3 level model for semantic screen scraping of web resources. Also Best practices, famous tools and a case study for web scraping is discussed.

1. SEMANTIC
SCRAPING
MODEL FOR WEB RESOURCES
by
SHYJAL RAAZI

2. AGENDA
 What is scraping
 Why we scrape
 Where it is used
 More on XPATH and RDF
 Levels of scraping
1. Scraping service level
2. Syntactic level
3. Semantic level
 Case study
 Tools
 Best practices
 Challenges

3. Scraping :
converting unstructured documents into structured
information or simply web content mining

4. More..
 Any program that retrieves structured data from the web, and then
transforms it to conform with a different structure.
 Isn’t that just ETL? (extract, transform, load), or cant we regex.
 Nope. because ETL implies that there are rules and expectations, and
these two things don’t exist in the world of web data. They can change
the structure of their dataset without telling you, or even take the
dataset down.

5. Why Scraping?
Data is usually not in format we expect.
 Get what you are interested in.
Web pages contain wealth of information (text form), designed mostly
for human Consumption
 Interfacing with 3rd party that have no API access
 Websites are more accurate than API’s
 No IP rate limiting
 Anonymous access

6. Where it is used
 Developers use it to interface API
 Mining Web content
 Online adverts
 RSS readers
 Web browsers

7. Related terms
 XML : A markup language that defines a set of rules for encoding
documents in a format that is both human and machine readable
 RSS : RSS feeds enable web publishers provide summary/update of data
automatically. It can be used for receiving timely updates from news or blog
websites.
 RDF :The Resource Description Framework (RDF) is a W3C standard for
describing Web resources, such as the title, author, modification date,
content, and copyright information of a Web page.
 XPATH :is a query language used to navigate through elements and
attributes in an XML document.

8. More on Resource Description Framework
• RDF is a framework for describing resources on the web.
• RDF is designed to be read and understood by computers
• Similar to entity relationship model.
• RDF is written in XML.
• RDF is based upon the idea of making statements about resources (in
particular web resources) in the form of subject-predicate-object
expressions.
• The notion "The sky has the color blue" in RDF is as the triple:
a subject denoting "the sky", a predicate denoting "has the color",
and an object denoting "blue”
• A collection of RDF statements intrinsically represents a labeled,
directed multi-graph

9. The objects are:
• "Eric Miller"(predicate : "whose
name is"),
• em@w3.org (predicate "whose email
address is"),
• "Dr." (predicate : "whose title is").
The subject is a URI.
The predicates also have URIs. For
example, the URI for each predicate:
• "whose name is"
is http://www.w3.org/2000/10/swap
/pim/contact#fullName,
• "whose email address is"
is http://www.w3.org/2000/10/swap
/pim/contact#mailbox,
• "whose title is"
is http://www.w3.org/2000/10/swap
/pim/contact#personalTitle.

10. More on XPATH
• XPATH uses path expressions to select nodes or node-sets in an XML
document.
• XPATH includes over 100 built-in functions. There are functions for
string values, numeric values, date manipulation and time comparison,
node and Name manipulation, sequence, Boolean values, and more.
<?xml version="1.0" encoding="ISO-8859-1"?>
<bookstore>
<book>
<title lang="en">Harry Potter</title>
<author>J K. Rowling</author>
</book>
</bookstore>
<bookstore> (root element node)
<author>J K. Rowling</author> (element node)
lang="en" (attribute node)
J K. Rowling (atomic value)

11. <bookstore>
<book category="COOKING">
<title lang="en">Italian</title>
<author>Giada </author>
<year>2005</year>
<price>30.00</price>
</book>
• Select all the titles
“/bookstore/book/title”
• Select price nodes with price>35
“/bookstore/book[price>35]/price”
<book category="CHILDREN">
• Select the title of the first book
<title lang="en">Harry Potter</title>
“/bookstore/book[1]/title”
<author>J K. Rowling</author>
<year>2005</year>
<price>29.99</price>
</book>
</bookstore>

12. SCRAPING Framework
Model considers three level abstraction for an integrated model
for semantic scraping

13. #1 : Syntactic scraping level.
This level gives support to the interpretation to the semantic scraping
model. It defines the required technologies to extract data from web
resources. Wrapping and Extraction techniques such as DOM selectors
are defined at this level for their use by the semantic scraping level.

14. Techniques in syntactic level
 Content Style Sheet selectors.
 XPATH selectors.
 URI patterns.
 Visual selectors.

15. Syntactic cont..
Selectors at the syntactic scraping level allow to identify HTML nodes.
Either a generic element or an identified element can be selected
using these techniques. Their semantics are defined in the next
scraping level, allowing to map data in HTML fragments to RDF
resources.

16. #2 : Semantic scraping level.
This level defines a model that maps HTML fragments to semantic
web resources. By using this model to define the mapping of a set of
web resources, the data from the web is made available as
knowledge base to scraping services.
• Apply the model to the definition of extractors of web resources.
• The proposed vocabulary serves as link between HTML document’s
data and RDF data by defining a model for scraping agents. With this
RDF model, it is possible to build an RDF graph of HTML nodes given
an HTML document, and connects the top and lowest levels in the
scraping framework to the semantic scraping level.

17. Semantic scraping cont..

18. #3 : Scraping service level.
This level comprises services that make use of semantic data
extracted from un annotated web resources. Possible services that
benefit from using this kind of data can be opinion miners,
recommenders, mashups that index and filter pieces of news, etc.
Scraping technologies allow getting wider access to data from
the web for these kinds of services.

19. Make service
 Scraping data identification.
 Data modelling.
 Extractor generalization.

20. Case study
Scenario : has the goal of showing the most commented sports news
on a map, according to the place they were taken.

21. Challenges :
• The lack of semantic annotations in the sports news web sites,
• The potential semantic mismatch among these sites
• The potential structural mismatch among sites.
• Sites does not provide microformats, and do not include some
relevant information in their RSS feeds, such as location, users’
comments or ratings
Approach :
• Defining the data schema to be extracted from selected sports news
web sites,
• Defining and implementing these Extractors/Scrapers.
Recursive access is needed for some resources. For instance, a piece of
news may show up as a title and a brief summary in a newspaper’s
homepage, but offers the whole content (including location, authors,
and more) in its own URL.
• Defining the mashup by specifying the sources

22. Case study visualization

23. Other scrape tools
 Beautiful soup
 Mechanize
 Firefinder
 http://open.dapper.net by yahoo

26. Visual scraper : firefinder

27. Best practices

28. #1:
Approximate
web
behavior

29. #2
Batch jobs
in non peak
hours

30. Challenges
 External sites can change without warning.
Figuring out the frequency is difficult, and changes can break scrapers easily
 Bad HTTP status codes
Cookie check, Check referrer
 Messy HTML markup
 Data Piracy

31. Conclusion
• With plain text, we give ourselves the ability to manipulate knowledge,
both manually and programmatically, using virtually every tool at our
disposal.
• The problem behind web information extraction and screen scraping has
been outlined, while the main approaches to it have been summarized.
The lack of an integrated framework for scraping data from the web has
been identified as a problem, and presents a framework that tries to fill
this gap.
• Developer can have an API for each and every websites.

32. References
 A SEMANTIC SCRAPING MODEL FOR WEB RESOURCES
By Jose´ Ignacio Ferna´ndez-Villamor, Jacobo Blasco-Garc´ıa, Carlos A´ . Iglesias, Garijo

33. THANK YOU