Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data

DBpedia and the Emerging Web ofLinkedDataSören Auer

2000 Mathematics and Computer Science studies inHagen, Dresden and Екатеринбург
Managing director of adVIS GmbH – SME focusedon Web-Application and Content Management technology
IT consultant for various companies (T-Mobile AG, RDL Corp., Science Computing AG)
2006 doctorate in Information Systems / Computer Science atUniversität Leipzig
2006-2008 post-doctoral researcher at the DB Group at University of Pennsylvania (USA)
Head of AKSW research group – DBpedia, OntoWiki, LinkedGeoData, Triplify
Research interests: Information Systems, Database and Web Technologies, Semantic Web and Knowledge Engineering, Adaptive Methodologies, HCI, E-Science, Digital Libraries
Coordinator of the EU FP7 IP Project “LOD2 – Creating Knowledge out of Interlinked Data”
Work as expert for W3C, EU FP6/FP7/CIP, University City Keystone Innovation Zone, Swiss National Science Foundation
Dr. SörenAuer

The Vision & Big Picture
Linked Data 101
The LinkedData Life-cycle
Agenda

1. Reasoningdoes not scale on the Web
IR / one dimensional indexingscales (Google)
Next stepconjunctivequerying (OWL-QL?, dynamicscale-out / clustering)
Web scalable DL reasoningis out-of-sight (maybefragment, fuzzyreasoninghassomechances)
2. Ifitwouldscaleitwould not beaffordable
“What is the only former Yugoslav republic in theEuropean Union?”
2880 POWER7 cores, 16 Terabytes memory, 4 Terabytes clustered storage (IBM Watson) still can not answer this question
WhytheSemantic Web won‘twork (soon)

Problem: Try to search for these things on the current Web:
Apartments near German-Russian bilingual childcare in Berlin.
ERP service providers with offices in Vienna and London.
Researchers working on multimedia topics in Eastern Europe.
Informationis available on the Web, but opaque to current search.
Why do we need the Data Web?
Solution: complement text on Web pages with structured linked open data & intelligently combine/integrate such structured information from different sources:
Search engine
HTML
HTML
RDF
RDF
Web server
Web server
Web server
Web server
berlin.de
Has everything about childcare in Berlin.
Immobilienscout.de
Knows all about real estate offers in Germany
DB
DB

From the Document Web to theSemantic Data Web
Semantic Web(Vision 1998, starting ???)
Reasoning
Logic, Rules
Trust
Data Web (since 2006)
URI de-referencability
Web Data integration
RDF serializations
Social Web (since 2003)
Folksonomies/Tagging
Reputation, sharing
Groups, relationships
Web (since 1992)
HTTP
HTML/CSS/JavaScript

Web 1.0 Web 2.0 Web 3.0
Many Web sitescontaining unstructured,textual content
Few large Web sites are specialized onspecific content types
Many Web sites containing & semantically syndicating arbitrarily structured content
Video
Pictures
Encyclopedicarticles
+
+

The Long Tail of Information Domains
Pictures
The Long Tail by Chris Anderson (Wired, Oct. ´04) adopted to information domains
Recipes
News
Video
Calendar
Popularity
SemWeb supported structured content
Requirements-Engineering
Talentmanagement
Special interest
communities
Itinerary ofKing George
Gene
sequences
…
…
…
…
Currently supportedstructuredcontent types
Not or insufficiently supported content types

1. Uses RDF Data Model
Linked Data in a Nutshell
3.10.2011
starts
organizes
SBC
SBBD2011
Florianopolis
takesPlaceIn
2. Is serialised in triples:
SBC organizes SBBD2011
SBBD2011 starts “20111003”^^xsd:date
SBBD2011 takesPlaceAt Florianopolis
3. Uses Content-negotiation

The emerging Web of Data
interlink
2009
2007
SILK
DXX Engine
fuse
create
2008
poolparty
SemMF
OntoWiki
2008
Sigma
WiQA
2008
2008
ORE
repair
classify
Virtouso
2009
DL-Learner
MonetDB
Sindice
enrich

Conceptual LevelData Access and Integration
Data Warehousing
Based on extract, transform load (ETL)
Global-As-View (GAV)
Data Web
URIs as entity identifiers
HTTP as data accessprotocol
Local-As-View (LAV)
Enterprise Information Integration
sets of heterogeneous data sources appear as a single, homogeneous data source
Research
Mediators
Ontology-based
P2P
Web service-based
Data Integration
Object-relational mappings (ORM)
NeXT’s EOF / WebObjects
ADO.NET Entity Framework
Hibernate
Procedural APIs
ODBC
JDBC
Query Languages
Datalog, SQL
SPARQL
XPATH/XQuery
Linked Data
de-referencable URIs
RDF serialization formats
Data Access
Others
XML, hierachical, tree, graph-oriented DBMS
RDBMS
Organize data in relations, rows, cells
Oracle, DB2, MS-SQL
Column-oriented DBMS
Collocates column values rather than row values
Vertica, C-Store, MonetDB
Triple/Quad Stores
RDF data model
Virtuoso, Oracle, Sesame
Data Models
Entity-attribute-value (EAV)
HELP medical record system, TrialDB

Linked Data 101(based on Michael Hausenblas‘ slides)
Agenda

Orientation

Linked Data 101
Linked Data provides a standardised API for:
Data and metadata discovery
Data integration
Distributed query

Linked Data principles
Use URIs to identify the “things” in your data
Use http:// URIs so people (and machines) can look them up on the web
When a URI is looked up, return a description ofthe thing (in RDF format)
Include links to related things
http://www.w3.org/DesignIssues/LinkedData.html

