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

DBpedia and the Emerging Web of
Linked Data
Sören Auer

Creating Knowledge
out of Interlinked Data
Sören Auer – SBBD: DBpedia and the Emerging Web of Linked Data 5.10.2011 Page 2 http://lod2.eu
• 2000 Mathematics and Computer Science studies in
Hagen, Dresden and Екатеринбург
• Managing director of adVIS GmbH – SME focused
on 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 at Universität 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. Sören Auer

Creating Knowledge
1. The Vision & Big Picture
2. Linked Data 101
3. The Linked Data Life-cycle
Agenda

Creating Knowledge
1. Reasoning does not scale on the Web
• IR / one dimensional indexing scales (Google)
• Next step conjunctive querying (OWL-QL?, dynamic
scale-out / clustering)
• Web scalable DL reasoning is out-of-sight (maybe fragment,
fuzzy reasoning has some chances)
2. If it would scale it would not be affordable
• “What is the only former Yugoslav republic in the
European Union?”
• 2880 POWER7 cores, 16 Terabytes memory, 4 Terabytes
clustered storage (IBM Watson) still can not answer this
question
Why the Semantic Web won‘t work (soon)

Creating Knowledge
Web
server
Web
server
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.
Information is available on the Web, but opaque to current search.
Why do we need the Data Web?
berlin.de
Has everything about
childcare in Berlin.
Immobilienscout.de
Knows all about real estate
offers in GermanyDB
Web
server
DB
Web
server
Search engineHTML HTML
RDF
RDF
Solution: complement text on Web pages with structured linked
open data & intelligently combine/integrate such structured
information from different sources:

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

Web 1.0 Web 2.0 Web 3.0
Many Web sites
containing unstructured,
textual content
Few large Web sites
are specialized on
specific content types
Many Web sites containing
& semantically syndicating
arbitrarily structured
content
Pictures
Video
Encyclopedic
articles
+ +

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

Creating Knowledge
1. Uses RDF Data Model
Linked Data in a Nutshell
SBBD2011
Florianopolis
3.10.2011
SBC
organizes
starts
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
20082007
2008
2008
2008
2009
2009
Virtouso
SemMF
SILK
poolparty
DL-Learner
Sindice
Sigma
ORE
OntoWiki
MonetDB
DXX Engine
WiQA
repair
interlink
fuse
classify
enrich
create

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

Creating Knowledge
2. 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
1. Use URIs to identify the “things” in your data
2. Use http:// URIs so people (and machines) can
look them up on the web
3. When a URI is looked up, return a description of
the thing (in RDF format)
4. 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 Law
http://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
REQUESTRESPONSE

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 scalability
http://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, vary
metadata
control data if-modified-since, cache-control
http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm
http://webofdata.wordpress.com/2009/10/09/linked-data-for-restafarians/

Web's Standard Retrieval Algorithm
1. parse URI and find HTTP protocol
2. look up DNS name to determine the associated
IP address
3. open a TCP stream to port 80 at the IP address
determined above
4. format an HTTP GET request for resource and
sends that to the server
5. read response from the server
6. from the status code (200) determine that a
representation of the resource is available
7. inspect the returned Content-Type
8. 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 node
Predicate: URI (also called properties)
Object : URI or blank nodes or literal
Burkhard Jung Leipzig
isMayorOf
Subject Predicate Object

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

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

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

Turtle Syntax
• URIs in angle brackets
• Literals in quotes
• Triples separated by dot
• Whitespace is ignored
3

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:
@prefix rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs:<http://www.w3.org/2000/01/rdf-schema#> .
@prefix dbp:<http://dbpedia.org/resource/> .
@prefix dbpp:<http://dbpedia.org/property/> .
@prefix geo:<http://www.w3.org/2003/01/geo/wgs84_pos#> .
dbp:Leipzig dbpp:hasMayor dbp:Burkhard_Jung .
dbp:Leipzig rdfs:label "Leipzig"@de .
dbp:Leipzig geo:lat "51.333332"^^xsd:float .
dbp:Leipzig geo:lon "12.383333"^^xsd:float .

