Linked Data Tutorial

Linked Data Tutorial - Presentation Transcript

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!