The document discusses the Semantic Web and linked data. It describes the Semantic Web as a way to publish information that is easier for machines to process by adding meaning through common formats and shared schemas. It outlines key Semantic Web standards like RDF, OWL, and SPARQL. The document also discusses how data is published on the Semantic Web through linked data, metadata in HTML, SPARQL endpoints, and feeds. It provides examples of publishing and consuming RDF data on the Semantic Web.

1. Data on the Semantic
Web
Peter Mika
Senior Research Scientist
Yahoo! Research

2. Vague, but exciting… Berners-Lee and the dawn of the Web
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3. Semantic Web
• Publish information in a way that is easier to process for machines
• Web of Data instead of Web of Documents
• Two main architectural challenges
– A common format for sharing data
– Sharing the meaning of data
• Through social means (shared schemas)
• By using powerful schema languages
• Semantic Web standards from W3C
– Languages (RDF, OWL, RIF)
– Serializations (RDF/XML, RDFa)
– Protocols (SPARQL, HTTP)
• Semantic Web research into knowledge representation and
reasoning, data integration, data quality and many other topics
• Community efforts to publish data and develop schemas
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4. Resource Description Framework (RDF)
• Each resource (thing, entity) is identified by a URI
– Globally unique identifiers
• RDF represents knowledge as a set of triples
– Each triple is a single fact about the entity (an attribute or a
relationship)
• A set of triples forms an RDF graph
RDF document
type foaf:Person
example:roi name
“Roi Blanco”
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5. Linking across the Web
Roi’s homepage Friend-of-a-Friend ontology
type
example:roi foaf:Person
name
“Roi Blanco” knows
sameAs
Yahoo!’s website
type
worksWith
#roi2 #peter
email
“pmika@yahoo-inc.com”
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6. Vocabularies (ontologies)
• Ontologies are collections of classes and properties used to
describe objects in a particular domain
– OWL (the Web Ontology Language) is the standard ontology
language
– OWL has an RDF serialization: ontologies are part of the
Semantic Web
• Classes can be described by sub- and superclasses,
required properties
– Class membership in RDF is expressed using the rdf:type
property
– An instance can have multiple classes (types)
– A class can have multiple superclasses
• Properties can be described by their domain, range,
cardinalities, etc.
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7. Example: schema.org
• Agreement on a shared set of schemas for common types of
web content
– Bing, Google, and Yahoo! as initial supporters
– Similar in intent to sitemaps.org (2006)
• Use a single format to communicate the same information to all
three search engines
• Support for microdata
• schema.org covers areas of interest to all search engines
– Business listings (local), creative works (video), recipes,
reviews
– User defined extensions
• Each search engine continues to develop its products
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8. Documentation and OWL ontology
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9. Sources of data

10. Data on the Web
• Most web pages on the Web are generated from structured
data
– Data is stored in relational databases (typically)
– Queried through web forms
– Presented as tables or simply as unstructured text
• The structure and semantics (meaning) of the data is not
directly accessible to search engines
• Two solutions
– Extraction using Information Extraction (IE) techniques
(implicit metadata)
• Supervised vs. unsupervised methods
– Relying on publishers to expose structured data using standard
Semantic Web formats (explicit metadata)
• Particularly interesting for long tail content
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11. Information Extraction methods
• Natural Entity Recognition (NER) and Disambiguation
(NED)
• OpenCalais, Zemanta API, Dbpedia Spotlight
• Yahoo! Placemaker
• Extraction of structured data from text
– Yago system (demo)
• Exploiting patterns in web page structure
– Dapper
– ScraperWiki
• Extraction from HTML tables
– Google Squared (deprecated)
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12. Publishing and consuming data on the Semantic Web
• Publishing data involves
– Deciding in which format to publish your data
– Deciding which schema (ontology, vocabulary) to use
• OR you can create a new schema and publish it as well
• Multiple ways of publishing RDF data:
1. Linked Data
2. Metadata in HTML
3. SPARQL endpoints
4. Feeds, e.g. OData
Note: you may implement more than one
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13. Option 1: Linked Data
• A web of RDF documents in parallel to the current Web
– Most often implemented as wrappers around databases or APIs
• The four rules of Linked Data:
– Use URIs to identify things.
– Use HTTP URIs so that these things can be referred to and
looked up ("dereference") by people and user agents.
– Provide useful information about the thing when its URI is
dereferenced, using standard formats such as RDF-XML.
– Include links to other, related URIs in the exposed data to
improve discovery of other related information on the Web.
..
#PeterM
“Peter Mika”
“Budapest”
.. “Peter Mika”
..
#PeterM
“Peter Mika”
“Budapest”
label
#Bud “2,000,000”
“Budapest” label
#PeterM born
label “2,000,000” #Hun
label
#Bud population
“2,000,000”
#Bud label
born capital-of
born
label #Hun
#Hun population
population
capital-of
capital-of
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14. Option 1: Linked Data
• Advantages:
– No change to the publishing of the HTML documents
– Data can be published by third party (e.g. Dbpedia)
• Disadvantages:
– Web servers need to be configured to properly handle URIs that
identify concepts instead of documents
– Not favored by search engines
• Lack of use cases
• Crawling needs to be changed
• Authority is difficult to determine
• Tools
– Triple stores (Virtuoso, Oracle etc.) and front-ends (Pubby)
– RDB-to-RDF mappers (e.g. D2RQ, Triplify)
– Validators (Vapour)
– Linked Data browsers (many)
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15. Growth of Linked Data
• Community effort to (re)publish open datasets as Linked
Data
– In particular, scientific and government datasets
– see linkeddata.org, the Data Hub
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16. Option 2: Metadata in HTML
• Using microformats, RDFa, Microdata (more later)
• Advantages:
– Data and document are always in sync
Peter Mika
Peter Mika
– Browser plug-in friendly was born
was born
in
in
– Search engine friendly Budapest.
Budapest.
– Copy-paste friendly
“Peter Mika”
“Budapest”
#PeterM
label “2,000,000”
#Bud
label
•
born
Tools:
#Hun
population
capital-of
– Any23 (Anything to Triples) Peter Mika
Peter Mika
– RDFaCE was born
was born
in
in
– RDFa Distiller Budapest.
Budapest. “Peter Mika”
“Budapest”
#PeterM
label “2,000,000”
#Bud
label
born
#Hun
population
capital-of
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17. Example: Facebook’s Open Graph Protocol
• RDF vocabulary to be used in conjunction with RDFa
– Simplify the work of developers by restricting the freedom in RDFa
• Activities, Businesses, Groups, Organizations, People, Places,
Products and Entertainment
• Only HTML <head> accepted
• http://opengraphprotocol.org/
<html xmlns:og="http://opengraphprotocol.org/schema/">
<head>
<title>The Rock (1996)</title>
<meta property="og:title" content="The Rock" />
<meta property="og:type" content="movie" />
<meta property="og:url"
content="http://www.imdb.com/title/tt0117500/" />
<meta property="og:image" content="http://ia.media-
imdb.com/images/rock.jpg" /> …
</head> ... - 18 -