Turtle Syntax: Shortcuts
Group triples with same subject using “;” instead of “.”:
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs="http://www.w3.org/2000/01/rdf-schema#> .
@prefix dbp="http://dbpedia.org/resource/> .
@prefix dbpp="http://dbpedia.org/property/> .
@prefix geo="http://www.w3.org/2003/01/geo/wgs84_pos#> .
dbp:Leipzig dbpp:hasMayor dbp: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:
@prefix dbp="http://dbpedia.org/resource/> .
@prefix dbpp="http://dbpedia.org/property/> .
dbp:Leipzig dbp:locatedIn dbp:Saxony, dbp:Germany;
dbpp:hasMayor dbp:Burkhard_Jung .

XML-Syntax von RDF
• Turtle intuitively readable and machine processable
• but: better tool support and programming libraries for XML
<?xml version="1.0"?>
<rdf:RDF 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#">
<rdf:Description rdf:about="http://dbpedia.org/resource/Leipzig">
<property:hasMayor
rdf:resource="http://dbpedia.org/resource/Burkhard_Jung" />
<rdfs:label xml:lang="de">Leipzig</rdfs:label>
<geo:lat rdf:datatype="float">51.3333</geo:lat>
<geo:lon rdf:datatype="float">12.3833</geo:lon>
</rdf:Description>
</rdf:RDF>

RDF/JSON
• JSON = JavaScript Object Notation
• Compact format for data exchange between applications
• JSON documents are valid JavaScript
• Programming language independent, since parser exist for all popular
programming languages
• Less overhead when parsing and serialising than XML
{ "S" : { "P" : [ O ] } }
• Subject: URI, BNode
• Predicate: URI
• Object:
Type: „URI“, „Literal“ or „bnode“
Value: data value
Lang: language tag
Datatype: URI of the datatype.

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 to embedding in HTML more overhead than other serialisations
• Less readable
<?xml version="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">
<html version="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>
<body about="http://dbpedia.org/resource/Leipzig">
<h1 property="rdfs:label" xml:lang="de">Leipzig</h1>
<p>Leipzig is a city in Germany. Leipzig's mayor is
<a href="Burkhard_Jung" rel="dbpp:hasMayor">Burkhard Jung</a>. It is located
at latitude <span property="geo:lat" datatype="xsd:float">51.3333</span>
and longitude <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

SS2011 41
Vokabulare: Friend-of-a-Friend (FOAF)
defines classes and properties for representing
information about people and their
relationships
Soeren rdf:type foaf:Person .
Soeren currentProject http://OntoWiki.net .
Soeren foaf:homepage http://aksw.org/Soeren .
Soeren foaf:knows http://sembase.at/Tassilo .
Soeren foaf:sha1 09ac456515dee .

SS2011 42
Vokabulare: Semantically
Interlinked Online Communities.
Represent content from Blogs, Wikis, Forums,
Mailinglists, Chats etc.

SS2011 43
Vokabulare: Simple Knowledge
Organization System (SKOS)
support the use of thesauri, classification schemes, subject
heading systems and taxonomies

SS2011
Instance data
Instances are associated with one or several classes:
Boddingtons rdf:type Ale .
Grafentrunk rdf:type Bock .
Hoegaarden rdf:type White .
Jever rdf:type Pilsner .

The
Linked Open Data cloud

20082007
2008
2008
2008
2009
20092010
46

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

LOD cloud stats
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
Third
Party
Effort
Consumer‘s
Effort
Publisher‘s
Effort
Fix
Overall Data
Integration
Effort

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 linkset is 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/

General dataset metadata
 Dataset homepage
 Publisher
 Title and description
 Categorisation
 Licensing
 Technical features

General dataset metadata
: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
:exampleDS void: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:class foaf:Person;
void:entities 312000;
];
void:propertyPartition [
void:property foaf: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:linkPredicate owl:sameAs .

Deployment and discovery
 Choosing URIs for datasets
 Publishing a VoID file alongside a dataset
 Turtle
 RDFa
 SPARQL Service Description Vocabulary
http://www.w3.org/TR/sparql11-service-description/
 Discovery (well-known URI), based on of RFC5758],
registered with IANA
http://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
http://sig.ma/search?q=Galway
Sig.ma is a Web of
Data platform
enabling entity
visualisation and
consolidation both for
humans and
machines (API)
68

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

Creating Knowledge
• 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

Creating Knowledge
• 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

Creating Knowledge
• 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)
Linked Data Benefits: Coherence
Berlin Germany
European Union
isCapitalOf
isMemberOfKnowledge base 1
Knowledge
base 2

