Slide 1
International Internet Preservation ConsortiumGeneral Assembly 2014, Paris
Mining a Large Web Corpus
Robert MeuselChristian Bizer
Slide 2
The Common Crawl
Slide 3
Hyperlink Graphs
Knowledge about the structure of the Web can be used to improve crawling strategies, to help SEO experts or to understand social phenomena.
Slide 4
HTML-embedded Data on the Web
Several million websites semantically markup the content of their HTML pages.
Markup Syntaxes
Microformats
RDFa
Microdata
Data snippets within info
boxes
Slide 5
Relational HTML Tables
HTML Tables over semi-structured data which can be used to build up or extend knowledge bases as DBPedia.
• Cafarella, et al.: WebTables: Exploring the Power of Tables on the Web. VLDB 2008.
In a corpus of 14B raw tables, 154M are „good“relations (1.1%)
Slide 6
The Web Data Commons Project
Has developed an Amazon-based framework for extracting data from large web crawls Capable to run on any cloud infrastructure
Has applied this framework to the Common Crawl data Adaptable to other crawls
Results and framework are publicly available http://webdatacommons.org
Goal: Offer an easy-to-use, cost efficient, distributed extraction framework for large web crawls, as well as datasets extracted out of the crawls.
Slide 7
Extraction Framework
AWS EC2 Instance
AWS EC2 Instance
Master
AWS SQS
AWS EC2 Instance
AWS S3
1: Fill queue
2: Launch instances
3: Request file-reference
4: Download file
5: Extract & Upload
automated
manual
6: Collect results
Slide 8
Extraction Worker
AWS S3
AWS S3
WDC Extractor
.(w)arc
Worker
Filter
output
Worker:• Written in Java• Process one page at
once • Independent from
other files and workers
Download file
Upload output file
Filter:•Reduce Runtime•Mime-Type filter•Regex detection of content or meta-information
Worker
Slide 9
Web Data Commons – Extraction Framework
Written in Java
Mainly tailored for Amazon Web Services
Fault tolerant and cheap 300 USD to extract 17 billion RDF statements from 44 TB
Easy customizable Only worker has to be adapted
Worker is a single process method processing one file each time
Scaling is automated by the framework
Access Open Source Code: https://www.assembla.com/code/commondata/
Alternative: Hadoop Version, which can run on any Hadoop cluster without Amazon Web Services.
Slide 10
Extracted Datasets
Hyperlink Graph
HTML-embedded Data
Relational HTML Tables
Hyperlink Graph
HTML-embedded Data
Relational HTML Tables
Slide 11
Hyperlink Graph
Extracted from the Common Crawl 2012 Dataset
Over 3.5 billion pages connected by over 128 billion links
Graph files: 386 GB
http://webdatacommons.org/hyperlinkgraph/http://wwwranking.webdatacommons.org/
Slide 12
Hyperlink Graph
Degrees do not follow a power-law
Detection of Spam pages
Further insights: WWW‘14: Graph Structure in the Web – Revisited (Meusel et al.)
WebSci‘14: The Graph Structure of the Web aggregated by Pay-Level Domain (Lehmberg et al.)
Discovery of evolutions in the global structure of the World Wide Web.
Slide 13
Hyperlink Graph
Discovery of important and interesting sites using different popularity rankings or website categorization libraries
Websites connected by at least ½ Million Links
Slide 14
HTML-embedded Data
More and more Websites semantically markup the content of their HTML pages.Markup Syntaxes
RDFaMicroformats
Microdata
Slide 15
Websites containing Structured Data (2013)
1.8 million websites (PLDs) out of 12.8 million provide Microformat, Microdata or RDFa data (13.9%)
585 million of the 2.2 billion pages contain Microformat, Microdata or RDFa data (26.3%).
Web Data Commons - Microformat, Microdata, RDFa Corpus 17 billion RDF triples from Common Crawl 2013
Next release will be in winter 2014
http://webdatacommons.org/structureddata/
Slide 16
Top Classes Microdata (2013)
• schema = Schema.org• dv = Google‘s
Rich Snippet Vocabulary
Slide 17
HTML Tables
• Cafarella, et al.: WebTables: Exploring the Power of Tables on the Web. VLDB 2008.
• Crestan, Pantel: Web-Scale Table Census and Classification. WSDM 2011.
In corpus of 14B raw tables, 154M are “good” relations (1.1%). Cafarella (2008)
Classification Precision: 70-80%
Slide 18
WDC - Web Tables Corpus
Large corpus of relational Web tables for public download
Extracted from Common Crawl 2012 (3.3 billion pages)
147 million relational tables
selected out of 11.2 B raw tables (1.3%)
download includes the HTML pages of the tables (1TB zipped)
Table Statistics
Heterogeneity: Very high.
http://webdatacommons.org/webtables/
Min Max Average Median
Attributes 2 2,368 3.49 3
Data Rows 1 70,068 12.41 6
Slide 19
Attribute Statistics
28,000,000 different attribute labels
WDC - Web Tables Corpus
Attribute #Tables
name 4,600,000
price 3,700,000
date 2,700,000
artist 2,100,000
location 1,200,000
year 1,000,000
manufacturer 375,000
counrty 340,000
isbn 99,000
area 95,000
population 86,000
Subject Attribute Values
1.74 billion rows 253,000,000 different subject labels
Value #Rows
usa 135,000
germany 91,000
greece 42,000
new york 59,000
london 37,000
athens 11,000
david beckham 3,000
ronaldinho 1,200
oliver kahn 710
twist shout 2,000
yellow submarine 1,400
Slide 20
Conclusion
Three factors are necessary to work with web-scale data:
Thanks to Common Crawl, this data is available
Like Amazon or other on-demand cloud-services
The Web Data Commons Framework, or standard tools like Pig
Cost evaluation on task-base, but the WDC framework has turned out to be cheaper
Availability of Crawls
Availability of cheap, easy-to-use infrastructures
Easy to adopt scalable extraction frameworks
Slide 21
Questions
Please visit our website: www.webdatacommons.org
Data and Framework are available as free download
Web Data Commons is supported by: