Linked Data Tutorial

From Document Web to a Web of Linked Data Dr. S ö ren Auer AKSW, Institut f ü r Informatik

Overview
The Linked Data Web Vision
Data Web Technologies
Publishing relational data on the Web
DBpedia – transforming Wikipedia into a knowledge base
OntoWiki – an Linked Data Wiki
Open Street Maps – linked open geo data
Linked Data Tutorial

From the Document Web to the Linked Open Data Web (and beyond) Linked Data Tutorial
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
CBD
RDF serializations

Conceptual Level Data Access and Integration Linked Data Tutorial
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
Data Models
Others
XML, hierachical, tree, graph-oriented DBMS
Procedural APIs
ODBC
JDBC
Data Access
Query Languages
Datalog, SQL
SPARQL
XPATH/XQuery
Data Integration
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

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 + + Linked Data Tutorial

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 Linked Data Tutorial

Why Do We Need Another Web?
Try to search for these things on the current Web:
Apartments near German-French bilingual childcare in Leipzig.
ERP service providers with offices in Vienna and Berlin.
Researchers working on DB related topics in south-east Asia.
Information to answer such search queries is available on the Web, but opaque to current Web search .
(Semantic) Data Web allows to complement text on Web pages with structured data and to intelligently combine and integrate such structured information from different sources:
Web server Web server Linked Data Tutorial Leipzig.de Has everything about childcare in L.e. Immobilienscout.de Knows all about real estate offers in Germany DB Web server DB Web server Search engine HTML HTML RDF RDF

Overview
Virtuoso – Knowledge Store
Open Street Maps – free and open geo data

RDF - Resource Description Framework
Distinguishes two fundamental base types :
Resources
Complex abstract or concret entities
Uniquely identified by an URI:
http://DBpedia.org/resource/Vienna
Literals
concrete data values
Optionally typed (e.g. xsl:string , xsl:dateTime etc.) or language (e.g. en , de ):
" 2008-05-31T09:30:00 " ^^xsd:dateTime
" Wien " @ " de "

RDF Statement / Triple Paradigm
RDF/XML:
<?xml version="1.0"?>
< rdf:RDF
xmlns="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/metadata/dublin_core#">
< Description about =" http://OntoWiki.net ">
< dc:Creator >Sö ren Auer < /DC:Creator >
</Description >
</rdf:RDF>
Linked Data Tutorial http://OntoWiki.net Sö ren Auer dc:creator Subject (Resource) Predicate (Resource) Object (Resource/Literal) RDF/N3: http://OntoWiki.net http://purl.org/metadata/dublin_core#Creator "Sö ren Auer “

RDF Document / Model / Graph
Simple Knowledge Base
Combines multiple RDF Statements
Linked Data Tutorial [email_address] http://OntoWiki.net http://aksw.org/staff/Soeren dc:Creator Sö ren Auer foaf:Email foaf:Name

RDF Serialization
<?xml version="1.0"?>
< rdf:RDF
xmlns="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/metadata/dublin_core#">
< rdf:Description about="http://OntoWiki.net">
<dc:Creator>
< rdf:Description>
< rdf:Description about="http://aksw.org/staff/Soeren">
<dc:Name>Sö ren Auer </dc:Name>
<dc:Email>auer@informatik.uni-leipzig.de</dc:Email>
< /rdf:Description >
</dc:Creator>
< /rdf:Description >
< /rdf:RDF >
Linked Data Tutorial http://OntoWiki.net http://purl.org/metadata/dublin_core#Creator http://aksw.org/staff/Soeren http://aksw.org/staff/Soeren http://purl.org/metadata/dublin_core#Name "Sö ren Auer " http://aksw.org/staff/Soeren http://purl.org/metadata/dublin_core#Email [email_address] [email_address] http://OntoWiki.net http://aksw.org/staff/Soeren Creator Sö ren Auer Email Name

RDF Schema
Restrict combinations of resources / literals
Structuring of vocabularies
Instantiation / classification
Provisioning of special resources:
Classes (concepts, frames) http://www.w3.org/2000/01/rdf-schema#Class
Attributes (properties, slots, roles) http://www.w3.org/2000/01/rdf-schema#Property
Instances (objects) http://www.w3.org/1999/02/22-rdf-syntax-ns#type
Linked Data Tutorial http://OntoWiki.net 16.11.2007 dc:creator ?