Linked Data principles
They are principles, not implementation advices
Not humans or machines but humans and machines!
Content negotiation (e.g. HTML and RDF/XML)
HTML+ RDFa
Metcalfe’s Lawhttp://en.wikipedia.org/wiki/Metcalfe%27s_law

Linked Data example
17

HTTP URIs
A Uniform Resource Identifier (URI) is a compact sequence of characters that identifies an abstract or physical resource. [RFC3986]
Syntax
URI = scheme ":" hier-part [ "?" query ] [ "#" fragment ]
Example
foo://example.com:8042/over/there?name=ferret#nose
_/ _________________/_________/ __________/ __/
| | | | |
scheme authority path query fragment

HTTP URIs
URI references
An RDF URI reference is a Unicode string does not contain any control characters (#x00 - #x1F, #x7F-#x9F) and would produce a valid URI character sequence representing an absolute URI when subjected to an UTF-8 encoding along with %-escaping non-US-ASCII octets.
Qualified Names (QNames)
XML’s way to allow namespaced elements/attributes as of QName = Prefix ‘:‘ LocalPart
Compact URIs (CURIEs)
Generic, abbreviated syntax for expressing URIs

HTTP
The Hypertext Transfer Protocol (HTTP) is an application-level protocol for distributed, collaborative, hypermedia information systems.
It is a generic, stateless, protocol which can be used for many tasks beyond its use for hypertext, such as name servers and distributed object management systems, through extension of its request methods, error codes and headers. A feature of HTTP is the typing and negotiation of data representation, allowing systems to be built independently of the data being transferred.

HTTP
HTTP messages consist of requests from client to server and responses from server to client
Set of methods is predefined
GET
POST
PUT
DELETE
HEAD
(OPTIONS)

HTTP
Status codes
Informational 1xx, provisional response, (100 Continue)
Successful 2xx, request successfully received, understood, and accepted (201 Created)
Redirection 3xx, further action needs to be taken by user agent to fulfill the request (301 Moved Permanently)
Client Error 4xx, client erred (405 Method Not Allowed)
Server Error 5xx, server encountered an unexpected condition (501 Not Implemented)

HTTP
GET /html/rfc2616 HTTP/1.1
Host: tools.ietf.org
User-Agent: Mozilla/5.0
Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8
HTTP/1.x 200 OK
Date: Thu, 05 Mar 2009 08:17:33 GMT
Server: Apache/2.2.11
Content-Location: rfc2616.html
Last-Modified: Tue, 20 Jan 2009 09:16:04 GMT
Content-Type: text/html; charset=UTF-8
REQUEST
RESPONSE

HTTP
Content Negotiation: selecting representation for a given response when multiple representations available
Three types of CN: server-driven, agent-driven CN, transparent CN
Example:
curl -I -H "Accept: application/rdf+xml" http://dbpedia.org/resource/Galway
HTTP/1.1 303 See Other
Content-Type: application/rdf+xml
Location: http://dbpedia.org/data/Galway.rdf

HTTP
Caching (see Cache–Control header field) is essential for scalabilityhttp://webofdata.wordpress.com/2009/11/23/linked-open-data-http-caching/
HTTPbis IETF WG chaired by Mark Nottingham, mainly about: patches, clarifications, deprecate non-used features, documentation of security properties

REST - HTTP
Representational State Transfer (REST)
resource intended conceptual target of a hypertext reference
resource identifier URL, URN
representation HTML document, JPEG image
representation media type, last-modified time
metadata
resource source link, alternates, varymetadata
control data if-modified-since, cache-control
http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htmhttp://webofdata.wordpress.com/2009/10/09/linked-data-for-restafarians/

Web's Standard Retrieval Algorithm
parse URI and find HTTP protocol
look up DNS name to determine the associated IP address
open a TCP stream to port 80 at the IP address determined above
format an HTTP GET request for resource and sends that to the server
read response from the server
from the status code (200) determine that a representation of the resource is available
inspect the returned Content-Type
pass the entity-body to its HTML rendering engine
http://www.w3.org/2001/tag/doc/selfDescribingDocuments

RDF
A data model - directed, labeled graph
Triple: (subject predicate object)
subject … URIref or bNode
predicate … URIref
object … URIref or bNode or literal

RDF Triple
Inspired by linguistic categories
Allowed usage:Subject: URI or blank nodePredicate: URI (also called properties)Object : URI or blank nodes or literal
isMayorOf
Leipzig
Burkhard Jung
Predicate
Object
Subject

Example RDF Graph
Leipzig
hasAreaCode
longitude
0341
12.3833
latitude
locatedIn
hasMayor
51.3333
isMayorOf
Saxony
Burkhard Jung
born
locatedIn
1958-03-07
isMemberOf
Germany
Social Democratic Party

Literals
Representation of data values
Serialization as strings
Interpretation based on the datatype
Literals without Datatype are treated as strings
longitude
12.3833
Leipzig
latitude
51.3333
isMayorOf
hasMayor
born
1958-03-07
Burkhard Jung

RDF Serialization
N3: "Notation 3" - extensive formalism
N-Triples: part of N3
Quelle:http://www.w3.org/DesignIssues/Notation3.html
Turtle: Extension of N-Triples (shortcuts)

Turtle Syntax
URIs in angle brackets
Literals in quotes
Triples separated by dot
Whitespace is ignored
33

Turtle Syntax: Shortcuts
http://dbpedia.org/resource/Leipzig http://dbpedia.org/property/hasMayor http://dbpedia.org/resource/Burkhard_Jung ;
http://www.w3.org/2000/01/rdf-schema#label "Leipzig"@de ;
http://www.w3.org/2003/01/geo/wgs84_pos#lat "51.333332"^^xsd:float ;
http://www.w3.org/2003/01/geo/wgs84_pos#lon "12.383333"^^xsd:float .
Shortcuts for namespace prefixes:
@prefixrdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefixrdfs:<http://www.w3.org/2000/01/rdf-schema#> .
@prefixdbp:<http://dbpedia.org/resource/> .
@prefixdbpp:<http://dbpedia.org/property/> .
@prefixgeo:<http://www.w3.org/2003/01/geo/wgs84_pos#> .
dbp:Leipzigdbpp:hasMayordbp:Burkhard_Jung .
dbp:Leipzigrdfs:label "Leipzig"@de .
dbp:Leipziggeo:lat "51.333332"^^xsd:float .
dbp:Leipziggeo:lon "12.383333"^^xsd:float .

