Integrating Product Data from
Websites offering Microdata
Markup
School of Business Informatics and Mathematics
Petar Petr...
Outline
1. HTML-embedded Data on the Web
2. The Data Integration Pipeline
1. Microdata extraction
2. Classification
3. Fea...
HTML-embedded Data
More and more Websites semantically markup the
content of their HTML pages.
Microformats
Microdata
RDFa...
Schema.org
• ask site owners to embed
data to enrich search results.
• 200+ Classes: Product, Review, LocalBusiness, Perso...
Usage of Schema.org Data @ Google
Data snippets
within
search results
Data snippets
within
info boxes
5Integrating Product...
Websites Containing Structured Data
(November 2013)
1.7 million websites (PLDs) out of 12.8 million
provide Microformat, M...
Top Classes, Microdata (2013)
• schema = Schema.org
• datavoc = Google‘s
Rich Snippet Vocabulary
7Integrating Product Data...
Example: Microdata, Local Business
8Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Biz...
Example: Microdata, Product
School of Business Informatics and Mathematics
The Data Integration Pipeline
• Objective: integrate all data found on the web
describing a specific entity (e.g. product ...
Outline
1. HTML-embedded Data on the Web
2. The Data Integration Pipeline
1. Microdata extraction
2. Classification
3. Fea...
Web Data Commons Extraction
Framework
• Web Data Commons project: extracts structured data from
the Common Crawl
– http://...
Looking Deeper into E-Commerce Data
Microdata Product (2013)
13Integrating Product Data from Microdata Markup. Petar Petro...
Looking Deeper into E-Commerce Data
Microdata Product (2012)
Example: Title and Description
Title
Description
AppleMacBook Air MC968/A 11.6-Inch Laptop
Faster Flash Storage with 64 GB...
Outline
1. HTML-embedded Data on the Web
2. The Data Integration Pipeline
1. Microdata extraction
2. Classification
3. Fea...
Product Classification
• Starting from 9.4 million products:
• Products with English descriptions with length grater than ...
Classification Performance
Category Precision % Recall % #
Books 86.58 87.95 233,249
Movies, Music & Games 89.81 70.63 186...
Outline
1. HTML-embedded Data on the Web
2. The Data Integration Pipeline
1. Microdata extraction
2. Classification
3. Fea...
Product Feature Extraction
• Low precision (69%) for identity resolution without product feature
extraction
– Used later a...
Free Text Preprocessor by Example
<http://wdc.org/resource/2> <http://schema.org/Product/title> "Apple iPod nano (8 GB, 6t...
Free Text Preprocessor by Example
<http://wdc.org/resource/2> <http://schema.org/Product/title> "Apple iPod nano (8 GB, 6t...
Silk Free Text Preprocessor by Example
<http://wdc.org/resource/2> <http://schema.org/Product/title> "Apple iPod nano (8 G...
Extractors – Bag-of-words
• Learning
• Creating a list of words for every feature in the training set
• Extraction
• Match...
Extractors – Feature-Value Pairs
Learns feature-value pairs from the structured data
Extraction
• Tagging – taking n-grams...
Extractors – Manual Configuration
Manually configure features and extraction methods
1. Regular expressions
• E.g. Process...
Extraction Experiments
• Dataset for extraction 5,000 electronic
products from WDC
• Training dataset (structured data)
– ...
Extraction Accuracy
Brand Model Storage Display Processor Dimension
iPod Nano .92 .98 .86 .49 .12 .78
Galaxy SII .72 .87 ....
Outline
1. HTML-embedded Data on the Web
2. The Data Integration Pipeline
1. Microdata extraction
2. Classification
3. Fea...
Identity Resolution
• We used Silk – a tool for discovering relationships
between data items within different linked data
...
Identity Resolution Experiments
• Gold standard: 5,000 links manually annotated
• 2,500 positive/2,500 negative
• 20 elect...
Silk Output: Learned Linkage Rule
:Property
wdc:Model
:Transform
lowerCase
:Comparison
func = Levensthein
threhold = 1.134...
Identity Resolution Results
Precision % Recall % F-Measure %
Baseline 69 90 78.1
Bag-of-words 75 82 77.9
Feature-value pai...
Outline
1. HTML-embedded Data on the Web
2. The Data Integration Pipeline
1. Microdata extraction
2. Classification
3. Fea...
Data Fusion
• Input: clusters of products after identity resolution
• Properties worth fusing/combining
– AggregateRating ...