RDF-S Class & Property Hierarchies
Beer rdf:type rdfs:Class
BottomFermentedBeer rdfs:subClassOf Beer
Bock rdfs:subClassOf BottomFermentedBeer
Lager rdfs:subClassOf BottomFermentedBeer
Pilsner rdfs:subClassOf BottomFermentedBeer
Linked Data Tutorial hasContent rdf:type rdfs:Property hasAlcoholicContent rdfs:subPropertyOf Beer hasOriginalWortContent rdfs:subClassOf BottomFermentedBeer

RDF-S Properties
… are defined and used independently from classes
Domain: Association with one or multiple classes
Range: defines values the property can assume
Instances of a certain class
literals typed with a certain XML schema data type
Linked Data Tutorial hasAlcoholicContent rdf:type owl:DatatypeProperty hasAlcoholicContent rdf:type owl:FunctionalProperty hasAlcoholicContent rdfs:domain Beer hasAlcoholicContent rdfs:range xsd:float hasAlcoholicContent rdfs:subPropertyOf hasContent brews rdf:type owl:ObjectProperty brews rdfs:domain Brewery brews rdfs:range Beer

RDF-S Instances
Are associated to one (or multiple) class(es) :
Linked Data Tutorial Boddingtons rdf:type Ale Grafentrunk rdf:type Bock Hoegaarden rdf:type White Jever rdf:type Pilsner

Semantic Web Layer Cake Linked Data Tutorial

Linked Data - Paradigm
Use URIs as names for things
Use HTTP URIs so that people can look up those names.
When someone looks up a URI, provide useful information.
Include links to other URIs. so that they can discover more things.

Linked Data – Publishing RDF
De-referenceable RDF-URIs, e.g.: http://dbpedia.org/resource/Busan
Different HTTP response depending on HTTP-Accept-Header

Benefits of using the RDF Data Model in the Linked Data Context
Clients can look up every URI in an RDF graph over the Web to retrieve additional information.
Information from different sources merges naturally.
The data model enables you to set RDF links between data from different sources.
The data model allows you to represent information that is expressed using different schemata in a single model.
Combined with schema languages such as RDF-S or OWL, the data model allows you to use as much or as little structure as you need, meaning that you can represent tightly structured data as well as semi-structured data.

Linking Open Data (LOD) Cloud Linked Data Tutorial

Data Web Moving Targets
Base technologies (RDF, SPARQL, HTTP etc.) are developed, standardized and ready to use
Big issues:
Scalability
User interfaces
Search engines
Business models
(Reasoning)

Data Web Business Models
Advertisement (page view) based businesses will probably not be first movers 
Large Web companies will probably not be first movers 
Data Web should focus on fragmented markets with many players which require widest distribution of information , e.g. realtors, online shops, transportation service providers, public information, geo data etc.

Overview

Triplify Motivation
growth of semantic representations still outpaced by the traditional Web
overcome the chicken-and-egg dilemma of missing semantic representations and search facilities on the Web
Triplify leverages relational representations behind existing Web applications:
often open-source, deployed hundred thousand times
structure and semantics encoded in relational database schemes (behind Web apps) is not accessible to Web search engines, mashups etc.
Linked Data Tutorial Monthly Web application downloads at Sourceforge

