Evolving the Web into a Global Dataspace – Advances and Applications

Bizer: Evolving the Web into a global Dataspace, BIS 2015, 24.6.2015 Slide 1
Prof. Dr. Christian Bizer
Evolving the Web into a Global
Dataspace
- Advances and Applications -
18th International Conference on Business Information System (BIS 2015)

Hello
Professor Christian Bizer
University of Mannheim
Research Topics
−Web Technologies
−Web Data Profiling
−Web Data Integration
−Web Mining

Data and Web Science Group @ University of Mannheim
− 6 Professors
• Heiner Stuckenschmidt
• Rainer Gemulla
• Christian Bizer
• Simone Ponzetto
• Heiko Paulheim
• Johanna Völker
− 25 researchers and PhD students
− http://dws.informatik.uni-mannheim.de/
1. Research methods for integrating and mining large
amounts of heterogeneous information from the Web.
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.

14 Years Later
There are 1.5 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 do smart things
with the data?

Outline
1. Semantic Annotations in HTML Pages
2. Linked Data
3. Knowledge Graphs
4. Conclusions

1. Semantic Annotations in HTML Pages
Simple idea: Help machines to understand
Web content by marking up data in HTML
pages.
<div itemtype="http://schema.org/Hotel">
<span itemprop="name">Vienna Marriott Hotel</span>
<span itemprop="address" itemscope="" itemtype="http://schema.org/PostalAddress">
<span itemprop="streetAddress">Parkring 12a</span>
<span itemprop="addressLocality">Vienna</span>
<span itemprop="addressCountry">Austria</span>
</span>
<div itemprop="aggregateRating" itemscope itemtype="http://schema.org/AggregateRating">
<span itemprop="ratingValue"> 4 </span> stars-based on
<span itemprop="reviewCount"> 250 </span> reviews.
</div>

Semantic Annotation Formats
Microformats
Microdata
RDFa
− date back to 2003
− small set of fixed formats
− W3C Recommendation in 2008
− can represent any type of data
− proposed in 2009
− tries to be simpler than RDFa

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

Schema.org
− ask site owners since 2011 to
annotate data for enriching search results
− 675 Types: Event, Place, Local Business, Product, Review, Person
− Encoding: Microdata, RDFa, JSON-LD

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

Event Data in Google Applications
https://developers.google.com/structured-data/

Flight Offers in Google Search Results
Annotated
webpages
directly below
Google Flights
results

Rich-Snippets Get More User Attention
− Suchen
Source: www.looktracker.com
Potential business
incentive.

Motivation for Semantic Annotations
− Study by searchmetrics.com in 2013: 10.000s of search keywords
− Type of rich-snippet displayed by Google:
Source: http://www.searchmetrics.com/de/knowledge-base/schema/
Google displays Rich-Snippets for 40% of all
queries.

The Common Crawl

The Web Data Commons Project
− extracts all Microformat, Microdata, RDFa data
from the Common Crawl
− analyzes and provides the extracted data for download
− four extraction runs so far
• 2014 CC Corpus: 2.0 billion HTML pages  20.4 billion RDF triples
• 2009/2010 CC Corpus: 2.5 billion HTML pages  5.1 billion RDF triples
− uses 100 machines on Amazon EC2
• approx. 3000 machine/hours
(spot instances of type c3.xlarge)  550 Euro
− http://www.webdatacommons.org/structureddata/

Overall Adoption 2014
620 million HTML pages out of the 2 billion pages
provide semantic annotations (30%).
2.72 million pay-level-domains (PLDs) out of the
15.68 million pay-level-domains covered by the
crawl provide annotations (17%).
Google, 2014*:
5 million websites provide Schema.org data.
* Guha in LDOW2014 Keynote

