. Valuable Types of Links: Offer – Store(s) XYZ gr:availableAtOrFrom for $ 99 18.09.2010 22
  23. 23. Valuable Types of Links: Company – Store(s) gr:hasPOS 18.09.2010 23
  24. 24. Part 2: Ontology Engineering Revisited 18.09.2010 24
  25. 25. Immanuel Kant on Ontologies & Linked Data „Thoughts without content are empty, intuitions without concepts are blind.“ Critique of Pure Reason (1781) 1. Ontologies without data are useless 2. Data without ontologies is blind 18.09.2010 25
  26. 26. In other words: Schemas Matter Photo credits: Flickr.com, available under CC BY 2.0 by dnorman Otherwise your data is just landfill… 18.09.2010 26
  27. 27. Albert Einstein on Schema Design "Make everything as simple as possible, but not simpler.“ Albert Einstein 18.09.2010 27
  28. 28. Data, Standards, Ontologies 18.09.2010 28
  29. 29. Subtle Distinctions Foster Data Reuse • Product Offer – „You can buy or lease my house“ • Store Business entity – „How many Tesco stores are in London?“ • Product Product Model – „How many digital cameras by Canon are listed on eBay“? 18.09.2010 29
  30. 30. Sophisticated Category Systems: Foundation for Intelligence and Judgment 18.09.2010 30
  31. 31. 18.09.2010 Ontology Economics Hepp, Martin: Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies, in: IEEE Internet Computing, 31 Vol. 11, No. 1, pp. 90-96, Jan-Feb 2007
  32. 32. Incremental Granularity & Lexical Carry-Over 18.09.2010 32
  33. 33. Ontology Engineering • Generic model – Stable distinctions – Easy to populate – Incremental Enrichment • Good textual elements • Good documentation • Tool support for the entire tool chain 18.09.2010 33
  34. 34. Part 3: GoodRelations Overview 18.09.2010 34
  35. 35. Basic Structure of Offers: Agent-Promise-Object Principle Object or Agent 1 Promise Happening Compensation Transfer of Rights Agent 2 35
  36. 36. The Minimal Scenario • Scope – Business entity – Points-of-sale – Opening hours – Payment options • Suitable for – Every business – E-commerce and brick-and-mortar 36
  37. 37. 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 37
  38. 38. 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 38
  39. 39. Product Model Data Scenario • Scope – Individual product models – Quantitative and qualitative features • Suitable for – Manufacturers of commodities 39
  40. 40. Developer Resources, Data, Tools http://purl.org/goodrelations/ 18.09.2010 40
  41. 41. The Minimal Scenario (UML & RDF/N3) 18.09.2010 41
  42. 42. The Simple Scenario: UML 18.09.2010 42
  43. 43. The Simple Scenario: RDF/N3 - Details 18.09.2010 43
  44. 44. Alternative Ways of Describing the Product or Service • Omit it – Minimal Example: Describe just your business & store • gr:ProductOrServiceSomeInstancesPlaceholder + rdfs:comment – Textual • Product or service ontology – eclassOWL – freeClass • DBPedia URIs • Turn proprietary hierarchy into pseudo-ontology 18.09.2010 44
  45. 45. Impact and Success • One of the few vocabularies implemented by major businesses out of their own budgets • BestBuy, O’Reilly, Overstock.com,… • Ca. 16 % of all triples as of now • Supported by Yahoo • Bing, Google may join 18.09.2010 45
  46. 46. Yahoo Enhanced by SearchMonkey 18.09.2010 46
  47. 47. Incredible Success 18.09.2010 47
  48. 48. GoodRelations #2 of all Web Ontologies …and this does not yet include the > 10 Mio. offers from Amazon and eBay! 18.09.2010 48
  49. 49. GoodRelations #2 of all Web Ontologies 18.09.2010 49
  50. 50. 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 18.09.2010 50
  51. 51. Syntax-neutral • RDF/XML, Turtle • Microdata • RDFa • dataRSS • OData • GData http://www.ebusiness-unibw.org/wiki/Syntaxes4GoodRelations 18.09.2010 51
  52. 52. Part 4: Publishing GoodRelations Data 18.09.2010 52
  53. 53. RDFa in Snippet Style http://www.ebusiness-unibw.org/tools/rdf2rdfa/ 18.09.2010 53
  54. 54. Publishing GoodRelations Data • RDFa in Snippet Style • sitemap.xml with proper lastmod attribute • robots.txt 18.09.2010 54
  55. 55. Microdata in Snippet Style http://www.ebusiness-unibw.org/tools/rdf2microdata/ 18.09.2010 55
  56. 56. Part 5: GoodRelations Advanced Topics 18.09.2010 56
  57. 57. GoodRelations-compliant Domain Ontologies 18.09.2010 57
  58. 58. Meta-Model for Quantitative Data 18.09.2010 58
  59. 59. Both Sides Can Help Build a Bridge gr:seeks property 18.09.2010 59
  60. 60. Ownership & Self Exposure • gr:owns property 18.09.2010 60
  61. 61. 6. Upcoming Research Challenges
  62. 62. Research Challenges (1) Natural Language Processing (2) Ontology Mapping and Alignment (3) Collaborative Ontology Engineering (4) Crawling, Update, Federation (5) Matchmaking & Query Learning (6) Applications and Interaction Patterns (7) Storage and Reasoning 18.09.2010 62
  63. 63. Natural Language Processing 18.09.2010 63
  64. 64. Ontology Mapping and Alignment 18.09.2010 64
  65. 65. Collaborative Ontology Engineering • OpenVocab • Knoodl • Protégé Collaboration Support • OntoVerse • MyOntology • Twine Ontology Editor • Neologism • MoKi http://www.ebusiness-unibw.org/wiki/Own_GoodRelations_Vocabularies 18.09.2010 65
  66. 66. Crawling, Update, Federation (1) Shop data changes every 1..24 h (2) Can you harvest the data from 1,000,000 shop sites just via – Sitemap.xml with proper lastmod attribute – RDFa inside the pages 18.09.2010 66
  67. 67. Matchmaking & Query Learning 18.09.2010 67
  68. 68. Applications and Interaction Patterns 18.09.2010 68
  69. 69. Storage and Reasoning • RDFS-style reasoning • Non-standard inference rules • Massive scale – 1 Mio shops etc. – 1 k – 100 k items,let’s say 10 k – 100 triples per item – 1 Mio * 10 k * 100 = 1,000,000,000,000 – 1 trillion triples 18.09.2010 69
  70. 70. Storage and Reasoning • Hybrid queries 18.09.2010 70
  71. 71. Data Quality Management http://www.ebusiness-unibw.org/tools/goodrelations-validator/ 18.09.2010 71
  72. 72. Thank you! http://purl.org/goodrelations/ Prof. Dr. Martin Hepp Chair of General Management and E-Business Universitaet der Bundeswehr 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 18.09.2010 72

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