Triplify Big Picture Linked Data Tutorial

Triplify Approach: Simplicity
Expose semantics as simple as possible
No (new) mapping languages
Few lines of code – easy to plug-in
Simple, reusable configurations
Available for most popular Web app languages
PHP (ready), Ruby/Python under development
Works with most popular Web app DBs
MySQL (extensively tested), PHP-PDO DBs (SQLite, Oracle, DB2, MS SQL, PostgreSQL etc.) should work, not needed for Virtuoso 
Triplify exposes RDF/Ntriples, LinkedData and RDF/JSON

Triplify Solution: SQL-SELECT queries map relational data to RDF
Triplify Configuration:
number of SQL queries selecting information, which should be made publicly available.
Special SQL query result structure required (in order to convert results into RDF:
first column must contain identifiers for generating instance URIs (i.e. the primary key of DB table)
column names are used to generate property URIs , renaming columns allows to reuse properties from existing vocabularies such as Dublin Core, FOAF, SIOC
e.g. SELECT id, name AS ' foaf:name ' FROM users
individual cells contain data values or references to other instances (eventually constitute the objects of resulting triples)

Example: Wordpress Blog Posts
Associate the URL path fragment 'post‘ with a number of SQL patterns:
http://blog.aksw.org/triplify/post/(xxx)
SELECT id, post_author AS 'sioc:has_creator->user' , post_title AS 'dc:title', post_content AS 'sioc:content', post_date AS 'dcterms:modified^^xsd:dateTime‘, post_modified AS 'dcterms:created^^xsd:dateTime'
FROM posts
WHERE post_status='publish‘ ( AND id=xxx)
SELECT post_id id, tag_label AS 'tag:taggedWithTag‘
FROM post2tag INNER JOIN tag ON( post2tag.tag_id=tag.tag_id )
( WHERE id=xxx)
SELECT post_id id, category_id AS 'belongsToCategory->category‘
FROM post2cat
( WHERE id=xxx)
Linked Data Tutorial Object property Datatype property 1 2 3

RDF Conversion Linked Data Tutorial http://blog.aksw.org/triplify/post/1 sioc:has_creator http://blog.aksw.org/triplify/user/5 http://blog.aksw.org/triplify/post/1 dc:title “New DBpedia release” http://blog.aksw.org/triplify/post/1 sioc:content “Today we released …” http://blog.aksw.org/triplify/post/1 dcterms:modified “20081020T1635”^^xsd:dateTime http://blog.aksw.org/triplify/post/1 dcterms:created “20081020T1635”^^xsd:dateTime http://blog.aksw.org/triplify/post/1 tag:taggedWithTag “DBpedia” http://blog.aksw.org/triplify/post/1 tag:taggedWithTag “Release” http://blog.aksw.org/triplify/post/1 belongsToCategory http://blog.aksw.org/triplify/category/34 1 2 3 http://blog.aksw.org/triplify/post/1 id post_author post_title post_content post_date post_modified 1 5 New DBpedia release Today we released … 200810201635 200810201635 id tag:taggedWithTag 1 DBpedia 1 Release .. id belogsToCategory 1 34 …