Fusion Results
Product Offers Reviews Ratings
iPod Nano 8GB 829 84 0
iPhone 4 16GB 624 35 52
Sony Ericsson Xperia Mini 450...
Conclusions
• By using Microdata, thousands of websites help us to
understand their content
• We have implemented the 5-st...
Questions?
38Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
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Petar Petrovski, Volha Bryl, Christian Bizer. Integrating Product Data from Websites offering Microdata Markup.The 4th Workshop on Data Extraction and Object Search (DEOS) @ WWW 2014

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Integrating Product Data from Websites offering Microdata Markup

  1. 1. Integrating Product Data from Websites offering Microdata Markup School of Business Informatics and Mathematics Petar Petrovski, Volha Bryl, Christian Bizer Data and Web Science Research Group University of Mannheim, Germany
  2. 2. Outline 1. HTML-embedded Data on the Web 2. The Data Integration Pipeline 1. Microdata extraction 2. Classification 3. Feature extraction 4. Identity resolution 5. Data Fusion 3. Conclusions 2Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  3. 3. HTML-embedded Data More and more Websites semantically markup the content of their HTML pages. Microformats Microdata RDFa 3Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  4. 4. Schema.org • ask site owners to embed data to enrich search results. • 200+ Classes: Product, Review, LocalBusiness, Person, Place, Event, … • Encoding: Microdata or RDFa 4Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  5. 5. Usage of Schema.org Data @ Google Data snippets within search results Data snippets within info boxes 5Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  6. 6. Websites Containing Structured Data (November 2013) 1.7 million websites (PLDs) out of 12.8 million provide Microformat, Microdata or RDFa data (13%) 585 million of the 2.2 billion pages contain Microformat, Microdata or RDFa data (26%). http://webdatacommons.org/structureddata/ Google, October 2013: 15% of all websites provide structured data. 6Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  7. 7. Top Classes, Microdata (2013) • schema = Schema.org • datavoc = Google‘s Rich Snippet Vocabulary 7Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  8. 8. Example: Microdata, Local Business 8Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  9. 9. Example: Microdata, Product School of Business Informatics and Mathematics
  10. 10. The Data Integration Pipeline • Objective: integrate all data found on the web describing a specific entity (e.g. product or organization) • Motivation: enables creation of powerful applications, e.g. comparison shopping portals • Use case: product data • Implemented Pipeline: 10Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  11. 11. Outline 1. HTML-embedded Data on the Web 2. The Data Integration Pipeline 1. Microdata extraction 2. Classification 3. Feature extraction 4. Identity resolution 5. Data Fusion 3. Conclusions 11Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  12. 12. Web Data Commons Extraction Framework • Web Data Commons project: extracts structured data from the Common Crawl – http://webdatacommons.org/ – http://commoncrawl.org/ • Code available at: – https://subversion.assembla.com/svn/commondata/ – Based on Anything To Triples (any23) library for extracting structured data: http://any23.apache.org • Common Crawl 2012 – 3 billion HTML pages, 40.6 million websites – 7.3 billion statements describing 1.15 billion things – 9.4 million product offers from 9240 e-shops Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  13. 13. Looking Deeper into E-Commerce Data Microdata Product (2013) 13Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  14. 14. Looking Deeper into E-Commerce Data Microdata Product (2012)
  15. 15. Example: Title and Description Title Description AppleMacBook Air MC968/A 11.6-Inch Laptop Faster Flash Storage with 64 GB Solid State Drive and USB 3.0. 720p FaceTime HD Camera. The new 1.6 GHz Intel Core i5 Processor with Intel HD Graphics 3000 enabling beautiful rendering and 4GB DDR3 RAM. 11.6” LED display with the best resolution… Title Description The MacBook Air MC 968/A powered by Intel Core i5(1.6GHz, 3MB L3). 64 GB SSD and 4096 MB of DDR3 RAM. 29.464cm (11.6”) TFT 1366x768, Intel HD Graphics, IEEE 802.11a/b/g, Bluetooth 4.0, FaceTme camera, OS X LIon Apple MacBook Air 11-in, Intel Core i5 1.60GHz, 4 GB, 64 GB, Mac OS X Lion 10.7 Various abbreviations can be found describing same features Often imprecise values due to rounding in numeric values can be found Different descriptions follow different levels of detail Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  16. 