Creating Knowledge
• 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

Creating Knowledge
• 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

Creating Knowledge
2. Linked Data 101
Agenda

Creating Knowledge
Achievements
1. Extension of the Web with a
data commons (25B facts
2. vibrant, global RTD
community
3. Industrial uptake begins (e.g.
BBC, Thomson Reuters, Eli
Lilly)
4. Emerging governmental
adoption in sight
5. Establishing Linked Data as a
deployment path for the
Semantic Web.
What works now? What has to be done?
 Challenges
1. Coherence: Relatively few,
expensively maintained links
2. Quality: partly low quality data
and inconsistencies
3. Performance: Still substantial
penalties compared to relational
4. Data consumption: large-scale
processing, schema mapping
and data fusion still in its infancy
5. Usability: Establishing direct
end-user tools and network
effect
• 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
July 2007 April 2008 September 2008
July 2009

Creating Knowledge
Inter-
linking/
Fusing
Classifi-
cation/
Enrichment
Quality
Analysis
Evolution /
Repair
Search/
Browsing/
Exploration
Extraction
Storage/
Querying
Manual
revision/
authoring
Linked Data
Lifecycle

Creating Knowledge
Extraction

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

Creating Knowledge
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
{{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
...
Wikitext-Syntax
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
• DBpedia Live (http://live.dbpedia.org/sparql/) &
Mappings Wiki (http://mappings.dbpedia.org)
integrate the community into a refinement cycle
• Upcomming DBpedia inline

2011/05/12 CONSEGI - Sören Auer: DBpedia 84
DBpedia Architecture
Extraction Job
Extraction Manager
PageCollections
Destinations
N-Triple
Dumps
Wikipedia
Dumps
Wikipedia
OAI-PMH
Database
Wikipedia
Live
Wikipedia
N-Triple
Serializer
SPARQL-
Update
Destination
Extractors
Generic Infobox
Label
Geo
Redirect Disambiguation
Image
Abstract Pagelink
Parsers
DateTime Units
Ontology-
Mappings
Mapping-based Infobox
String-List Numbers
Geo
SPARQL
endpoint
Linked
Data
The Web
RDF browser
HTML browserSPARQL clients
DBpedia apps
Triple Store
Virtuoso
Update
Stream
Article-
Queue
Wikipedia
Category

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

DBpedia SPARQL Endpoint
http://dbpedia.org/sparql
hosted on a OpenLink Virtuoso server
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?

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

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

DBpedia Applications

DBpedia Applications: Relfinder
http://www.visualdataweb.org/relfinder.php

DBpedia Applications: Zemanta

DBpedia Applications: Faceted-Browser

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

Universität Leipzig ▪ Agile Knowledge Engineering and Semantic Web (AKSW) Authors: Sören Auer, Jens Lehmann,
Slide 94
LinkedGeoData
Conversion, interlinking and publishing of
OpenStreetMap.org* data sets as RDF.
* ”Wikipedia for geographic data”

Slide 95
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.

Slide 96
OpenStreetMap - Datamodel
● Basic entities are:
● Nodes Latitude, Longitude
● Ways Sequence of nodes
● Relations Associations between any number of nodes, ways and relations.
● Each entity may be described with tags (= key-value
pairs)

Slide 97
Example: Leipzig's zoo

Slide 100
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

Slide 101
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

Slide 102
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

Slide 103
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)

Slide 104
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.

Slide 105
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

Slide 106
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

Creating Knowledge
Sören Auer – SDDB: DBpedia and the Emerging Web of Linked Data 5.10.2011 Page 107 http://lod2.eu
Many different approaches (D2R, Virtuoso RDF
Views, Triplify, …)
No agreement on a formal
semantics of RDF2RDF
mapping
• LOD readiness,
SPARQL-SQL translation
W3C RDB2RDF WG
Extraction Relational Data
Tool Triplify D2RQ
Virtuoso RDF
Views
Technology
Scripting
languages
(PHP)
Java
Whole
middleware
solution
SPARQL
endpoint
- X X
Mapping
language
SQL RDF based RDF based
Mapping
generation
Manual
Semi-
automatic
Manual
Scalability
Medium-high
(but no
SPARQL)
Medium High

Creating Knowledge
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

Creating Knowledge
Sören Auer – SBBD: DBpedia and the Emerging Web of Linked Data 5.10.2011 Page 109 http://lod2.euStorage and Querying