Number of PLDs providing Semantic Annotations

Most Popular Classes
RDFa
Microdata

Topical Focus – Microdata 2014
2014 2013
Class Instances # PLDs PLDs
# % # %
1 schema:WebPage 51.757.000 148,893 18,16% 69.712 15,04
2 schema:Article 54.972.000 88,7 10,82% 65.930 14,22
3 schema:Blog 3.787.000 110,663 13,50% 64.709 13,96
4 schema:Product 288.083.000 89,608 10,93% 56.388 12,16
5 schema:PostalAddress 48.804.000 101,086 12,33% 52.446 11,31
6 dv:Breadcrumb 269.088.000 76,894 9,38% 44.187 9,53
7 schema:AggregateRating 59.070.000 50,510 6,16% 36.823 7,94
8 schema:Offer 236.953.000 62,849 7,66% 35.635 7,69
9 schema:LocalBusiness 20.194.000 62,191 7,58% 35.264 7,61
10 schema:BlogPosting 11.458.000 65,397 7,98% 32.056 6,92
11 schema:Organization 101.769.000 52,733 6,43% 24.255 5,23
12 schema:Person 115.376.000 47,936 5,85% 21.107 4,55
13 schema:ImageObject 35.356.000 25,573 3,12% 16.084 3,47
14 dv:Product 12.411.000 16,003 1,95% 13.844 2,99
15 schema:Review 42.561.000 20,124 2,45% 13.137 2,83
16 dv:Review-aggregate 3.964.000 14,094 1,72% 13.075 2,82
17 dv:Organization 3.155.000 10,649 1,30% 9.582 2,07
18 dv:Offer 7.170.000 11,64 1,42% 9.298 2,01
19 dv:Address 2.138.000 9,674 1,18% 8.866 1,91
20 dv:Rating 1.732.000 9,367 1,14% 8.360 1,8
− Top Classes
− Topics:
• CMS and blog
metadata
• products and
offers
• ratings and
reviews
• business listings
• address data
• ...and a massive
long tail
schema: = Schema.org
dv: = Google Rich Snippet Vocabulary (deprecated)

Adoption by E-Commerce Websites
Distribution by Alexa Top-15 Shopping Sites
Top-Level Domain
TLD #PLDs
com 38344
co.uk 3605
net 1813
de 1333
pl 1273
com.br 1194
ru 1165
com.au 1062
nl 1002
Website schema:Product
Amazon.com 
Ebay.com 
NetFlix.com 
Amazon.co.uk 
Walmart.com 
etsy.com 
Ikea.com 
Bestbuy.com 
Homedepot.com 
Target.com 
Groupon.com 
Newegg.com 
Lowes.com 
Macys.com 
Nordstrom.com 
Adoption by Top-15:
60 %

Properties used to Describe Products
Top 15 Properties PLDs
# %
schema:Product/name 78,292 87 %
schema:Product/image 59,445 66 %
schema:Product/description 58,228 65 %
schema:Product/offers 57,633 64 %
schema:Offer/price 54,290 61 %
schema:Offer/availability 36,789 41 %
schema:Offer/priceCurrency 30,610 34 %
schema:Product/url 23,723 26 %
schema:Product/aggregateRating 21,166 24 %
schema:AggregateRating/ratingValue 20,513 23 %
schema:AggregateRating/reviewCount 14,930 17 %
schema:Product/manufacturer 10,150 11 %
schema:Product/brand 9,739 11 %
schema:Product/productID 9,221 10 %
schema:Product/sku 7955 9 %

Adoption by Travel Websites
Top 15 Travel Websites schema:Hotel Any Class
Booking.com (uses DataVoc)  
TripAdvisor  
Expedia  
Agoda  
Hotels.com  
Kayak  
Priceline  
Travelocity  
Orbitz  
ChoiceHotels  
HolidayCheck  
ChoiceHotels  
InterContinental Hotels Group  
Marriott International  
Global Hyatt Corp.  
Adoption:
73 %

Properties used to Describe Hotels
# %
schema:Hotel/name 4173 88,35 %
schema:Hotel/address 3311 70,10 %
schema:Hotel/telephone 2488 52,68 %
schema:PostalAddress/streetAddress 2362 50,01 %
schema:PostalAddress/addressLocality 2231 47,24 %
schema:Hotel/url 2102 44,51 %
schema:PostalAddress/postalCode 2096 44,38 %
schema:AggregateRating/ratingValue 1952 41,33 %
schema:Hotel/aggregateRating 1866 39,51 %
schema:AggregateRating/bestRating 1697 35,93 %

Adoption by Job Websites
Distribution by Top-10 Employment Sites
Top-Level Domain
Adoption by Top-10: 70 %
TLD #PLDs
jobs 908
com 828
org 263
co.uk 194
net 40
nl 38
ca 33
de 32
jobs 908
Website schema:JobPosting
Indeed.com 
Monster.com 
Careerbuilder.com 
Snagajob.com 
Jobsdb.com 
Jobsearch.about.com 
Jobs.net 
Internships.com 
Jobs.aol.com 
Quintcareers.com 

Properties used to Describe Job Postings
# %
JobPosting/title 2588 91.16 %
JobPosting/hiringOrganization 1412 49.74 %
JobPosting/description 1192 41.99 %
JobPosting/jobLocation 1062 37.41 %
Organization/name 862 30.36 %
JobPosting/datePosted 793 27.93 %
Place/address 471 16.59 %
JobPosting/baseSalary 227 8.00 %
JobPosting/industry 209 7.36 %
JobPosting/educationRequirements 145 5.11 %

Class / Property Distribution
 Only a small set of
classes / properties
is used.
 Strong focus on
Schema.org and
Facebook vocabularies.
schema.org
675 classes
965 properties

Opportunity 1: Search Engine Optimization
Get richer visibility in search results and potentially more clicks.