Turtle Syntax: Shortcuts
Group triples with same subject using “;” instead of “.”:
@prefixrdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefixrdfs="http://www.w3.org/2000/01/rdf-schema#> .
@prefixdbp="http://dbpedia.org/resource/> .
@prefixdbpp="http://dbpedia.org/property/> .
@prefixgeo="http://www.w3.org/2003/01/geo/wgs84_pos#> .
dbp:Leipzigdbpp:hasMayordbp:Burkhard_Jung ;
rdfs:label "Leipzig"@de ;
geo:lat "51.333332"^^xsd:float ;
geo:lon "12.383333"^^xsd:float .
also Triple with same subject and predicate:
@prefixdbp="http://dbpedia.org/resource/> .
@prefixdbpp="http://dbpedia.org/property/> .
dbp:Leipzigdbp:locatedIndbp:Saxony, dbp:Germany;
dbpp:hasMayordbp:Burkhard_Jung .

XML-Syntax von RDF
Turtle intuitively readable and machine processable
but: better tool support and programming libraries for XML
<?xmlversion="1.0"?>
<rdf:RDFxmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:dbpp="http://dbpedia.org/property/"
xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#">
<rdf:Descriptionrdf:about="http://dbpedia.org/resource/Leipzig">
<property:hasMayor
rdf:resource="http://dbpedia.org/resource/Burkhard_Jung" />
<rdfs:labelxml:lang="de">Leipzig</rdfs:label>
<geo:latrdf:datatype="float">51.3333</geo:lat>
<geo:lonrdf:datatype="float">12.3833</geo:lon>
</rdf:Description>
</rdf:RDF>

RDF/JSON
JSON = JavaScript Object Notation
Compact formatfordataexchangebetweenapplications
JSON documentsare valid JavaScript
Programminglanguageindependent, sinceparserexistfor all popularprogramminglanguages
Lessoverheadwhenparsingandserialisingthan XML
{ "S" : { "P" : [ O ] } }
Subject: URI, BNode
Predicate: URI
Object:
Type: „URI“, „Literal“ or „bnode“
Value:datavalue
Lang: language tag
Datatype: URI ofthedatatype.

JSON Example
{
"http://dbpedia.org/resource/Leipzig" : {
"http://dbpedia.org/property/hasMayor":
[ { "type":"uri", "value":"http://dbpedia.org/resource/Burkhard_Jung" } ],
"http://www.w3.org/2000/01/rdf-schema#label":
[ { "type":"literal", "value":"Leipzig", "lang":"en" } ] ,
"http://www.w3.org/2003/01/geo/wgs84_pos#lat":
[ { "type":"literal", "value":"51.3333",
"datatype":"http://www.w3.org/2001/XMLSchema#float" } ]
"http://www.w3.org/2003/01/geo/wgs84_pos#lon":
% [ { "type":"literal", "value":"12.3833",
"datatype":"http://www.w3.org/2001/XMLSchema#float" } ]
}
}

RDFa Syntax
RDFa = Resource Description Framework – in –attributes
Embedding RDF in XHTML
UTF-8 and UTF-16, since Extension of XML based XHTML
Due toembedding in HTML moreoverheadthanotherserialisations
Lessreadable
<?xmlversion="1.0" encoding="UTF-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML+RDFa 1.0//EN"
"http://www.w3.org/MarkUp/DTD/xhtml-rdfa-1.dtd">
<htmlversion="XHTML+RDFa 1.0" xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:dbpp="http://dbpedia.org/property/"
xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#">
<head><title>Leipzig</title></head>
<bodyabout="http://dbpedia.org/resource/Leipzig">
<h1 property="rdfs:label" xml:lang="de">Leipzig</h1>
<p>Leipzig is a city in Germany. Leipzig'smayoris
<a href="Burkhard_Jung" rel="dbpp:hasMayor">Burkhard Jung</a>. Itislocated
atlatitude <span property="geo:lat" datatype="xsd:float">51.3333</span>
andlongitude <span property="geo:lon" datatype="xsd:float">12.3833</span>.</p>
</body>
</html>

Vocabularies
Schema layer of RDF
Defines terms (classes and properties)
Typically RDFS or OWL family
Common vocabularies:
Dublin Core, SKOS
FOAF, SIOC, vCard
DOAP
Core Organization Ontology
VoID
http://www.slideshare.net/prototypo/introduction-to-linked-data-rdf-vocabularies

41
Vokabulare: Friend-of-a-Friend (FOAF)
defines classes and properties for representinginformation about people and theirrelationships
Soerenrdf:typefoaf:Person .
SoerencurrentProject http://OntoWiki.net .
Soerenfoaf:homepage http://aksw.org/Soeren .
Soerenfoaf:knows http://sembase.at/Tassilo .
Soeren foaf:sha1 09ac456515dee .

42
Vokabulare: SemanticallyInterlinked Online Communities.
Represent content from Blogs, Wikis, Forums, Mailinglists, Chats etc.

