Slides from my talk at the 3rd KRDB school on Trends in the Web of Data, September 18, Brixen-Bressanone, Italy. http://www.inf.unibz.it/krdb/school/2010/

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. Part 1: Why bother?
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3. 6. Upcoming Research Challenges

4. Part 1: Why bother?
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5. Matchmaking in Market Economies
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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)
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7. Key Driver of Search Costs:
Specificity
How much you loose when you can‘t
use a good for what it was designed.

8. Growth in Specificity
1920: 5168 Types of Goods
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9. Examples 2010
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10. Examples 2010
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11. Examples 2010
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12. Specificity Increases the
Search Space
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13. WWW:
Dramatic Reduction of Search Effort
1993 2010
Lower search costs per search than
ever before in history.
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14. But ….

15. The WWW: A Giant Data Shredder
Source: Recipient:
Structured Data Unstructured Text
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16. What is Linked Data Linked
loves
Susi Martin
1 2 3 4
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17. What is Special About E-Commerce Data?
1
2 RDBMS
3
4 $$$
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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
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19. On the Shoulders of Giants
A Unified View of Commerce Data
on the Web
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20. GoodRelations Deployment: Small Data
Packets Inside Your Page (RDFa)
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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
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22. Valuable Types of Links:
Offer – Store(s)
XYZ
gr:availableAtOrFrom
for $
99
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23. Valuable Types of Links:
Company – Store(s)
gr:hasPOS
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24. Part 2: Ontology Engineering
Revisited
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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
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26. In other words: Schemas Matter
Photo credits: Flickr.com, available
under CC BY 2.0 by dnorman
Otherwise your data is just landfill…
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27. Albert Einstein on Schema Design
"Make everything as simple as possible, but
not simpler.“
Albert Einstein
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28. Data, Standards, Ontologies
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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“?
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30. Sophisticated Category Systems:
Foundation for Intelligence and
Judgment
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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. Incremental Granularity
& Lexical Carry-Over
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33. Ontology Engineering
• Generic model
– Stable distinctions
– Easy to populate
– Incremental Enrichment
• Good textual elements
• Good documentation
• Tool support for the entire tool chain
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34. Part 3: GoodRelations Overview
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35. Basic Structure of Offers:
Agent-Promise-Object Principle
Object or
Agent 1 Promise
Happening
Compensation Transfer of
Rights
Agent 2
35

36. The Minimal Scenario
• Scope
– Business entity
– Points-of-sale
– Opening hours
– Payment options
• Suitable for
– Every business
– E-commerce and
brick-and-mortar
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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
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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
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39. Product Model Data Scenario
• Scope
– Individual product
models
– Quantitative and
qualitative features
• Suitable for
– Manufacturers of
commodities
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40. Developer Resources, Data, Tools
http://purl.org/goodrelations/
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41. The Minimal Scenario (UML & RDF/N3)
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42. The Simple Scenario: UML
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43. The Simple Scenario: RDF/N3 - Details
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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
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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
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46. Yahoo Enhanced by SearchMonkey
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47. Incredible Success
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48. GoodRelations #2 of all Web Ontologies
…and this does not yet include the > 10 Mio. offers
from Amazon and eBay!
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49. GoodRelations #2 of all Web Ontologies
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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
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51. Syntax-neutral
• RDF/XML, Turtle • Microdata
• RDFa • dataRSS
• OData
• GData
http://www.ebusiness-unibw.org/wiki/Syntaxes4GoodRelations
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52. Part 4: Publishing GoodRelations Data
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53. RDFa in Snippet Style
http://www.ebusiness-unibw.org/tools/rdf2rdfa/
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54. Publishing GoodRelations Data
• RDFa in Snippet Style
• sitemap.xml with proper lastmod attribute
• robots.txt
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55. Microdata in Snippet Style
http://www.ebusiness-unibw.org/tools/rdf2microdata/
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56. Part 5: GoodRelations Advanced
Topics
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57. GoodRelations-compliant Domain Ontologies
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58. Meta-Model for Quantitative Data
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59. Both Sides Can Help Build a Bridge
gr:seeks property
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60. Ownership & Self Exposure
• gr:owns property
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61. 6. Upcoming Research Challenges

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
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63. Natural Language Processing
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64. Ontology Mapping and Alignment
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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
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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
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67. Matchmaking & Query Learning
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68. Applications and Interaction Patterns
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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
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70. Storage and Reasoning
• Hybrid queries
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71. Data Quality Management
http://www.ebusiness-unibw.org/tools/goodrelations-validator/
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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
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