Evolving the Web into a Global Database - Advances and Applications.

Bizer: Evolving the Web into a global Database – Advances and Applications, 30.1.2014 Slide 1
Prof. Dr. Christian Bizer
Evolving the Web into a global
Database
- Advances and Applications -

Data and Web Science Group @ University of Mannheim
− 3 Professors
• Prof. Dr. Heiner Stuckenschmidt
• Prof. Dr. Simone Paolo Ponzetto
• Prof. Dr. Christian Bizer
− 5 Post-Doctoral Researchers
− 18 PhD Students
− http://dws.informatik.uni-mannheim.de/
1. Research methods for integrating and mining large
amounts of heterogeneous information within
enterprise and open Web contexts.
2. Empirically analyze the content and structure of the
Web.

Querying the classic Web
DB
HTML

Long standing Goal
Query the Web like
a single, global
database

2001 Article: The Semantic Web
Envisions three things to happen:
1.people publish data in structured form
in addition to HTML pages on the Web
2.common vocabularies / ontologies are used
to represent data
3.people implement cool applications that
do smart things with the available data.
Tim Berners-Lee, James Hendler and Ora Lassila:
The Semantic Web. Scientific American, May 2001.

13 Years Later
There are 1.3 million publications about the
Semantic Web on Google Scholar, but
1. Do people publish structured data on the Web?
2. Do people agree on common vocabularies / ontologies?
3. What are the cool applications that exploit the data?

Outline
1. Linked Data
2. HTML-embedded Data
3. The Role of Wikipedia
4. Conclusions

1. Linked Data
B C
RDF
RDF
link
A D E
RDF
links
RDF
links
RDF
links
RDF
RDF
RDF
RDF
RDF RDF
RDF
RDF
RDF
• by using RDF to publish structured data on the Web
• by setting links between data items within different data sources.
Set of best practices for publishing structured data on
the Web in the form of a single global data graph.

Global Identifiers and Links as Integration Hints
 publishing Identity Links on the Web
 publishing Vocabulary Links on the Web
<http://www4.wiwiss.fu-berlin.de/is-group/resource/persons/Person4>
owl:sameAs
<http://dblp.l3s.de/d2r/resource/authors/Christian_Bizer> .
<http://xmlns.com/foaf/0.1/Person>
owl:equivalentClass
<http://dbpedia.org/ontology/Person> .

Effort Distribution between Publisher and Consumer
Publishers or third
parties provides
identity/vocabulary links
Consumer mines missing
identity/vocabulary links
Effort
Distribution

W3C Linking Open Data Project
− Grassroots community effort started in 2007 to
• publish existing open license datasets as Linked Data on the Web
• interlink things between different data sources
• maintain a data set catalog on the CKAN DataHub

LOD Datasets on the Web: September 2011
295 data sets
31,6 billion RDF triples

Newer statistics
− LODstats (University of Leipzig, 2014): 928 data sets
− LDspider Crawl (University of Mannheim, 2013): 850 data sets
Distribution by Topical Domain (September 2011)
Domain Data Sets Triples Percent RDF Links Percent
Media 25 1,841,852,061 5.82 % 50,440,705 10.01 %
Geographic 31 6,145,532,484 19.43 % 35,812,328 7.11 %
Government 49 13,315,009,400 42.09 % 19,343,519 3.84 %
Library 87 2,950,720,693 9.33 % 139,925,218 27.76 %
Cross-domain 41 4,184,635,715 13.23 % 63,183,065 12.54 %
Life sciences 41 3,036,336,004 9.60 % 191,844,090 38.06 %
User content 20 134,127,413 0.42 % 3,449,143 0.68 %
SUM 295 31,634,213,770 503,998,829

Ontological Agreement
− Out of the 295 data sources
• 102 (35%) only use terms from common vocabularies
• 105 (36%) only use proprietary terms
• 88 (29%) mix common and proprietary terms
− Popular Vocabularies
Vocabulary # Data Sets
Dublin Core 92 (31.19 %)
FOAF 81 (27.46 %)
SKOS 58 (19.66 %)
GEO 25 (8.47 %)
AKT 17 (5.76 %)
BIBO 14 (4.75 %)
Music Ontology 13 (4.41 %)
SIOC 10 (3.39 %)

Uptake in the Government Domain
− Goals
• Make data available to the public and other government agencies
• Ease data integration by providing unique identifiers and by setting links
− W3C Government Linked Data Working Group

Uptake in the Libraries Community
− Institutions publishing Linked Data
• Library of Congress (subject headings)
• German National Library (PND dataset and subject headings)
• Swedish National Library (Libris - catalog)
• Hungarian National Library (OPAC and Digital Library)
• Europeana Digital Library (4 million artifacts)
− Goals:
1. Integrate Library Catalogs on global scale
2. Interconnect resources between repositories
(by topic, by location, by historical period, by ...)