43
Vokabulare: Simple Knowledge Organization System (SKOS)
support the use of thesauri, classification schemes, subjectheading systems and taxonomies

Instance data
Instancesareassociatedwithoneorseveralclasses:
Boddingtonsrdf:type Ale .
Grafentrunkrdf:type Bock .
Hoegaardenrdf:type White .
Jeverrdf:type Pilsner .

The Linked Open Data cloud

Linked Open Data cloud
2009
2007
2008
2008
2010
2008
2008
2009
46

Linked Open Data cloud
Media
User-generated
Government
Publications
Cross-domain
Geo
Life sciences
http://lod-cloud.net/

LOD cloudstats
triples distribution
links distribution
http://lod-cloud.net/state/

TimBL’s 5-star plan for open data
★ Make your data available on the Web under an open license
★★ Make it available as structured data(Excel sheet instead of image scan of a table)
★★★ Use a non-proprietary format(CSV file instead of an Excel sheet)
★★★★ Use Linked Data format(URIs to identify things, RDF to represent data)
★★★★★ Link your data to other people’s data to provide context
More: http://lab.linkeddata.deri.ie/2010/star-scheme-by-example/

Why going for the 5th star?
Central Contractor Registration (CCR)
Geonames
http://webofdata.wordpress.com/2011/05/22/why-we-link/

Effort distribution
Fix Overall Data IntegrationEffort

Datasets
A dataset is a set of RDF triples that are published,
maintained or aggregated by a single provider

Linksets
An RDF link is an RDF triple whose subject and object are described in different datasets
A linksetis a collection of such RDF links between two datasets

Describing Datasets - VoID
General dataset metadata
Access metadata
Structural metadata
Describing linksets
Deployment and discovery of voiD files
http://www.w3.org/TR/void/

Dataset homepage
Publisher
Title and description
Categorisation
Licensing
Technical features

:DBpedia a void:Dataset ;
dcterms:title "DBpedia” ;
dcterms:description "RDF data extracted from Wikipedia” ;
dcterms:contributor :FU_Berlin ;
dcterms:contributor :Uni_Leipzig ;
dcterms:contributor :Openlink ;
dcterms:source <http://dbpedia.org/resource/Wikipedia> ;
void:feature <http://www.w3.org/ns/formats/RDF_XML> ;
dcterms:modified "2008-11-17"^^xsd:date .
:Geonames a void:Dataset ;
dcterms:subject <http://dbpedia.org/resource/Location> .
:GeoSpecies a void:Dataset ;
dcterms:license <http://creativecommons.org/licenses/by-sa/3.0/us/> .

Access metadata
SPARQL endpoints
RDF data dumps
Root resources
URI lookup endpoints
OpenSearch description documents

Access metadata
:exampleDSvoid:Dataset ;
void:sparqlEndpoint <http://example.org/sparql> ;
void:dataDump <http://example.org/dump1.rdf> ;
void:uriLookupEndpoint <http://api.example.org/search?qt=term> .

Structural metadata
Provides high-level information about the schema and internal structure of a dataset and can be helpful when exploring or querying datasets:
Example resources
Patterns for resource URIs
Vocabularies
Dataset partitions
Statistics

Structural metadata
:DBpedia a void:Dataset;
void:exampleResource <http://dbpedia.org/resource/Berlin> .
:LiveJournal a void:Dataset;
void:vocabulary <http://xmlns.com/foaf/0.1/> .
:DBpedia a void:Dataset;
void:classPartition [
void:classfoaf:Person;
void:entities 312000;
];
void:propertyPartition [
void:propertyfoaf:name;
void:triples 312000;
];
.

Describing linksets

Describing linksets
:DBpedia a void:Dataset ;
void:subset :DBpedia2Geonames .
:Geonames a void:Dataset .
:DBpedia2Geonames a void:Linkset ;
void:target :DBpedia ;
void:target :Geonames ;
void:linkPredicateowl:sameAs .

Deployment and discovery
Choosing URIs for datasets
Publishing a VoID file alongside a dataset
Turtle
RDFa
SPARQL Service Description Vocabularyhttp://www.w3.org/TR/sparql11-service-description/
Discovery (well-known URI), based on of RFC5758], registered with IANAhttp://www.example.com/.well-known/void

Consumption - Essentials
Linked Data provides for a global data-space with a uniform API (due to RDF as the data model)
Access methods
Dereference URIs via HTTP GET (RDF/XML, RDFa, etc.)
SPARQL (‘the SQL of RDF’)
Data dumps (RDF/XML, etc.)

Consumption - Technologies
Linked Data access mechanisms widely supported
all major platforms and languages (HTTP interface & RDF parsing), such as Java, Python, PHP, C/C++/.NET, etc.
Command line tools (curl, rapper, etc.)
Online tools
http://redbot.org/ (HTTP/low-level)
http://sindice.com/developers/inspector (RDF/data-level)
Structured query: SPARQL (more later)

Consumption - Technologies
Distributed setup  need for central point of access (indexer, aggregator)
Sindice, an index of the Web of Data
http://sindice.com/
Sig.ma, Web of Data aggregator & browser
http://sig.ma/
Relationship discovery
http://relfinder.semanticweb.org/

Technologies – FYN
http://dbpedia.org/resource/Galway
67

Technologies – Sig.ma
Sig.ma is a Web of Data platform enabling entity visualisation and consolidation both for humans and machines (API)
http://sig.ma/search?q=Galway
68

Technologies – sameas.org
Sameas.org is a service to find co-references on the Web of Data
http://sameas.org/html?q=Galway

All Linked Data datasets share a uniform data model, the RDF statement data model
Information is represented in facts expressed as (subject, predicate, object) triples
Components: globally unique IRI/URI entity identifiers & typed data values (literals) as objects
Linked Data Benefits: Uniformity