Creating Knowledge
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

Creating Knowledge
• Uses DBpedia as data and
a selection of 25 frequently
executed queries
• Can generate fractions and
multiples of DBpedia‘s size
• Does not resemble
relational data
Performance differences,
observed with other
benchmarks are amplified
DBpedia Benchmark
Geometric Mean

Creating Knowledge
• 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

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

Creating Knowledge
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

Creating Knowledge
OntoWiki Dynamic views on
knowledge bases

Creating Knowledge
OntoWiki
RDF triples on
resource details
page

Creating Knowledge
OntoWiki
Dynamische
Vorschläge aus dem
Daten Web

Creating Knowledge
Catalogus Professorum Lipsiensis

Creating Knowledge
RDFauthor in OntoWiki

Creating Knowledge
Semantic Portal with OntoWiki: Vakantieland

Creating Knowledge
RDFaCE- RDFa Content Editor

Creating Knowledge
RDFaCE Architecture

Creating Knowledge
Integrating various NLP APIs

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

LIMES 0.3: Basic Idea
 Uses the characteristics of metric
spaces
 Especially consequences of triangle
inequality
◦ 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
◦ Use pessimistic approximations of
distances instead of computing them
◦ Only compute distances when needed

Overview
Computation
of exemplars
Filtering
Similarity
computation
Serialization
Knowledge
sources

Computation of Exemplars
 Assumption: number of exemplars is
given
 Goal: Segment target data set

Computation of Exemplars
NB: Distances from exemplars to all
other points are known

Filtering
x y
z
1. Measure distance from each x to
each exemplar

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

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

Serialization
 Results are returned as RDF
 For example mapping DBpedia and Drugbank
@prefix drugbank: <http://www4.wiwiss.fu-
berlin.de/drugbank/resource/drugbank/> .
@prefix dbpedia: <http://dbpedia.org/ontology/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
dbpedia:Cefaclor owl:sameAs drugbank:DB00833 .
dbpedia:Clortermine owl:sameAs
drugbank:DB01527 .
dbpedia:Prednicarbate owl:sameAs
drugbank:DB01130 .
dbpedia:Linezolid owl:sameAs drugbank:DB00601
.
dbpedia:Valaciclovir owl:sameAs

Experiments
 Q1: What is the best number of
exemplars?
 Q2: What is the relation between the
similarity threshold q and the total
number of comparisons?
 Q3: Does the assignment of S and T
matter?
 Q4: How does LIMES compare to
SILK?

Q1 and Q2
 Experiments on synthetic data
 Knowledge bases of sizes 2000, 3000,
5000, 7500 and 10000
 Varied number of exemplars
 Varied thresholds
 Experiments were repeated 5 times
 Average results are presented

Q1 and Q2
0
20000000
40000000
60000000
80000000
100000000
120000000
0 50 100 150 200 250 300
0.75
0.8
0.85
0.9
0.95
Brute force

Q1 and Q2
 Q1
◦ Best number of exemplars depends on q
◦ For q > 0.9, best number lies around |T|1/2
 Q2
◦ As expected, number of comparisons
diminishes with growing q

Q3 (order of S and T)
 Experiments on synthetic data
 Knowledge bases of sizes 1000, 2000,
3000, …, 10000
 Number of exemplars was |T|1/2
 Experiments were repeated 5 times
 Average results are presented

Q3
TS 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
1000 0.20 0.37 0.53 0.69 0.88 1.04 1.14 1.40 1.58 1.67
2000 0.36 0.64 0.88 1.24 1.37 1.63 1.97 2.25 2.50 2.70
3000 0.51 0.86 1.17 1.57 2.00 2.09 2.69 2.91 3.35 3.58
4000 0.70 1.11 1.59 2.00 2.45 2.88 3.10 3.61 3.94 4.50
5000 0.85 1.36 1.87 2.28 2.81 3.39 3.91 4.20 4.84 5.54
6000 1.02 1.60 2.14 2.81 3.29 3.93 4.44 4.96 5.39 6.08
7000 1.22 1.86 2.58 3.15 3.66 4.35 5.11 5.69 6.44 6.62
8000 1.41 2.04 2.78 3.43 4.06 4.98 5.51 6.55 7.14 7.53
9000 1.63 2.36 2.99 3.85 4.72 5.44 6.25 6.88 7.59 8.20
10000 1.80 2.62 3.51 4.25 4.97 6.01 6.33 7.81 8.31 9.15
 Green = S first is more time-efficient
 Overall less than 5% difference

