Download - (13) Semantic Web Technologies - Linked Data & Semantic Search

Semantic Web Technologies

LectureDr. Harald Sack

Hasso-Plattner-Institut für IT Systems EngineeringUniversity of Potsdam

Winter Semester 2012/13

Lecture Blog: http://semweb2013.blogspot.com/This file is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0)

Dienstag, 22. Januar 13

http://semweb2013.blogspot.com


http://creativecommons.org/licenses/by-nc/3.0/

http://creativecommons.org/licenses/by-nc/3.0/

Semantic Web Technologies , Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

2 1. Introduction 2. Semantic Web - Basic Architecture

Languages of the Semantic Web - Part 1

3. Knowledge Representation and LogicsLanguages of the Semantic Web - Part 2

4. Applications in the ,Web of Data‘

Semantic Web Technologies Content


Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

3

Ontolo

gical

Engine

ering



4

Linked

Data

Applic

ations

&

Semant

ic Sea

rch



4. Applications in the Web of Data4.1.Ontological Engineering4.2.Linked Data Engineering 4.3.Semantic Search



Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamTurmbau zu Babel, Pieter Brueghel, 1563

How do we get Data from the Web...?

4.1 Linked Data Engineering4.1.1 APIs vs. Linked Data4.1.2 Linked Data Principles4.1.3 Linked Data @ Work



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How to get Data from the Web?

•Data can only be found on the Web, if it is available at some website

Database

Web-Server

JDBC

HTTPHTML

Browser



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How to get Data from the Web?•There is a anumber of different (proprietary) Web APIs, data exchange formats and Mashups on top of that

Database 1

WebAPI 1

WebAPI 2

WebAPI 3

WebAPI 4

Database 2 Database 3 Database 4

Mashup



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In the Web today...

•Data is locked up in small data islands •Other applications usually cannot acces this data...

Database

Database

Database

Database

Database

Database

Database

Database

Database



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http://www.w3.org/2009/Talks/0204-ted-tbl/#(22)

Problems ahead....





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But there is a solution:•...open up proprietary data islands•...publish all data that are of public interest

•...in a way that •other applications can access, utilize, and process this data,and

•all applications can access additional(meta)data for the available data

Database 1 Database 2 Database 3



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•Apply semantic technologies:•to publish structured data on the web•to draw connections from one data source to data from other data sources

Database 1 Database 2 Database 3 Database 4

RDF Data RDF Data RDF Data RDF Data

RDF Links

RDF Links

RDF Links

But there is a solution:



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Linked Data and the ‘Web of Data‘

■Term refers to an idea originally from Tim Berners-Lee(Tim Berners-Lee, Linked Data, 2006, http://www.w3.org/DesignIssues/LinkedData.html)

□Set of best practices for publication and linking of structured data on the web□Basic assumption: The value of data on the web increases when

they are connected to other data sources

M.Hausenblas, Quick Linked Data Introduction, http://www.slideshare.net/mediasemanticweb/quick-linked-data-introduction

The Web of data is abouta dataand namingmodel on the Web


http://www.w3.org/DesignIssues/LinkedData.html

http://www.w3.org/DesignIssues/LinkedData.html

http://www.slideshare.net/mediasemanticweb/quick-linked-data-introduction

http://www.slideshare.net/mediasemanticweb/quick-linked-data-introduction


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Linked Data Principles(1) Use URIs as names for things.(2) Use HTTP URIs, so that people can look up those

names.(3) When someone looks up a URI, provide useful

information, using the standards (RDF, SPARQL)(4) Include links to other URIs, so that they can discover

more things.



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Linked Data Principles

(1) Use URIs as names for things.

• URIs do not only identify documents but also arbitrary objects of the real world as well as abstract concepts

http://dbpedia.org/resource/Albert_Einstein

http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d






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(2) Use HTTP URIs, so that people can look up those names.

• HTTP URIs (URLs) as globally unique names enable dereferencing of assiciated information in the Web

• via http Content Negotiation• 303 URIs

http Response Code 303 ,See Other‘ (redirect)

• Hash URIshttp://example.com/Harald#me



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Linked Data for Humans and Computers

■URI should deliver information as well as for humans as for computers, i.e.

URI

Accept: application/rdf+xml Accept: text/html

(Thing)

(RDF data) (HTML page)


http://dbpedia.org/resource/Ernest_Hemingway









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■Server delivers different HTTP responses dependent ofHTTP-Accept-Header (Content Negotiation)

http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/






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■URI should deliver information as well as for humans as for computers, i.e.

Accept: application/rdf+xml Accept: text/html

(Thing)

(RDF data) (HTML page)


http://dbpedia.org/data/Ernest_Hemingway.rdf

http://dbpedia.org/page/Ernest_Hemingway




















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(3) When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL)

• RDF as universal data model for publishing structured data on the Web

• Make all URIs in the RDF graph dereferencable• Avoid RDF constructs that cause problems in Linked Data

context

• RDF Reification• RDF Collections und Containers• unnamed Blank Nodes



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(4) Include links to other URIs, so that they can discover more things.

• Link RDF references among data between different data sources, to find information related by content

• Relationship LinksLinks to external LOD Entitites related with the original entity

• Identity LinksLinks to external LOD Entities referring to the same object or concept

• Vocabulary LinksLinks to definitions of the original entity



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The application of the Linked Data Principles leads to a ,Web of Data‘



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Development of the ,Web of Data‘

May 2007



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Nov 2007




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July 2009




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September 2010




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September 2011

300 Datasets 31B RDF Triples504M Links




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□Semantic Mashups are applications that use linked RDF data from various data sources

□ in difference to interfaces and exchange formats or ordinary Web APIs, Linked Data offers the following benefits:□ a flexible and standardized data format (RDF)□ standardized access mechanism (http)□ possibility to put links (RDF-Links) among different data sources

» enables navigation» is supported by search engines (Crawler)» enables expressive search facilities over the crawled data

and beyond

S. Auer, J. Lehmann, Ch. Bizer: Semantitsche Mashups auf Basis vernetzter Daten, in T. Pellegrini, A. Blumauer (Hrsg.): Social Semantic Web, Springer, 2009.

Semantic Mashups



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Linked Data Sources in the Web

□Native publication□ D2R-Server, OpenLink Virtuoso, Pubby, etc.

□ Implementation of Wrappers around existing applications / APIs□SIOC Exporter for Wordpress, Drupal, phpBB,...□RDF Book Mashup (Amazon API, Google Base-API,...)

□Linking Open Data Project□Semantic Web Education and Outreach W3C working group □Catalogue of all known sources of linked data with an open

source license» DBPedia, Flickr, Open-Cyc, FOAF, SIOC, GeoNames, ...



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Browser for Linked Data

■Differences to arbitrary RDF-Browsers□RDF Data to be visualized does not necessarely reside in

local repository, but is distributed in the Web□ requires dynamic reload of RDF resources■Tabulator (Tim Berners-Lee, MIT-)

(T. Berners-Lee et al.: Tabulator: Exploring and analyzing linked data on the semantic web, in Proc. 3rd Int. Semantic Web User Interaction Workshop, 2006, http://swui.semanticweb.org/swui06/papers/Berners-Lee/Berners-Lee.pdf)

■ OpenLink RDF Data Explorer□enables visualization as graph, timeline, map, etc.

http://ode.openlinksw.com/

■Zitgist Browserhttp://browser.zitgist.com/

■DISCO Browserhttp://sites.wiwiss.fu-berlin.de/suhl/bizer/ng4j/disco/


http://ode.openlinksw.com

http://ode.openlinksw.com

http://sites.wiwiss.fu-berlin.de/suhl/bizer/ng4j/disco/



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Search Engines for Linked Data■Crawler-based, follow links in datasets to create an index that

can be queried

■Swoogle□ keyword-based full text searcg (Apache-Lucene), uses only limited

semantic annotationhttp://swoogle.umbc.edu/

■ Semantic Web Search Engine (SWSE)□ additionally uses rdf:type properties as search filter

http://swse.deri.org/

■Sindicehttp://www.sindice.com/

■ Falcons□with data browser for result analysis

http://iws.seu.edu.cn/services/falcons/

■Sig.ma - Semantic Information Mashup (based on Sindice)http://sig.ma/


http://swoogle.umbc.edu

http://swoogle.umbc.edu

http://swse.deri.org

http://swse.deri.org

http://www.sindice.com

http://www.sindice.com



http://sig.ma

http://sig.ma


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http://dbpedia.neofonie.com


http://dbpedia.neofonie.com/

http://dbpedia.neofonie.com/


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Linked Open Data■ public Linked Data Resourcen in the Web, licensed as

„Creative Common CC-BY“ ■ 5-Star Criteria for Linked Open Data

Available on the web (whatever format) but with an open licence, to be Open Data

Available as machine-readable structured data (e.g. excel instead of image scan of a table)

as (2) plus non-proprietary format (e.g. CSV instead of excel)

All the above plus, Use open standards from W3C (RDF and SPARQL) to identify things, so that people can point at your stuff

All the above, plus: Link your data to other people’s data to provide context

★

★ ★

★ ★ ★★ ★ ★ ★

★ ★ ★ ★ ★



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Linked Open Data



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Linked Data □ordered by categories



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Linked Data

Media

User Generated Content



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Linked Data Publications



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Government

Linked Data

Geographic



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Life Sciences Linked Data Cross-Domain



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Linking Open Data■Some statistics (as of 09/2011)

distribution of RDF Triples by domain



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Linking Open Data■Some statistics (as of 09/2011)

distribution of Links by domain



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Linked Data Ontologien □Ontologies hold the Linked Data Cloud together


Linked Data Ontologien □ z.B. OWL

□owl:sameAs connects identical individuals□owl:equivalentClass connects equivalent classes


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Linked Data Ontologien □ z.B. umbel (version 1.0, Feb. 2011)

□ „Upper Mapping and Binding Exchange Layer“□Subset of OpenCyc

as RDF Triples based on SKOS and OWL2□Upper Ontology with 28.000

concepts (skos:Concept)□46.000 Mappings into

DBpedia, geonames e.a.(owl:equivalentClass, rdfs:subClassOf)□Links to more than 2 Mio Wikipedia

pages


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Linked Data Ontologien □ z.B. SKOS

□ „Simple Knowledge Organization System“□based on RDF and RDFS □applied for definitions and mappings

of vocabularies and ontologies□skos:Concept (clsses)□skos:narrower

□skos:broader

□skos:related

□skos:exactMatch, skos:narrowMatch,skos:broadMatch, skos:relatedMatch



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Linked Data (Research) Applications □WhoKnows

http://apps.facebook.com/whoknows_/

□RISQ!http://141.89.225.43/whoknowsmovies/game.html

□ for Data Cleansing□ for relevance ranking of facts□ for entity summarization




http://141.89.225.43/whoknowsmovies/game.html







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Semant

ic

Search

Albrecht Dürer: Melancholia I, 1514



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4.3 Semantic Search4.3.1 Information Retrieval 4.3.3 Semantic Analysis and Retrieval4.3.4 Exploratory Search



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The ,Google Dilemma‘Dienstag, 22. Januar 13


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Classical Information Retrieval

(nach Salton,G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York 1983)

Set of Documents

files of records

Set of Queries

Information requests

indexing language

similarity

indexingQueryFormulation



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Classical Information Retrieval(simplified version)

Set of documents

search index

searching, vb. , in allen ger n sprachen bezeugt: got.sokjan, ags. sēcan, as. sokian, an. Soekj

[Bd. 20, Sp. 835]

sēza, ahd. suohhan. aus idg. sprachen steht am nächsten lat. sāgiospüre, air. saigim gehe

einer sache nach, suche; zur weiteren verwandtschaft vgl. Walde-Pokorny 2, 449.