URIs not just used for identifying entities, but also (as URLs) for locating and retrieving resources that describe these entities on the Web
Linked Data Benefits: De-referencability

Linked Data Benefits: Coherence
Berlin
Germany
isCapitalOf
Knowledgebase 2
isMemberOf
Knowledgebase 1
European Union
triples containing URIs from different namespaces as subject and object, establish a link between (the entity identified by the) subject with (the entity identified by the) object (typed RDF links)

RDF data model, is based on a single mechanism for representing information (triples) -> very easy to attain a syntactic and simple semantic integration of different Linked Data sets.
higher level semantic integration can be achieved by employing schema and instance matching techniques and expressing found matches again as additional triple facts
Linked Data Benefits: Integrateability

Publishing and updating Linked Data is relatively simple thus facilitating a timely availability
once a Linked Data source is updated it is straightforward to access and use the updated data source (time consuming and error prune extraction, transformation and loading not required)
Linked Data Benefits: Timeliness

Linked Data 101
Agenda

What works now? What has to be done?
Web - a global, distributed platform for data, information and knowledge integration
exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF
Achievements
Extension of the Web with a data commons (25B facts
vibrant, global RTD community
Industrial uptake begins (e.g. BBC, Thomson Reuters, Eli Lilly)
Emerging governmental adoption in sight
Establishing Linked Data as a deployment path for the Semantic Web.
Challenges
Coherence: Relatively few, expensively maintained links
Quality: partly low quality data and inconsistencies
Performance: Still substantial penalties comparedto relational
Data consumption: large-scale processing, schema mapping and data fusion still in its infancy
Usability: Establishing direct end-user tools and network effect
April 2008
July 2007
September 2008
July 2009

Linked DataLifecycle

Extraction

From unstructured sources
NLP, text mining, annotation
From semi-structured sources
DBpedia, LinkedGeoData, SCOVO/DataCube
From structured sources
RDB2RDF
Extraction

extract structured information from Wikipedia & make this information available on the Web as LOD:
ask sophisticated queries against Wikipedia (e.g. universities in brandenburg, mayors of elevated towns, soccer players),
link other data sets on the Web to Wikipedia data
Represents a community consensus
Recently launched DBpedia Live transforms Wikipedia into a structured knowledge base
Transforming Wikipedia into an Knowledge Base

Structure in Wikipedia
Title
Abstract
Infoboxes
Geo-coordinates
Categories
Images
Links
other language versions
other Wikipedia pages
To the Web
Redirects
Disambiguations

Infobox templates
Wikitext-Syntax
{{Infobox Korean settlement
| title = Busan Metropolitan City
| img = Busan.jpg
| imgcaption = A view of the [[Geumjeong]] district in Busan
| hangul = 부산 광역시
...
| area_km2 = 763.46
| pop = 3635389
| popyear = 2006
| mayor = Hur Nam-sik
| divs = 15 wards (Gu), 1 county (Gun)
| region = [[Yeongnam]]
| dialect = [[Gyeongsang]]
}}
http://dbpedia.org/resource/Busan
dbp:Busan dbpp:title ″Busan Metropolitan City″
dbp:Busan dbpp:hangul ″부산 광역시″@Hang
dbp:Busan dbpp:area_km2 ″763.46“^xsd:float
dbp:Busan dbpp:pop ″3635389“^xsd:int
dbp:Busan dbpp:region dbp:Yeongnam
dbp:Busan dbpp:dialect dbp:Gyeongsang
...
RDF representation

A vast multi-lingual, multi-domain knowledge base
DBpedia extraction results in:
descriptions of ca. 3.4 million things (1.5 million classified in a consistent ontology, including 312,000 persons, 413,000 places, 94,000 music albums, 49,000 films, 15,000 video games, 140,000 organizations, 146,000 species, 4,600 diseases
labels and abstracts for these 3.2 million things in up to 92 different languages; 1,460,000 links to images and 5,543,000 links to external web pages;4,887,000 external links into other RDF datasets, 565,000 Wikipedia categories, and 75,000 YAGO categories
altogether over 1 billion pieces of information (i.e. RDF triples): 257M from English edition, 766M from other language editions
DBpediaLive (http://live.dbpedia.org/sparql/) &Mappings Wiki (http://mappings.dbpedia.org)integrate the community into a refinement cycle
UpcommingDBpedia inline

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DBpedia Architecture
Extraction Manager
Wikipedia
N-Triple
Dumps
Extraction Job
Destinations
Article-
Queue
Extractors
Update
Stream
N-Triple
Serializer
Image
Label
Category
PageCollections
SPARQL-
Update
Destination
Redirect
Disambiguation
Wikipedia
Dumps
Database
Wikipedia
Pagelink
Geo
Abstract
Generic Infobox
Triple Store
Virtuoso
Wikipedia
OAI-PMH
Live
Wikipedia
Mapping-based Infobox
Parsers
Ontology-
Mappings
DateTime
Units
Geo
String-List
Numbers
Linked Data
SPARQL endpoint
The Web
SPARQL clients
HTML browser
DBpedia apps
RDF browser

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Hierarchies
DBpedia Ontology Schema:
manually created for DBpedia (infoboxes)
275 classes + 1335 properties; 20mio triples
YAGO:
large hierarchy linking Wikipedia leaf categories to WordNet
250,000 classes
UMBEL (Upper Mapping and Binding Exchange Layer):
20000 classes derived from OpenCyc
Wikipedia Categories:
Not a class hierarchy (e.g. cycles), represented using SKOS
415,000+ categories

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DBpedia SPARQL Endpoint
http://dbpedia.org/sparql
hosted on a OpenLink Virtuososerver
can answer SPARQL queries like
Give me all Sitcoms that are set in NYC?
All tennis players from Moscow?
All films by Quentin Tarentino?
All German musicians that were born in Berlin in the 19th century?
All soccer players with tricot number 11, playing for a club having a stadium with over 40,000 seats and is born in a country with over 10 million inhabitants?