Q4 (comparison with SILK)
 3 Experiments on real data
◦ Drugs
◦ Diseases
◦ SimCities
 Number of exemplars was |T|1/2
 Comparison of runtime with SILK
 Experiments were repeated thrice
 Best runtimes are presented

Q4
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Drugbank SimCities Diseases
LIMES (0.95)
LIMES (0.90)
LIMES (0.85)
LIMES (0.80)
LIMES (0.75)
SILK

Q4
 We outperform SILK 2 by 1.5 orders of
magnitude
 The larger the data sources, the
higher our speedup (64 for SimCities)

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

Creating Knowledge
Visualizing Pingbacks in OntoWiki

Creating Knowledge
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

Creating Knowledge
Enrichment

Creating Knowledge
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

Creating Knowledge
Sören Auer – SBBD: DBpedia and the Emerging Web of Linked Data 5.10.2011 Page 156 http://lod2.euAnalysis
Quality

Creating Knowledge
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

Creating Knowledge
• 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

Creating Knowledge
Evolution Patterns

Creating Knowledge
Exploration

Creating Knowledge
An ecosystem of LOD visualizations
LODExploration
Widgets
Spatial faceted-
browsing
Faceted-
browsing
Statistical
visualization
Entity-/faceted-
Based browsing
Domain specific
visualizations … …
LODDatasetsChoreography
layer
• Dataset analysis (size, vocabularies, property histograms etc.)
• Selection of suitable visualization widgets

Creating Knowledge
TODO: Put ULEI slides
Faceted spatial-semantic browsing
component

Creating Knowledge
Pure JavaScript, requires only SPARQL Endpoint for data access, Cross-Origin Resource
Sharing (CORS) enabled.
operates on local spatial regions, doed not 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/curve markers, domain specific visualization templates, integration of other
sources, mobile interface
Publication:
• Claus Stadler, Jens Lehmann, Konrad Höffner, Sören Auer: LinkedGeoData: A Core for a
Web of Spatial Open Data. To appear in Semantic Web Journal - Special Issue on Linked
Spatiotemporal Data and Geo-Ontologies.
Faceted spatial-semantic browsing - Availability

Creating Knowledge
Generic entity-based exploration with OntoWiki
http://fintrans.publicdata.eu

Creating Knowledge
Domain-specific visualization:
http://energy.publicdata.eu

Creating Knowledge
Visualization of statistic
data (datacube vocab.)
http://scoreboard.lod2.eu

Creating Knowledge
13.08.2016 Sören Auer - The emerging 170

Creating Knowledge
13.08.2016 Sören Auer - The emerging 171

Creating Knowledge
Visual Query Builder

Creating Knowledge
Relationship Finder in CPL

Creating Knowledge
Distributed Social Semantic Networking

Creating Knowledge
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-referenceability
so 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.
Distributed
Social
Semantic
Networking

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

Creating Knowledge
• Open-source, MVC architecture
• Plattform independent, based on HTML5, CSS,
Javascript
• jQuery, jQuery Mobile, jQuery UI
• rdfQuery – simple triple store in Javascript
• PhoneGap (Apache Device ready) native apps for
iOS, Android, Blackberry OS, WebOS, Symbian,
Bada
• http://aksw.org/Projects/MobileSocialSemanticWeb
DSSN Mobile Client

Creating Knowledge

Creating Knowledge
DSSN Mobile Browsing

Creating Knowledge
DSSN Mobile Editing

EU-FP7 LOD2 Project Overview . Page 182 http://lod2.eu
Creating Knowledge out of Interlinked Data
Inter-
linking/
Fusing
Classifi-
cation/
Enrichmen
t
Quality
Analysis
Evolution /
Repair
Search/
Browsing/
Exploratio
n
Extractio
n
Storage/
Querying
Manual
revision/
authoring
LOD Lifecycle
supported by
Debian based
LOD2 Stack
(released next week)

First release of the LOD2 Stack: stack.lod2.eu & demo.lod2.eu/lod2de

AKSW Team

Creating Knowledge
Thanks for your attention!
Sören Auer
http://www.uni-leipzig.de/~auer/ | http://aksw.org | http://lod2.org
auer@uni-leipzig.de

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

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