16. Outline 1. HTML-embedded Data on the Web 2. The Data Integration Pipeline 1. Microdata extraction 2. Classification 3. Feature extraction 4. Identity resolution 5. Data Fusion 3. Conclusions 16Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  17. 17. Product Classification • Starting from 9.4 million products: • Products with English descriptions with length grater than 20 words => 1,986,359 products from 9,240 e-shops • Training set – 18,000 labeled products, 9 classes • Training the model – Naïve Bayes Classifier • Features generation – 4 step process – tokenizing and removing stop words, pruning, n-grams, TF-IDF – ~3600 features 17Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  18. 18. Classification Performance Category Precision % Recall % # Books 86.58 87.95 233,249 Movies, Music & Games 89.81 70.63 186,832 Electronics & Computers 92.98 88.00 219,118 Home, Garden & Tools 73.81 60.78 186,495 Grocery, Health & Beauty 70.20 72.86 120,573 Toys, Kids, Baby & Pets 75.00 64.85 114,236 Clothing, Shoes & Jewelry 88.56 89.93 206,315 Sports & Outdoors 72.83 67.90 143,156 Automotive & Industrial 73.06 65.50 168,567 Average 80.31 74.26 1,578,541 18Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer The offers originate from 9,240 e-shops
  19. 19. Outline 1. HTML-embedded Data on the Web 2. The Data Integration Pipeline 1. Microdata extraction 2. Classification 3. Feature extraction 4. Identity resolution 5. Data Fusion 3. Conclusions 19Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  20. 20. Product Feature Extraction • Low precision (69%) for identity resolution without product feature extraction – Used later as a baseline for identity resolution • We developed the Free Text Preprocessor – Makes the data more structured by extracting new property- value pairs from free-text properties – https://www.assembla.com/spaces/silk/wiki/Silk_Free_Text_Preprocessor 20Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  21. 21. Free Text Preprocessor by Example <http://wdc.org/resource/2> <http://schema.org/Product/title> "Apple iPod nano (8 GB, 6th generation, Graphite)" . <http://wdc.org/resource/2> <http://schema.org/Product/description> "Memory size: 8GB. Colour: Graphite Generation: 6th generation. Memory type: Integrated. Weight: 21.1g. Radio: With Radio. Audio/Video formats: AAC, AIFF, Audible, MP3, WAV, VBR Display: 1.5-inch" . 21Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  22. 22. Free Text Preprocessor by Example <http://wdc.org/resource/2> <http://schema.org/Product/title> "Apple iPod nano (8 GB, 6th generation, Graphite)" . <http://wdc.org/resource/2> <http://schema.org/Product/description> "Memory size: 8GB. Colour: Graphite Generation: 6th generation. Memory type: Integrated. Weight: 21.1g. Radio: With Radio. Audio/Video formats: AAC, AIFF, Audible, MP3, WAV, VBR Display: 1.5-inch" . <http://wdc.org/resource/2> <http://schema.org/Product/Brand> "Apple" . <http://wdc.org/resource/2> <http://schema.org/Product/Model> "iPod nano" . <http://wdc.org/resource/2> <http://schema.org/Product/Storage> "8GB" . <http://wdc.org/resource/2> <http://schema.org/Product/Display> "1.5-inch" . 22Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  23. 23. Silk Free Text Preprocessor by Example <http://wdc.org/resource/2> <http://schema.org/Product/title> "Apple iPod nano (8 GB, 6th generation, Graphite)" . <http://wdc.org/resource/2> <http://schema.org/Product/description> "Memory size: 8GB. Colour: Graphite Generation: 6th generation. Memory type: Integrated. Weight: 21.1g. Radio: With Radio. Audio/Video formats: AAC, AIFF, Audible, MP3, WAV, VBR Display: 1.5-inch" . <http://wdc.org/resource/2> <http://schema.org/Product/Brand> "Apple" . <http://wdc.org/resource/2> <http://schema.org/Product/Model> "iPod nano" . <http://wdc.org/resource/2> <http://schema.org/Product/Storage> "8GB" . <http://wdc.org/resource/2> <http://schema.org/Product/Display> "1.5-inch" . Free Text Preprocessor Specification 23Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  24. 24. Extractors – Bag-of-words • Learning • Creating a list of words for every feature in the training set • Extraction • Matching tokens against the learned lists • Pros • Good for extracting nominal and numerical (with units of measurement) attributes • Cons • Bad for extracting multi-token values • Inconclusive for values that refer to more than one feature Brand Storage Display Samsung Benq Apple Cannon … 64 GB megabytes 512GB … 42-inch 3.5-inches Inches 15.24cm … 24Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  25. 