Example Config
<?php include('../wp-config.php'); $triplify['namespaces'] =array( 'vocabulary'=>'http://triplify.org/vocabulary/Wordpress/', 'foaf'=>'http://xmlns.com/foaf/0.1/', … ); $triplify['queries'] =array( 'post'=>array( " SELECT id,post_author 'sioc:has_creator->user',post_date 'dcterms:created',post_title 'dc:title', post_content 'sioc:content', post_modified 'dcterms:modified‘ FROM {$table_prefix}posts WHERE post_status='publish'", " SELECT post_id id,tag_id 'tag:taggedWithTag' FROM {$table_prefix}post2tag", " SELECT post_id id,category_id 'belongsToCategory' FROM {$table_prefix}post2cat", ), 'tag'=>" SELECT tag_ID id,tag 'tag:tagName' FROM {$table_prefix}tags", 'category'=>" SELECT cat_ID id,cat_name 'skos:prefLabel',category_parent 'skos:narrower' FROM {$table_prefix}categories", 'user'=>array( " SELECT id,user_login 'foaf:accountName', SHA(CONCAT ('mailto:',user_email)) 'foaf:mbox_sha1sum', user_url 'foaf:homepage',display_name 'foaf:name' FROM {$table_prefix}users", " SELECT user_id id,meta_value 'foaf:firstName' FROM {$table_prefix}usermeta WHERE meta_key='first_name'", " SELECT user_id id,meta_value 'foaf:family_name' FROM {$table_prefix}usermeta WHERE meta_key='last_name'", ), 'comment'=>" SELECT comment_ID id,comment_post_id 'sioc:reply_of',comment_author AS 'foaf:name', SHA(CONCAT ('mailto:',comment_author_email)) 'foaf:mbox_sha1sum', comment_author_url 'foaf:homepage',
comment_date AS 'dcterms:created', comment_content 'sioc:content',comment_karma,comment_type FROM {$table_prefix}comments WHERE comment_approved='1'", ); $triplify['objectProperties'] =array( 'sioc:has_creator'=>'user', 'tag:taggedWithTag'=>'tag', 'belongsToCategory'=>'category‘,'skos:narrower'=>'category','sioc:reply_of'=>'post'); $triplify['classMap'] =array('user'=>'foaf:person', 'post'=>'sioc:Post', 'tag'=>'tag:Tag', 'category'=>'skos:Concept'); $triplify['TTL'] =0; // Caching $triplify['db'] =new PDO('mysql:host='.DB_HOST.';dbname='.DB_NAME,DB_USER,DB_PASSWORD);
?>

Triplify Temporal Extension
Problem: How do next generation search engines know something changed on the Data Web?
Different solutions:
Try to crawl always everything : currently deployed on the Web
Ping a central update notification service: PingTheSemanticWeb.com – will probably not scale if the Data Web gets really deployed
Each linked data endpoint publishes an update log: Triplify Update Logs

Triplify Temporal Extension
http://example.com/Triplify/update
http://example.com/Triplify/update/2007 rdf:type update:UpdateCollection .
http://example.com/Triplify/update/2008 rdf:type update:UpdateCollection .
http://example.com/Triplify/update/2008
http://example.com/Triplify/update/2008/Jan rdf:type update:UpdateCollection .
http://example.com/Triplify/update/2008/Feb rdf:type update:UpdateCollection .
Nesting continues until we finally reach an URL, which exposes all updates performed in a certain second in time…
http://example.com/Triplify/update/2008/Jan/01/17/58/06
http://example.com/Triplify/update/2008/Jan/01/17/58/06/user123
update:updatedResource http://example.com/Triplify/users/JohnDoe ;
update:updatedAt "20080101T17:58:06"^<xsd:dateTime> ;
update:updatedBy http://example.com/Triplify/users/JohnDoe .
Linked Data Tutorial special update path and vocabulary

Triplify Spatial Extension
How to publish geo-data using Triplify?
OpenStreetMaps – 160 GB Geo Data lots of POIs – hotels, gas stations, universities …
http://LinkedGeoData.org/near/48.213056,16.359722/1000/Hotel
http://LinkedGeoData.org/point/212331
Linked Data Tutorial Lon Lat Radius Tag

RDB2RDF tool comparison Linked Data Tutorial More at: http://esw.w3.org/topic/Rdb2RdfXG/StateOfTheArt Tool Triplify R2DQ 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

Marrying DBs with RDF & Ontologies
Using DBs for storage and querying of RDF & ontologies
Linked Data Tutorial Publishing DB content as RDF Relational Databases RDF & Ontologies Data Model Relational (tables, columns, rows) Triples (subject, predicate, object) Schema and data separation   Implicit information   Scalability   Schema flexibility   Web data integration readiness  