Opportunity 2: Change Push to Pull Communication
− Current situation:
• Information providers need to
push data into multiple channels
• multiple search engines
• multiple domain-specific portals
− Web approach:
• You maintain a website
• All interested parties
crawl your data

Opportunity 3: Applications beyond Rich-Snippets
− E-Commerce
• Rich source of product data, offers, and reviews
• Opportunity to build global product catalogs
• Opportunity to mine product and rating data on global-scale
− Tourism
• Additional data for tourism applications: Nearby local businesses, nearby
landmarks, nearby hospitals, nearby events
• Search engines as new competitors put pressure on large booking portals?
− Recruitment
• Increased market transparency
• Search engines as new competitors put pressure on job portals that charge
per posting?
− High up-to-dateness of data
• as original data providers know about changes first

Main Challenge: Data Integration and Cleansing
The schema is standardized, but
1. entity names differ
2. the schema is rather shallow and a rather low number of
properties is used
3. data quality differs as the data is created by experts and
rookies

Property PLDs
# %
schema:Product/name 78,292 87%
schema:Product/description 58,228 65%
schema:Product/manufacturer 10,150 11%
schema:Product/brand 9,739 11%
schema:Product/productID 9,221 10%
Looking Deeper into the E-Commerce Data
1. The structure of the data is rather shallow
• Product features are encoded in titles and descriptions
• Example product name:
“Apple MacBook Air 11-in, Intel Core i5 1.60GHz, 64 GB”
• Example product description:
“Faster Flash Storage with 64 GB Solid State Drive and USB 3.0 …”
• Product IDs are provided by only 10% of the websites
• Categorization information is provided only by 2% of the websites.

Categorization of Product Offers
− We analyzed 1.9 million product offers from 9200 shops
− We trained bag-of-words classifier for 9 product categories
on product descriptions from Amazon.
Source: Petar Petrovski, Volha Bryl, Christian Bizer: Integrating Product Data from Websites offering
Microdata Markup. In: 4th Workshop on Data Extraction and Object Search (DEOS2014) @ WWW2014

Identity Resolution for Electronic Products
− We trained feature extractors for product descriptions on offers for
electronic products from Amazon.
− We used the Silk framework for identity resolution.
Precision= 85%

Starting Points for Further Improvements
− Identity Resolution
• Exploit product identifiers to learn better product recognizers
• 10% of the websites (9,221 PLDs) use s:Product/productID
• 1% of the websites (935 PLDs) use s:Product/gtin13
− Categorization of Products
• Exploit categorization information provided by subset of the websites
• 1,5% of the websites (1,497 PLDs) use s:Offer/category
• 0,5% of the websites (460 PLDs) use s:WebPage/breadcrumb
• Challenge: Integration of ~ 2,000 product taxonomies
Home > Shop > Outdoor & Garden > Barbecues & Outdoor Living > Garden
Furniture > Tables > Dining Tables
Home > Shop > Outdoor & Garden > Barbecues & Outdoor Living > Garden
Furniture > Tables > Dining Tables
Philadelphia Eagles > Philadelphia Eagles Mens > Philadelphia Eagles Mens Jerseys >
over $60
Philadelphia Eagles > Philadelphia Eagles Mens > Philadelphia Eagles Mens Jerseys >
over $60

Conclusion: Semantic Annotations in HTML Pages
1. Wide-spread adoption of semantic annotations
• motivated by mayor search engines
1. Strong ontology agreement driven by data consumers
• Schema.org, Open Graph Protocol
1. Main application: Rich-snippets
2. Endless data pool for
• Commercial applications
• product and travel data integration and mining
• up-to-date listings of local businesses
• job search engines that increase market transparency
• Research
• large-scale data integration and mining
• information extraction (using annotations as distant supervision*)
* Foley, et al.: Learning to Extract Local Events from the Web. SIGIR 2015

Download and Play with the Data
− http://www.webdatacommons.org/structureddata/
− Only tip of the iceberg, as each website is only partly crawled.

