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

Part 1: Why bother?

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6. Upcoming Research Challenges

Part 1: Why bother?

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Matchmaking in Market Economies

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Macroeconomic Impact

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John Joseph Wallis and Douglas C. North:

Measuring the Transaction Sector in the

American Economy, 1870 – 1970

(1986)

Transaction Costs:

> 50 % of the

US GDP (1970)

Key Driver of Search Costs: Specificity

How much you loose when you can‘t

use a good for what it was designed.

Growth in Specificity

1920: 5168 Types of Goods

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Examples 2010

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Examples 2010

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Examples 2010

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Specificity Increases the Search Space

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WWW: Dramatic Reduction of Search Effort

Lower search costs per search than ever before in history.

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1993 2010

But ….

The WWW: A Giant Data Shredder

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Source: Structured Data

Recipient: Unstructured Text

What is Linked Data Linked

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Susi Martin

loves

1 2 3 4

What is Special About E-Commerce Data?

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$$$

RDBMS

1

2

3

4

GoodRelations: A Global Schema for Commerce Data on the Web

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Product Model Master Data Shop

Offerings Auctions Spare Parts & Consumables

Warranty

Delivery Payment

Retailers Manufacturers

Arbitrary Query

Extraction and Reuse

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On the Shoulders of Giants

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A Unified View of Commerce Data on the Web

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GoodRelations Deployment: Small Data Packets Inside Your Page (RDFa)

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Valuable Types of Links: Product - Product Model

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Ford T Data-sheet

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gr:hasMakeAndModel

Often via strong, non-URI identifiers like EAN/UPC

Valuable Types of Links: Offer – Store(s)

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XYZ for $ 99

gr:availableAtOrFrom

Valuable Types of Links: Company – Store(s)

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gr:hasPOS

Part 2: Ontology Engineering Revisited

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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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In other words: Schemas Matter

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Otherwise your data is just landfill… Photo

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Albert Einstein on Schema Design

"Make everything as simple as possible, but

not simpler.“

Albert Einstein

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Data, Standards, Ontologies

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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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Sophisticated Category Systems:

Foundation for Intelligence and Judgment

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Ontology Economics

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in:

Possib

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How

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Incremental Granularity & Lexical Carry-Over

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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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Part 3: GoodRelations Overview

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Basic Structure of Offers: Agent-Promise-Object Principle

35

Agent 1 Object or

Happening Promise

Agent 2

Compensation Transfer of Rights

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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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

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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Product Model Data Scenario

• Scope

– Individual product models

– Quantitative and qualitative features

• Suitable for

– Manufacturers of commodities

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Developer Resources, Data, Tools

http://purl.org/goodrelations/

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The Minimal Scenario (UML & RDF/N3)

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The Simple Scenario: UML

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The Simple Scenario: RDF/N3 - Details

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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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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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Yahoo Enhanced by SearchMonkey

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Incredible Success

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GoodRelations #2 of all Web Ontologies

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…and this does not yet include the > 10 Mio. offers from Amazon and eBay!

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GoodRelations #2 of all Web Ontologies

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GoodRelations Design Principles

• Keep simple things simple and make complex things possible

• Cater for LOD and OWL DL worlds

• Academically sound

• Industry-strength engineering

• Practically relevant

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Lightweight Web of Data

LOD RDF + a little bit

Heavyweight Web of Data

OWL DL

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Syntax-neutral

• RDF/XML, Turtle

• RDFa

• OData

• GData

• Microdata

• dataRSS

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http://www.ebusiness-unibw.org/wiki/Syntaxes4GoodRelations

Part 4: Publishing GoodRelations Data

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RDFa in Snippet Style

http://www.ebusiness-unibw.org/tools/rdf2rdfa/

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Publishing GoodRelations Data

• RDFa in Snippet Style

• sitemap.xml with proper lastmod attribute

• robots.txt

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Microdata in Snippet Style

http://www.ebusiness-unibw.org/tools/rdf2microdata/

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Part 5: GoodRelations Advanced Topics

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GoodRelations-compliant Domain Ontologies

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Meta-Model for Quantitative Data

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Both Sides Can Help Build a Bridge

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gr:seeks property

Ownership & Self Exposure

• gr:owns property

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6. Upcoming Research Challenges

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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Natural Language Processing

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Ontology Mapping and Alignment

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Collaborative Ontology Engineering

• OpenVocab

• Knoodl

• Protégé Collaboration Support

• OntoVerse

• MyOntology

• Twine Ontology Editor

• Neologism

• MoKi

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http://www.ebusiness-unibw.org/wiki/Own_GoodRelations_Vocabularies

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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Matchmaking & Query Learning

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Applications and Interaction Patterns

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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

Storage and Reasoning

• Hybrid queries

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Data Quality Management

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http://www.ebusiness-unibw.org/tools/goodrelations-validator/

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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