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DBpedia SPARQL Endpoint
SELECT ?name ?birth ?description ?person WHERE {
?person dbp:birthPlace dbp:Berlin .
?person skos:subject dbp:Cat:German_musicians .
?person dbp:birth ?birth .
?person foaf:name ?name .
?person rdfs:comment ?description .
FILTER (LANG(?description) = 'en') .
} ORDER BY ?name

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DBpedia Applications
DBpedia Mobile: location aware mobile client for DBpedia
Uses current location and DBpedia to display map
Can navigate into other knowledge bases
DBpedia Query Builder: user front end for building queries
DBpedia Relationship Finder finds relation between two objects in DBpedia

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DBpedia Applications

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DBpediaApplications: Relfinder
http://www.visualdataweb.org/relfinder.php

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DBpediaApplications: Zemanta

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DBpediaApplications: Faceted-Browser

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DBpedia Applications (3rd party)
Muddy Boots (BBC): Annotate actors in BBC News with DBpedia identifiers
Open Calais (Reuters): named entity recognition; entities are connected via owl:sameAs to DBpedia, Freebase, Geonames
Faviki: Social Bookmarking Tool uses DBpedia in backend to group tags etc. and multi-language support
Topbraid Composer: ontology editor, which links entities to DBpedia based on their labels

LinkedGeoData
Conversion, interlinking and publishing of OpenStreetMap.org* data sets as RDF.
* ”Wikipedia for geographic data”

Motivation
Ease information integration tasks that require spatial knowledge, such as
Offerings of bakeries next door
Map of distributed branches of a company
Historical sights along a bicycle track
Therefore use RDF/OWL in order overcome structural and semantic heterogeneity.
Requires a vocabulary – which we try to establish.
LOD cloud contains data sets with spatial features
e.g. Geonames, DBpedia, US census, EuroStat
But: they are restricted to popular or large entities like countries, famous places etc.
Therefore they lack buildings, roads, mailboxes, etc.

OpenStreetMap - Datamodel
Basic entities are:
NodesLatitude, Longitude
WaysSequence of nodes
RelationsAssociations between any number of nodes, ways and relations.
Each entity may be described with tags (= key-value pairs)

Example: Leipzig's zoo

Data/Mapping Example

Mapping Types
Three Mapping Types
Text
(5, name, Leipzig) -> lgd:node5 rdfs:label ”Leipzig”
(5, name:de, Leipzig) -> lgd:node5 rdfs:label ”Leipzig”@de
Datatypes
(6, seats, 4) -> lgd:node6 lgdo:seats ”4”^^xsd:integer
Classes/Object Properties
(7, place, city) -> lgdn:7 a lgdo:City
(7, religion, pastafarian) -> lgdn:7 lgdo:religion lgdo:Pastafarian

Access
Rest Interface (based on Postgis DB, full osm dataset loaded, > 1billion triples)
Supports limited queries (e.g. circular/rectangular area, filtering by labels)
Sparql Endpoints (based on Virtuoso DB, subset of osm dataset, ~222M triples)
Static (http://linkedgeodata.org/sparql)
Live (http://live.linkedgeodata.org/sparql)
Downloads (http://downloads.linkedgeodata.org)
Monthly updates on the above datasets envisioned

LinkedGeoData Live
OpenStreetMap provides full dumps and minutely changesets for download
Changesets are numbered, e.g. ”001/234/567.osc.gz”
We also convert the changesets to sets of added and removed triples (relative to our store) and publish them
001/234/567.added.nt.gz
001/234/567.removed.nt.gz
Advantage: Other users could easily sync their RDF store with LinkedGeoData

DBpedia Mapping – Step By Step
Given a DBpedia point, query LGD points within type specific maximum distance
Basic idea (performed with Silk):
Compute spatial score
Compute name similarity (rdfs:label)

DBpedia Mapping – Step By Step
Given a DBpedia point, query LGD points within type specific maximum distance
Basic idea (performed with Silk):
Compute spatial score
Compute name similarity (rdfs:label)
Combine both scores
Depending on final score, either automatically accept/reject links or mark for manual verification.

Statistics (2011-Feb-23)
222.539.712 Triples
6.666.865 Ways
5.882.306 Nodes
Among them
352.673 PlaceOfWorship
60.573 RailwayStation
59.468 Recycling
50.955 Town
30.099 Toilet
7.222 City

Conclusion
OpenStreetMap
immensely successful project for collaboratively creating free spatial data
Community uses key value structures, which provide a rich source of information
Key strength: broad coverage
LGD Contributions
Established mapping to Dbpedia
Geonames mapping partially done (37 different entity types cities, churches, ...)
Facet-based LGD Browser provides an interface for OSM/LGD, which highlights its structural aspects
Live sync
Goal: Make LGD as useful (succesful) as DBpedia for the geospatial domain

Many different approaches (D2R, Virtuoso RDF Views, Triplify, …)
No agreement on a formalsemantics of RDF2RDFmapping
LOD readiness, SPARQL-SQL translation
W3C RDB2RDF WG
Extraction Relational Data

From unstructured sources
Deploy existing NLP approaches (OpenCalais, Ontos API)
Develop standardized, LOD enabled interfaces between NLP tools (NLP2RDF)
From semi-structured sources
Efficient bi-directional synchronization
From structured sources
Declarative syntax and semantics of data model transformations (W3C WG RDB2RDF)
Orthogonal challenges
Using LOD as background knowledge
Provenance
Extraction Challenges