Overview

Transforming Wikipedia into a Knowledge base
☺ Wikipedia is the 8th most popular website (according to Alexa.com)
☺ Maybe the finest example of truly collaboratively created content (>8M articles in >200 languages written by >300.000 authors)
☺ Covers all possible topics and domains, articles are a result of a “community consensus”
Θ Many inconsistencies can be found on different pages/language versions
Θ Not very well integrated with other data sources
Θ Lacks structured representations of content which facilitate querying and search
Simple Questions – hard to answer:
What have the Art Nouveau and Berlin in common ?
Who are mayors of central European towns elevated more than 1000m ?
Which films are longer than 4 hours and had a budget of less than $1 Million ?
The information required to answer these is contained in Wikipedia !
How can we reveal structure and semantics of Wikipedia content?

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 Linked Data Tutorial

Class Hierarchy
200k people (70k athletes, 65k artists, 18k office holders)
193k places (100k areas, 40k cities, 10k rivers)
187k works (71k music albums, 24k singles, 31k films, 15k books)
87k species
70k organisations (20k educational institutions, 18k companies, 12k radio stations)
22k buildings (8k airports, 5k stations, 2k stadiums, 1k bridges)
12k planets
And more… (events, diseases, proteins, drugs, aircrafts, automobiles, ships, astronaut, architect, scientists)

Extraction results
Extraction algorithm with the English Wikipedia content ( http://dumps.wikimedia.org/enwiki )
<1h needed to extract templates and convert them to RDF (>2M English Wikipedia articles, >10GB raw data)
roughly 30M facts extracted from infobox templates alone
Sample checks reveal: ~ 90% accuracy , 9% redundant information, 1% erroneous
multi-domain ontology covering a large body of domains
extraction results and source code of the extraction algorithm available at http://dbpedia.org
Linked Data Tutorial Dataset (en) Triples Articles 7.6M Abstracts 2.1M External Links 3.2M Categories 7.3M Infoboxes 29.3M Persons 560k Yago Classes 2M Wordnet Classes 338k Geo-coordinates 450k Mapping to Flickr, DBLP, Eurostat, CIA-Factbook, Musicbrainz, Project Gutenberg, US Census, … 100k Mapping to OpenCyc 45k

DBpedia Components Wikipedia Dumps Article texts DB tables Infobox Articles Categories … DBpedia datasets SPARQL Endpoint Query Builder SNORQL Browser Traditional Web Browser Web 2.0 Mashups Virtuoso MySQL Extraction loaded into published via … Linked Data … Semantic Web Browsers OpenCyc Wordnet Freebase Geonames … … … interlinked with other open data Linked Data Tutorial

User Interfaces Linked Data Tutorial

DBpedia SPARQL Endpoint (1)
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 (2)
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

Overview
Virtuoso – Knowledge Store

OntoWiki
Semantic Wiki
Differences
Similarities
Architecture
Use Cases

Semantic Wiki
Wiki with added semantics
Goal: Wiki pages + background knowledge base
Examples: Semantic MediaWiki , Rhizome, IkeWiki

Conceptual Differences: Views over Articles Wiki articles Linked Data Tutorial Resource views

Conceptual Differences: Forms over Code Wiki code Linked Data Tutorial Forms

Conceptual Similarities: Wikiwiki Concepts
Everyone can edit anything
Content is edited in the same way as structure is
Activity can be watched and reviewed by everyone
Ward Cunningham Linked Data Tutorial

Versioning
Everything can be undone
Philosophy: make it easy to correct mistakes

OntoWiki Application Framework: Interfaces
SPARQL Endpoint
Linked Data Endpoint
WebDAV
REST API
Command Line Interface
LDAP

Extensibility
Plugins
Views/Templates
Themes
Localizations

Access Control
Model-based
Action-based
(Statement-based)

Other Features
Facet-based browsing
Inline editing
Auto-adaptive user interface
Resource auto-suggestion
SPARQL Query Editor

Architecture Linked Data Tutorial

Vision
Generic data wiki for RDF models
no data model mismatch (structured vs. unstructured)
Application framework for:
Knowledge-intensive applications
Agile processes
Distributed user groups