2. 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 directly 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.

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

LOD Datasets on the Web: April 2014
Growth without new category Social Networking: 94 %
Source: Max Schmachtenberg, Christian Bizer, Heiko Paulheim: Adoption of the Linked Data Best Practices in
Different Topical Domains. In: 13th International Semantic Web Conference (ISWC2014).

Uptake in the Government Domain
− Various efforts by public sector
institutions world-wide
− Forerunners
• UK government
• US government
− Types of data published
• statistical data
• environmental data
• budget and election data
− Goals
• Make data available to the public and
other government agencies
• Ease data integration by using standards,
providing unique identifiers and by setting links

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)
• Springer (metadata about conference proceedings)
− Goals:
1. Interconnect resources between repositories
(by topic, by location, by historical period, by ...)
2. Integrate library catalogs on global scale

Uptake in the Life Science Domain
− Goals:
1. Connect life science datasets
in order to support
• biological knowledge discovery
• drug discovery
1. Reuse results of previous
integration efforts

Uptake in the Linguistic Research Community
http://linguistic-lod.org/llod-cloud
http://www.lider-project.eu/

Ontological Agreement
− Strong agreement on some vocabularies
− Proprietary vocabularies are used in
addition to common ones,
as data is often very specific
Widely-Used Vocabularies
Proprietary Vocabularies

RDF Links
− Some datasets put a lot of effort into linking
− Many datasets only link to a small number of
other datasets or do not set RDF links at all
Datasets with Top In-Degrees Out-Degrees per Category

RDF Links in the LOD Cloud: August 2014

Linked Data 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 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)
• Energy consumption (negative)
• Hospital beds/inhabitants
(negative)
• Fast food restaurants (positive)
• Police stations (positive)
Source: Petar Ristoski, Christian Bizer, and Heiko Paulheim: Mining the Web of Linked
Data with RapidMiner. Semantic Web Challenge, Winner of the Open Track, 2014.

Commercial Applications: Content Management at BBC
− Interconnect content management systems of different TV and radio stations.
− Similar efforts to connect content repositories at Elsevier and Springer.
Source: http://www.w3.org/2001/sw/sweo/public/UseCases/BBC

− IBM Rational uses Linked Data
technologies to connect data
from different
• software development tools
• software lifecycle tools
− Goals:
1. Make data independent
of concrete tool (IBM or third party)
2. Allow services (reporting, discovery)
to access data from all tools
3. Distributed data space as an
alternative to central repository or
integration hub / bus
Commercial Applications: Application Integration at IBM
Source: http://www.w3.org/2001/sw/sweo/public/UseCases/IBM

Conclusion: Linked Data vs. HTML-embeded Data
Linked Data Microdata, Microformats, RDFa
~ 1000 sources millions of sources
covers wider range of specific topics
focused on search engines and
facebook
more complex
data structures
very simple and shallow
data structures
partial ontology agreement strong ontology agreement
data integration eased by RDF links
data integration often
requires NLP techniques
various application prototypes
some industrial uptake
strong application pull
by search engines

3. Knowledge Graphs
− Google Knowledge Graph
• development started 2012, builds on Freebase
• 570 million objects described by over 18 billion facts (2012)
• 1500 classes, 35,000 properties
− Microsoft Satori Knowledge Base
• revealed to the public in mid-2013
− Yahoo Knowledge Graph
• revealed to the public early-2014
− Knowledge Graphs employ RDF-style graph data models
Large cross-domain knowledge bases which
aim
to cover all “relevant” entities in the world.

Data Sources used to Build Knowledge Graphs
1. Wikipedia
• infoboxes, category system, information extraction from text
1. Open license sources
• e.g. CIA World Factbook, MusicBrainz, …
1. Commercial third-party data
• e.g. IMDB, company listings, …
1. schema.org annotations in web pages
• e.g. contact information for companies
• e.g. logos of companies
Lots of effort is spend on data integration and manual data curation

Application of the Google Knowledge Graph
− Enrich search results with knowledge cards and lists
− Goal: Fulfil information need without having users navigate to other
websites

Application of the Microsoft Knowledge Graph

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

Google Now Smart Cards
− Direct answers are especially important in the mobile context
− Google Now displays direct answers for 19.45% of the queries
(Source: Stone Temple Consulting, 2015)
− Medical facts are reviewed by an average of 11.1 doctors
(Source: Google)

New SEO Topic: How to influence Knowledge Graphs?
Source: http://searchengineland.com/
leveraging-wikidata-gain-google-
knowledge-graph-result-219706

Behind-the-Scenes Applications
− Google
• uses its knowledge graph to identity entities in web pages (Entity Linking)
• Hummingbird ranking algorithm (deployed in 2013) uses
knowledge graph as background knowledge for ranking
search results.
− Yahoo
• uses its knowledge graph to “support applications across the company:
• Web Search, Content Understanding
• Recommendation, Personalization, Advertisement”*
− Data Integration
• becomes matching data sources against knowledge graphs
as intermediate schemata.
Various tasks become easier, if you know all
entities in the world.
*Source: Nicolas Torzec, Yahoo 2014

Public Knowledge Graphs

The DBpedia Knowledge Base - Version 2014
− Describes 4.58 million things, out of which
4.22 million are classified in a consistent ontology
using 685 classes and 2679 different properties
• 1,445,000 persons
• 735,000 places
• 241,000 organizations
• 123,000 music albums
− Altogether 3 billion pieces of information (RDF triples)
• 580 million were extracted from the English edition of Wikipedia
• 29,000,000 links to external web pages
• 50,000,000 external links into other RDF datasets
− DBpedia Internationalization
• provides data from 125 Wikipedia language editions for download
• For 28 popular languages DBpedia provides cleaned infobox data

DBpedia @ BIS2015
1. Thursday, 10:00
The Past, Present & Future of DBpedia
Keynote by Dimitris Kontokostas
2. Thursday, 10:45
4th DBpedia Community Meeting
Room 2

Google Knowledge Vault
− Research project to build a knowledge base
using facts extracted from 1 billion web pages
1. Web text (TXT): Entity linking,
relationship extraction
2. HTML trees (DOM): Wrapper induction
3. HTML tables (TBL): Relational tables
4. Semantic Annotations (ANO): schema.org, OGP
− Employs probabilistic model for data fusion
− Results: 1.6 billion facts
• 271 million with confidence >90%
• 90 million not in Freebase
Source: Luna Dong, Evgeniy Gabrilovich, et al.: Knowledge Vault:
A Web-scale approach to probabilistic knowledge fusion. In SIGKDD, 2014.

Data Sources for Public Research in this Space
1. Common Crawl
• ~ 2 billion HTML pages
• updated very couple of months
1. WebDataCommons HTML Tables Corpus
• 147 million relational web tables
• selected out of the 11 billion tables contained in the Common Crawl
• http://webdatacommons.org/webtables/
1. WebDataCommons Microdata and RDFa Corpora
• 20.4 billion RDF triples
• http://www.webdatacommons.org/structureddata/
1. Billion Triples Challenge Dataset 2014
• 4 billion RDF triples crawled from Linked Data sources
• http://km.aifb.kit.edu/projects/btc-2014/

Conclusion: 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.

4. Conclusions
1. Publication of Structured Data
• there is more data available as most people from research and industry like
• especially, schema.org annotations are currently gaining traction
• exciting test-bed for research on data profiling and data integration techniques
1. Ontological Agreement
• exists due to application-pull (Google, Facebook)
• but data source-specific attributes are also important
(e.g. in life science or government statistics domain)
1. Applications
• the big players are moving (Rich-Snippets, Knowledge Graphs)
• there is a lot of further application potential in the available data
• experimentation in industry, but many efforts are still in the prototype stage

Thanks
− References
• Robert Meusel, Petar Petrovski and Christian Bizer: The WebDataCommons Microdata, RDFa
and Microformat Dataset Series. 13th International Semantic Web Conference (ISWC2014).
• Max Schmachtenberg, Christian Bizer, Heiko Paulheim: Adoption of the Linked Data Best
Practices in Different Topical Domains (Slides, Video). 13th International Semantic Web
Conference (ISWC2014).
• Petar Petrovski, Volha Bryl, Christian Bizer: Integrating Product Data from Websites offering
Microdata Markup. 4th Workshop on Data Extraction and Object Search (DEOS2014).
− Detailed statistics on RDFa, Microdata and Microformats adoption
• http://www.webdatacommons.org/structureddata/
− Detailed statistics on Linked Data adoption
• http://linkeddatacatalog.dws.informatik.uni-mannheim.de/state/

Evolving the Web into a Global Dataspace – Advances and Applications

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