Storage and Querying

Still by a factor 5-50 slower than relational data management (BSBM, DBpedia Benchmark)
Performance increases steadily
Comprehensive, well-supported open-soure and commercial implementations are available:
OpenLink’s Virtuoso (os+commercial)
Big OWLIM (commercial), Swift OWLIM (os)
4store (os)
Talis (hosted)
Bigdata (distributed)
Allegrograph (commercial)
Mulgara (os)
RDF Data Management

UsesDBpediaasdataand a selectionof 25 frequentlyexecutedqueries
Can generatefractionsand multiples ofDBpedia‘ssize
Does not resemble relational data
Performance differences, observedwithotherbenchmarksareamplified
DBpedia Benchmark
GeometricMean

Reduce the performance gap between relational and RDF data management
SPARQL Query extensions
Spatial/semantic/temporal data management
More advanced query result caching
View maintenance / adaptive reorganization based on common access patterns
More realistic benchmarks
Storage and Querying Challenges

Authoring

1. Semantic (Text) Wikis
Authoring of semanticallyannotated texts
2. Semantic Data Wikis
Direct authoring of structured information (i.e. RDF, RDF-Schema, OWL)
Two Kinds of Semantic Wikis

Versatile domain-independent tool
Serves as Linked Data / SPARQL endpoint on the Data Web
Open-source project hosted at Google code
Not just a Wiki UI, but a whole framework for the development of Semantic Web applications
Developed in PHP based on the Zend framework
Very active developer and user community
More than 500 downloads monthly
Large number of use cases
OntoWiki – a semantic data wiki

Dynamic views on knowledgebases
OntoWiki

OntoWiki
RDF triples on resourcedetailspage

OntoWiki
Dynamische Vorschläge aus dem Daten Web

CatalogusProfessorumLipsiensis

OntoWiki: Caucasian Spiders

RDFauthor in OntoWiki

Semantic Portal with OntoWiki: Vakantieland

RDFaCE- RDFa Content Editor

RDFaCEArchitecture

Integratingvarious NLP APIs

Automatic
Semi-automatic
SILK
LIMES
Manual
Sindice integration into UIs
Semantic Pingback
LOD Linking

LIMES 0.3: Basic Idea
Usesthecharacteristicsofmetricspaces
Especiallyconsequencesoftriangleinequality
d(x, y) < d(x, z) + d(z, y)
d(x, z) - d(z, y) < d(x, y) < d(x, z) + d(z, y)
Basic idea
Usepessimisticapproximationsofdistancesinsteadofcomputingthem
Onlycomputedistanceswhenneeded

Overview
Knowledgesources

Computationof Exemplars
Assumption: numberofexemplarsisgiven
Goal: Segment targetdataset

NB: Distancesfromexemplarsto all otherpointsareknown

Filtering
y
x
z
1. Measuredistancefromeach x toeachexemplar

Filtering
y
x
z
2. Applyd(x, y) - d(y, z) > t d(x, z) > t

SimilarityComputation
y
x
z
d(x, y) - d(y, z) <t Compute d(x, z)

Serialization
Resultsarereturnedas RDF
ForexamplemappingDBpediaandDrugbank
@prefixdrugbank: <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/> .
@prefixdbpedia: <http://dbpedia.org/ontology/> .
@prefixowl: <http://www.w3.org/2002/07/owl#> .
dbpedia:Cefaclorowl:sameAs drugbank:DB00833 .
dbpedia:Clortermineowl:sameAs drugbank:DB01527 .
dbpedia:Prednicarbateowl:sameAs drugbank:DB01130 .
dbpedia:Linezolidowl:sameAs drugbank:DB00601 .
dbpedia:Valaciclovirowl:sameAs drugbank:DB00577 .
….

Experiments
Q1: Whatisthebestnumberofexemplars?
Q2: Whatistherelationbetweenthesimilaritythresholdqandthe total numberofcomparisons?
Q3: Doestheassignmentof S and T matter?
Q4:Howdoes LIMES compareto SILK?

Q1 and Q2
Experiments on syntheticdata
Knowledgebasesofsizes 2000, 3000, 5000, 7500 and 10000
Variednumberofexemplars
Variedthresholds
Experiments wererepeated 5 times
Averageresultsarepresented

Q1 and Q2

Q1 and Q2
Q1
Best numberofexemplarsdepends on q
Forq> 0.9, bestnumber lies around |T|1/2
Q2
As expected, numberofcomparisonsdiminisheswithgrowingq

Q3 (order of S and T)
Experiments on syntheticdata
Knowledgebasesofsizes 1000, 2000, 3000, …, 10000
Numberofexemplars was |T|1/2
Experiments wererepeated 5 times
Averageresultsarepresented

Q3
Green = S firstismore time-efficient
Overall lessthan 5% difference

Q4 (comparisonwith SILK)
3 Experiments on real data
Drugs
Diseases
SimCities
Numberofexemplars was |T|1/2
Comparisonofruntimewith SILK
Experiments wererepeatedthrice
Best runtimesarepresented

Q4
Weoutperform SILK 2 by 1.5 ordersofmagnitude
The larger thedatasources, thehigherourspeedup (64 forSimCities)

LIMES Future Work
Extension ofapproachto semi-metrics
Adaption ofexemplarcomputationtoskew in data
Automatic computationoftheaccuratenumberofexemplars
ParallelizationusingHadoopshowshighspeedupgain
Combinationwithactivelearning

update and notification services for LOD
Downward compatible with Pingback (blogosphere)
http://aksw.org/Projects/SemanticPingBack
Creating a network effect aroundLinking Data: Semantic Pingback