25. Extractors – Feature-Value Pairs Learns feature-value pairs from the structured data Extraction • Tagging – taking n-grams up to 4 and matching against the values from the training set • Parsing – taking the combination of feature-value pairs that best describes an object from the training dataset • Pros • Extracting multi-token values Cons • Inconclusive for values that refer to more than one feature <Model, Asus EEE 10.1 Inch> <Processor, 1.66 GHz Intel Atom N445> <Display, 10.1-inches> .. <Model, Panasonic Viera> <Display, 42-Inch> 25Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  26. 26. Extractors – Manual Configuration Manually configure features and extraction methods 1. Regular expressions • E.g. Processor - d*.?d+GHz 2. Dictionary search • E.g. Dictionary of brands (Samsung, Panasonic, Lenovo, Apple) • Pros • Extraction process can be fine-tuned according to the data • Good solution when no training (structured) data are available • Cons • Needs domain knowledge • Non-trivial to efficiently pick extraction methods manually 26Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  27. 27. Extraction Experiments • Dataset for extraction 5,000 electronic products from WDC • Training dataset (structured data) – 20 electronics products Amazon dataset 27Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  28. 28. Extraction Accuracy Brand Model Storage Display Processor Dimension iPod Nano .92 .98 .86 .49 .12 .78 Galaxy SII .72 .87 .89 .81 .40 .91 GalaxyTab 7.7 .80 .92 .89 .85 .72 .93 Ixus 120IS 1 .96 N/A .89 N/A .56 Vaio VPC .99 .65 .81 .77 .73 .32 Viera 42 .95 .72 N/A .82 N/A .64 Sandisk 1 1 .85 N/A N/A .31 • Extraction using Combination configuration (bag-of-words for Brand, Storage and Display; feature-value pairs for Model and Dimension; custom regular expression for the Processor) 28Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  29. 29. Outline 1. HTML-embedded Data on the Web 2. The Data Integration Pipeline 1. Microdata extraction 2. Classification 3. Feature extraction 4. Identity resolution 5. Data Fusion 3. Conclusions 29Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  30. 30. Identity Resolution • We used Silk – a tool for discovering relationships between data items within different linked data sources Provides a expressive language for defining linkage rules Uses genetic programming to learn linkage rules Has shown high performance on various datasets https://www.assembla.com/spaces/silk/wiki/Home 30Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  31. 31. Identity Resolution Experiments • Gold standard: 5,000 links manually annotated • 2,500 positive/2,500 negative • 20 electronics products Amazon dataset (reference set) • Experiment on 5 configurations – Baseline (no feature extraction step) – Bag-of-words – Feature-value pairs – Manual configuration – Combinations 31Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  32. 32. Silk Output: Learned Linkage Rule :Property wdc:Model :Transform lowerCase :Comparison func = Levensthein threhold = 1.134 :Property wdc:Display :Aggregation func= max :Aggregation func= average :Transform lowerCase :Property amazon:Model :Transform tokenize :Transform tokenize :Property amazon:Display :Comparison func = Jaccard threhold = 0.23 :Comparison func = Jaccard threhold = 0.02 :Property amazon:Storage :Property wdc:Storage 32Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  33. 33. Identity Resolution Results Precision % Recall % F-Measure % Baseline 69 90 78.1 Bag-of-words 75 82 77.9 Feature-value pairs 80 77 78.4 Custom 82 80 80.9 Combination 85 80 82.4 33Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  34. 34. Outline 1. HTML-embedded Data on the Web 2. The Data Integration Pipeline 1. Microdata extraction 2. Classification 3. Feature extraction 4. Identity resolution 5. Data Fusion 3. Conclusions 34Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  35. 35. Data Fusion • Input: clusters of products after identity resolution • Properties worth fusing/combining – AggregateRating and Review 35Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  36. 36. Fusion Results Product Offers Reviews Ratings iPod Nano 8GB 829 84 0 iPhone 4 16GB 624 35 52 Sony Ericsson Xperia Mini 450 31 12 iPad 16GB 423 40 48 Motorola XOOM 32GB 270 12 0 Samsun Galaxy SII 142 8 0 36Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  37. 37. Conclusions • By using Microdata, thousands of websites help us to understand their content • We have implemented the 5-step data integration pipeline – From Microdata markup to an integrated dataset • A newly introduced feature extraction step is crucial for the precision of data integration – Identity resolution precision increases from 69% to 85% • Future work – Automatically learning regular expressions – Automatically discovering combinations of extractors 37Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
  38. 38. Questions? 38Integrating Product Data from Microdata Markup. Petar Petrovski, Volha Bryl, Chris Bizer
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