SoftWiki* Linked Data Tutorial Problem: Requirements Engineering with large, spatially distributed stakeholder groups Solution: comprehensive ontology for representing RE relevant knowledge + adapted OntoWiki application Application of text-mining methods for duplicate detection * Work in BmbF funded project with UniDuE, T-Systems, QA-Systems, LeCoS, ProDV

Caucasian Spiders
Faunistic database on spiders of the Caucasus
Taxonomy
Localities
240k triples

Professor Catalogue
Professor catalogue with 800 entries and 60 schema elements
OntoWiki used as backend for data entry
Custom front-end

Semantic Wikis: Related Work Linked Data Tutorial OntoWiki Semantic MediaWiki IkeWiki Main developer Uni Leipzig AKSW AIFB Karlsruhe Salzburg Research Technology PHP/MySQL
PHP/MySQL (MediaWiki extension)
Java/Postgres Base artifacts Facts
(annotated) texts
(annotated) texts Authoring WYSIWIG facts / forms Wiki syntax / semantic forms WYSIWIG / forms Other Data Web development framework Planned Wikipedia deployment Visual KB browser

Vakantieland*
One of the largest tourist information sites in NL (>100.000 daily page views, >20.000 points of interest)
Traditional relational DB system was to inflexible to capture the increasingly heterogeneous content types
Development of an OntoWiki based Data Web application
Geo-data integration from OpenStreetMaps
Semantic-Search
Integration of DBpedia data
Comprehensive performance tuning
* work with Ceriel Jakobs, Michael Martin partially funded by SenterNovem

Overview
Open Street Maps – linked open geo data

Linked Open Geo Data
Spatial data is crucial for the Data Web in order to interlink geographically linked resources.
Open Street Map project (OSM) collects, organizes and publishes geo data the wiki way:
80.000 OSM users collected data about 22M km ways (roads, highways etc.) on earth , 25T km are added daily
OSM contains a vast amount points-of-interest descriptions e.g. shops, amenities, sports venues, businesses, touristic and historic sights.
Goal: publish OSM geo data, interlink it with other data sources and provide efficient means for browsing and authoring:
Open Street Map data extraction works on the basis of OSM database dumps, a bi-directional live integration of OSM and our Linked Geo Data browser and editor is currently in the works.
Triplify spatial data publishing , the Triplify script for publishing linked data from relational databases is extended for publishing geo data, in particular with regard to the retrieval of information about geographical areas.
LinkedGeo Data browser and editor is a facet-based browser for geo content, which uses an OLAP inspired hypercube for quickly retrieving aggregated information about any user selected area on earth.

Faceted Linked-Geo-Data Browser Linked Data Tutorial

AKSW Linked Data Web Building Blocks DBpedia “ Semantification” of Wikipedia Linked Data Tutorial Triplify “ Semantification” of (small) Web Applications OntoWiki Collaborative creation of explicit knowledge via Semantic Wikis OWLDB Extending DBs for ontology handling / revealing implicit information Vakantieland Building Data Web applications SoftWiki Distributed, stakeholder driven Requirements Engineering Foundations Marrying databases with RDF and ontologies Tools Applications Bringing the Data Web to end users
RDF Query Subsumption & View Maintenance
Scaling database backed Triple Stores
xOperator Combining Instant Messaging with the Data Web OpenResearch.org A semantic Wiki for the sciences … DL-Learner Machine Learning for Ontologies

Thanks!
Dr. S ö ren Auer
[email_address]
Research group Agile Knowledge Engineering & Semantic Web (AKSW): http://aksw.org
http://Triplify.org
http://DBpedia.org
http://OntoWiki.net
http://OpenResearch.org
http://aksw.org/projects/xOperator
DL-Learner.org
Cofundos.org

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Linked Data Tutorial

by Sören Auer on Jan 26, 2009

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