Industry Uptake
− Media Industry
• British Broadcasting Corporation
• New York Times
• Wolters Kluwer
• Springer
− Pharmaceutical Industry
• Johnson & Johnson
• Eli Lilly and Company
• AstraZeneca
− IT Industry
• IBM

2. HTML-embedded Data
Microformats
Microdata
RDFa
Websites semantically markup the
content of their HTML pages using:

Schema.org
− ask site owners since 2011
to markup data to enrich search results.
− 200+ Types: Event, Organization, Person, Place, Product, Review
− Encoding: Microdata or alternatively RDFa

Open Graph Protocol
− allows site owners to determine how
entities are described in Facebook
− relies on RDFa for encoding data in HTML pages
− available since April 2010

The Common Crawl

The WebDataCommons.org Project
− extracts all Microformat, Microdata, RDFa data from the
Common Crawl
− analyzes and provides the extracted data for download
− Two extractions runs
• 2009/2010 CC Corpus: 2.5 billion HTML pages  5.1 billion RDF triples
• 2012 CC Corpus: 3.0 billion HTML pages  7.3 billion RDF triples
− used 100 machines on Amazon EC2
• approx. 3000 machine/hours
(spot instances of type c1.xlarge)  550 EUR
− Jointed effort in the context of the EU project

Websites providing Structured Data (2012)
2.29 million websites (PLDs) out of 40 million
provide Microformat, Microdata or RDFa data
(5.65%)
369 million of the 3 billion pages contain
Microformat, Microdata or RDFa data (12.3%)
Google, October 2013:
15% of all websites provide structured data.

Breakdown by Encoding Format and Site Popularity
Grouped by Alexa Website Popularity Rank
(rank based on amount of page views)

− Top Classes:
− Topics
• CMS and Blog
metadata
• Product data
• Ratings/Reviews
• Company listings
RDFa Topics (CC 2012)
og = Facebook‘s Open
Graph Protocol

− Top Classes:
− Topics
• CMS and Blog
metadata
• Navigational
metadata
• Products and offers
• Business listings
• Ratings
• Places
• Events
Microdata Topics (CC 2012)
schema = Schema.org
datavoc = Google‘s
Rich Snippet Vocabulary

Class / Property Distribution
 A small set of
classes / properties
is used.
 Strong focus on
Schema.org and
Facebook vocabularies

Looking Deeper into the E-Commerce Data
Microdata
(2012)
Example Names:
• AppleMacBook Air MC968/A 11.6-Inch Laptop
• Apple MacBook Air 11-in, Intel Core i5 1.60GHz, 64 GB, Lion 10.7
Example Description:
• Faster Flash Storage with 64 GB Solid State Drive and USB 3.0 …

Usage of Schema.org Data @ Google
Rich snippets
within
search results

Usage of Open Graph Protocol Data @ Facebook
− allows site owners to determine how
entities are described in Facebook

Valuable Resource for Comparison Shopping Sites
− We analyzed 1.9 million product offers from 9200 shops
− We trained classifier for 9 product categories on product descriptions
from Amazon.

Identity Resolution for Electronic Products
− We trained parser for product descriptions on offers for electronic
products from Amazon.
− We used Silk framework for identity resolution.

Linked Data vs. HTML-embeded Data
LOD Cloud Microdata, Microformats, RDFa
< 1000 sources millions of sources
covers wider range of specific topics
focused on search engines and
Facebook
contains more complex
data structures
very simple and shallow
data structures
partial ontology agreement strong ontology agreement
data integration eased by RDF links
data integration requires NLP
techniques

Title
Description
Cross
Language
Links
Geo-
Coordinates
Images
Infoboxes
3. The Role of Wikipedia

Extracting Knowledge from Wikipedia

The DBpedia Knowledge Base - Version 3.9
− describes 4.00 million things, out of which
3.22 million are classified in a consistent ontology
using 529 classes and 2217 different properties
• 832,000 persons
• 639,000 places
• 209,000 organizations
• 116,000 music albums
− Altogether 2.46 billion pieces of information (RDF triples)
• 24,000,000 links to external web pages
• 27,200,000 external links into other RDF datasets
− DBpedia Internationalization
• provide data from 119 Wikipedia language editions for download
• 24 popular languages we provide cleaned infobox data

1. Answer fact queries: “birthdate michael douglas”
2. Compare things: „compare eiffel tower vs empire state building”
Applications of Google‘s Knowledge Graph

Applications of Google‘s Knowledge Graph
3. Enrich search results with infoboxes and lists
• Infoboxes might also contain Microdata/RDFa data, e.g. concerts of a band
3. Rank of search results using new Hummingbird ranking algorithm

DBpedia as Background Knowledge for Data Mining
− Which factors correlate with unemployment in France?

Unemployment Table with additional Attributes

RapidMiner Linked Open Data Extension
Allows you to
1. link local table to DBpedia and other LOD data sources
2. extend local table with additional attributes
3. mine extended tables using all Rapidminer features

Finding Correlations
− Use additional attributes to find interesting correlations
− Example correlation for unemployment in France:
• African islands, Islands in the Indian Ocean,
Outermost regions of the EU (positive)
• Population growth (positive)
• Disposable income (negative)
• Energy consumption (negative)
• Fast food restaurants (positive)
• Hospital beds/inhabitants
(negative)
• Police stations (positive)

Conclusions
1. Publication of Structured Data
• There is more data than most people from research and industry like
• Exciting test-bed for data profiling and data integration techniques
• Not even the research focus has moved to the integration of 1000s of
sources
1. Ontological Agreement
• Application-pull helps (Google et al.)
• But data source-specific attributes are also important
(e.g. in life science or statistics domain)
1. Applications
• the big players are moving
• there is a lot of experimentation in industry, but many efforts are still in the
prototype stage

Thanks
Mannheim Linked Open Data Meetup
−Free beer and food
−Talks by Springer, Wolters Kluwer, Semantic Web
Company, LOD2 project participants, DWS group
members
−Sunday, February 23, 2014, 6:30 PM
−http://www.meetup.com/OpenKnowledgeFoundation/M
annheim-DE/1092882/
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