Semantic Web-based E-Commerce: The GoodRelations Ontology
Presentation at the Semantic Technology Conference, June 15, 2009
http://purl.org/goodrelations/
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
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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
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Site Page
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Site Page Page Page
3 6 7 8
Martin Hepp, 8
mhepp@computer.org
12. Web of Linked Data (âSemantic Webâ)
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
â Triple: (Subject, Predicate, Object)
â Exchange syntax: RDF/XML, N3, etc.
⢠RDFS and OWL: Formal languages for that help reduce ambiguity
and codify implicit facts
â 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 SpeciďŹcations: 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
20. What Do We Need?
⢠Vocabularies ⢠Tools
â Product or service ⢠Applications
types
â Businesses
â Offerings
⢠Data Sets
â Product model data
â Businesses, contact
details, opening hours
â 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
â RDF
â OWL
http://purl.org/goodrelations/
Martin Hepp, 21
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.â
Albert Einstein
Martin Hepp, 25
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26. What Makes for A Good Ontology?
⢠Main Contribution: Avoiding reclassification of
phenomena
â 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
â Over time
â Between individuals
â Across contexts
Martin Hepp, 26
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
32. The Minimal Scenario
⢠Scope
â Business entity
â Points-of-sale
â Opening hours
â Payment options
⢠Suitable for
â Every business
â E-commerce and
brick-and-mortar
Martin Hepp, 32
mhepp@computer.org
33. The Simple Scenario
⢠Scope: Minimal scenario plus
â Range of products or services
â Business functions
â Eligible regions or customer
types
â Delivery options
⢠Suitable for
â Any business: E-Commerce and
brick-and-mortar
â Specific products or services
Martin Hepp, 33
mhepp@computer.org
34. The Comprehensive Scenario
⢠Scope: Simple scenario plus
â Individual products or services
â Product features
â Pricing, rebates, etc.
â Availability
⢠Suitable for
â Any business: E-commerce and
brick-and-mortar
â Specific products or services
â Structured product database
Martin Hepp, 34
mhepp@computer.org
35. Product Model Data Scenario
⢠Scope
â Individual product
models
â Quantitative and
qualitative features
⢠Suitable for
â Manufacturers of
commodities
Martin Hepp, 35
mhepp@computer.org
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
51. Why Should I Bother?
⢠Web Shops: Better visibility in latest generation
search engines (e.g. Yahoo)
â 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. Itâs
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
â Form-based data entry
â RDFa: Embedding meta-
â RDBMS data in XHTML
â XML, e.g. BMEcat â RDF/XML: Extra file
â CSV â dataRSS: Yahoo feed
â Google CSV, RSS 1.0, format
RSS 2.0
⢠Amount of Detail and
Data Model
â What shall be included?
â How shall the type of
products be represented?
Martin Hepp, 53
mhepp@computer.org
54. Step-by-Step (2)
⢠Update Mechanism & Data Management
â PHP on demand
â Script-based data dump
⢠Publishing the Data
â Server configuration
â Notifying Semantic Web crawlers, Yahoo, âŚ
â 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