18. Current state of metadata on the Web
• 31% of webpages, 5% of domains contain some
metadata
– Analysis of the Bing Crawl (US crawl, January, 2012)
– RDFa is most common format
• By URL: 25% RDFa, 7% microdata, 9% microformat
• By eTLD (PLD): 4% RDFa, 0.3% microdata, 5.4% microformat
– Adoption is stronger among large publishers
• Especially for RDFa and microdata
• See also
– P. Mika, T. Potter. Metadata Statistics for a Large Web Corpus,
LDOW 2012
– H.Mühleisen, C.Bizer.Web
Data Commons - Extracting Structured Data from Two Large Web Corpo
, LDOW 2012
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19. Exponential growth in RDFa data
Another five-fold increase
Another five-fold increase
between October 2010 and
between October 2010 and
January, 2012
January, 2012
Five-fold increase
Five-fold increase
between March, 2009 and
between March, 2009 and
October, 2010
October, 2010
Percentage of URLs with embedded metadata in various formats
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20. Option 3: SPARQL endpoints
• An API for accessing RDF databases on the Web
– A query language and an HTTP protocol “Peter Mika”
“Budapest”
#PeterM
• Advantages: born
label
#Bud
label
“2,000,000”
#Hun
population
– Flexible access: make any query you want
capital-of
– Also possible to expose a traditional RDBMs via a wrapper
• Disadvantages:
– For the publisher: cost of supporting arbitrary queries
– For the search engine: discovery of SPARQL servers is unsolved
• Tools:
– Triple stores
• Sesame, Jena, OWLIM, Redland, Oracle, Virtuoso, Stardog etc.
– RDB-to-RDF mappers such as D2RQ and Triplify
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21. Example: Dbpedia
• demo
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22. Crawling the Semantic Web
• Linked Data
– Similar to HTML crawling, but the the crawler needs to parse
RDF/XML (and others) to extract URIs to be crawled
– Semantic Sitemap/VOID descriptions
• RDFa
– Same as HTML crawling, but data is extracted after crawling
– Mika et al. Investigating the Semantic Gap through Query Log
Analysis, ISWC 2010.
• SPARQL endpoints
– Endpoints are not linked, need to be discovered by other
means
– Semantic Sitemap/VOID descriptions
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23. Data fusion
• Ontology (schema) matching
– Widely studied in Semantic Web research
• ontologymatching.org
• Entity resolution
– Finding links between datasets
– Tools: SILK, LIMES
• Blending
– Merging objects that represent the same real world entity and
reconciling information from multiple sources
• Cleaning
– Google Refine
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24. More info
• Ideas for hacks
– http://challenge.semanticweb.org/
– http://iswc2011.semanticweb.org/calls/linked-data-a-thon/
• Book
– Segaran, Evans and Taylor. Programming the Semantic Web.
O’Reilly, 2009.
• More tools
– Exhibit: faceted browsing and other visualizations
– http://www.dajobe.org/talks/200906-semtech-open/
– LOD2 stack (stack.lod2.eu)
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