Semantic Web-based E-Commerce: The GoodRelations Ontology Presentation at the Semantic Technology Conference, June 15, 2009 http://purl.org/goodrelations/

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