Visualizing Pingbacks in OntoWiki

Only 5% of the information on the Data Web is actually linked
Make sense of work in the de-duplication/record linkage literature
Consider the open world nature of Linked Data
Use LOD background knowledge
Zero-configuration linking
Explore active learning approaches, which integrate users in a feedback loop
Maintain a 24/7 linking service: Linked Open Data Around-The-Clock project (LATC-project.eu)
Interlinking Challenges

Enrichment

Linked Data is mainly instance data and !!!
ORE (Ontology Repair and Enrichment) tool allows to improve an OWL ontology by fixing inconsistencies & making suggestions for adding further axioms.
Ontology Debugging: OWL reasoning to detect inconsistencies and satisfiable classes + detect the most likely sources for the problems. user can create a repair plan, while maintaining full control.
Ontology Enrichment: uses the DL-Learner framework to suggest definitions & super classes for existing classes in the KB. works if instance data is available for harmonising schema and data.
http://aksw.org/Projects/ORE
Enrichment & Repair

Quality
Analysis

Quality on the Data Web is varying a lot
Hand crafted or expensively curated knowledge base (e.g. DBLP, UMLS) vs. extracted from text or Web 2.0 sources (DBpedia)
Research Challenge
Establish measures for assessing the authority, provenance, reliability of Data Web resources
Linked Data Quality Analysis

unified method, for both data evolution and ontology refactoring.
modularized, declarative definition of evolution patterns is relatively simple compared to an imperative description of evolution
allows domain experts and knowledge engineers to amend the ontology structure and modify data with just a few clicks
Combined with RDF representation of evolution patterns and their exposure on the Linked Data Web, EvoPat facilitates the development of an evolution pattern ecosystem
patterns can be shared and reused on the Data Web.
declarative definition of bad smells and corresponding evolution patterns promotes the (semi-)automatic improvement of information quality.
EvoPat – Pattern based KB Evolution

Evolution Patterns

Exploration

An ecosystemof LOD visualizations
Statistical
visualization
Faceted-browsing
Spatialfaceted-browsing
Entity-/faceted-
Basedbrowsing
Domain specificvisualizations
LOD Exploration
Widgets
…
…
Dataset analysis (size, vocabularies, propertyhistograms etc.)
Selectionofsuitablevisualizationwidgets
Choreographylayer
LOD Datasets

TODO: Put ULEI slides
Faceted spatial-semantic browsing component

Pure JavaScript, requires onlySPARQL Endpoint for data access, Cross-Origin Resource Sharing (CORS) enabled.
operates on local spatial regions, doednot depend on global meta-data about the data
Source code:
https://github.com/AKSW/SpatialSemanticBrowsingWidgets
Online Demo - LinkedGeoData Browser:
http://browser.linkedgeodata.org
Next steps
Polygone/curvemarkers, domainspecificvisualizationtemplates, integrationofothersources, mobile interface
Publication:
Claus Stadler, Jens Lehmann, Konrad Höffner, Sören Auer: LinkedGeoData: A Core for a Web of Spatial Open Data. Toappear in Semantic Web Journal - Special Issue on Linked Spatiotemporal Data and Geo-Ontologies.
Faceted spatial-semantic browsing - Availability

Genericentity-basedexplorationwithOntoWiki
http://fintrans.publicdata.eu

Domain-specificvisualization:http://energy.publicdata.eu

Visualization ofstatisticdata (datacubevocab.)
http://scoreboard.lod2.eu

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Visual Query Builder

Relationship Finder in CPL

Distributed Social Semantic Networking

DistributedSocialSemanticNetworking
Social Networks are walled gardens
Take users' data out of their hands,
predefined privacy & data security regulations
infrastructure of a single provider (lock-in)
Facebook (600M+ users) = Web inside the Web
Interoperability is limited to proprietary APIs
Social networks should be open and evolving
allow users to control what to enter & keep control over their data
users should be able to host the data on infrastructure, which is under their direct control, the same way as they host their own website (TBL)
We need a truly Distributed Social Semantic Network (DSSN)
Initial approaches appeared with GNU social and more recently Diaspora
a DSSN should be based on semantic resource descriptions and de-referenceabilityso as to ensure versatility, reusability and openness in order to accommodate unforeseen usage scenarios
a number of standards and best-practices for social, Semantic Web applications such as FOAF, WebID and Semantic Pingback emerged.

Resources announce services and feeds, feeds announce services – in particular a push service.
Applications initiate ping requests to spin the Linked Data network
Applications subscribe to feeds on push services and receive instant notifications on updates.
Update services are able to modify resources and feeds (e.g. on request of an application)
Personal and global search services index social network resources and are used by applications
Access to resources & services can be delegated to applications by a WebID, i.e. application can act in name of WebID owner
The majority of all access operations is executed through standard web requests.
DSSN Architecture

Open-source, MVC architecture
Plattform independent, based on HTML5, CSS, Javascript
jQuery, jQuery Mobile, jQueryUI
rdfQuery – simple triplestore in Javascript
PhoneGap (Apache Device ready) native appsforiOS, Android, Blackberry OS, WebOS, Symbian, Bada
http://aksw.org/Projects/MobileSocialSemanticWeb
DSSN Mobile Client

DSSN Mobile Browsing

DSSN Mobile Editing

LOD Lifecycle
supported byDebian based
LOD2 Stack
(released next week)

First releaseofthe LOD2 Stack: stack.lod2.eu & demo.lod2.eu/lod2demo

AKSW Team

Thanksforyourattention!
Sören Auer
http://www.uni-leipzig.de/~auer/ | http://aksw.org | http://lod2.org
auer@uni-leipzig.de

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Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data

by Sören Auer on Oct 06, 2011

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Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data Presentation Transcript