der umlaut des stammvokals erscheint im nd., er wird im md. verzeichnet vonCrecelius

oberhess. wb. 827; Spiess henneb. id. 248; Hertel Thüringen240; Gerbet Vogtland 425

und auf kolonialem boden bei Schröerdeutsche mundarten des ungrischen

berglandes 225. neben eigentlichem suchen 'einer sache

nachspüren, sich bemühen, sie aufzufinden' (dann auch 'jemanden

aufsuchen, ihn bedrohen, angreifen') steht eine reich bezeugte bedeutungsgruppe mehr

keywords

„search“?

search query

search term(s)



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relevant documents retrieved documents

relevant documents that have been retrieved

RP

Recall=| R ∩ P |

|R|

Precision=| R ∩ P |

|P|

Fα=(1+α)⋅(Recall ⋅ Precision )

α⋅(Recall + Precision )

Evaluation of Information Retrieval Systems



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Search Engines in the Web

• The World Wide Web is a distributed hypermedia system with•multimedia documents and• linked via hyperlinks



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URL list

http://www.xxxx.de/1234...http://www.xxxx.de/2234...http://www.xxxx.de/3234...http://www.xxxx.de/4234...http://www.xxxx.de/5234...http://www.xxxx.de/6234...http://www.xxxx.de/7234......

<a href=“...“ .../>

<a href=“...“ .../>

HTMLdocuments

WWW-ServerHTTP Request

WWW server delivers requestedHTML documents to the web crawler

1

2

3

4

Web-Crawler (Web Robot)



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Data Normalization

Web Crawler

Data Analysis and creation of

index data structures

Preprocessing and IndexingSearch Engines in the WWW

Tokenization

Speech Identification

Word Stemming

POS-Tagging

Descriptor Generation

Document Preprocessing



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Efficient Index Data Structures

Aachen

Altavista

Ananas

……

Zustand

Zypern

Index

AnanasDocID Pos Frequency Weight

D123 1;13;77;132 4 9.4D456 22;38 2 6.7 … … … …D998 15 1 1.2

Location List D123Frequency URL <H1> … <H6> <title> … text

4 1 1 0 1 … 1

D123 http://producers.ananas.org/index.htm

<html><head><title=“Ananas around the World“></head><body> … </body></html>

Inverted File

File

Search Engines in the WWW



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Relevance Ranking

• Link Popularity (Google PageRank)

A

1.0

D

1.0

B

1.0

C

1.0

Start

Nr. PR(A) PR(B) PR(C) PR(D)1 1,0 1,0 1,0 1,02 1,0 0,575 2,275 0,153 2,083 0,575 1,191

20,15

… … … … …n 1,49 0,7833 1,577 0,15

Iteration of the PageRank computationA

1.49

D

0,15

B

0,78

C

1.57

resulting PageRank

Search Engines in theWWW


Semantic Web Technologies , Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

The Web is big. Really big. You just won't believe how vastly, hugely, mind-bogglingly big it is.(...according to Douglas Adams)

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Language has its fa

llacies...



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in particular,

if we don‘t know the langua

ge



4242 42 4224424242 42 424267




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Definition (first try)Semantic Search

4242244242 • Annotation of (text-based) metadata with semantic entities• Entity-based Information Retrieval• Make use of semantic relations, as e.g. content-based

similarities of relationships• Interoperable metadata via semantic annotations• for content-based description• for structural / technical description

(Multimedia Ontologies)

Overall Goal: Quantitative and qualitative improvement of Information Retrieval


• MPEG-7 has been re-engineered to become an OWL-DL ontology (2007: Arndt et al., COMM model)


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Multimedia OntologiesSemantic Metadata

4242244242

• Localize a region → Draw a bounding box

• Annotate the content → Interpret the content → Tag ,Astronaut‘


4242244242


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Multimedia OntologienSemantic Metadata

Example: Tagging with an MPEG-7 Ontology

Reg1

mpeg7:image

mpeg7:depicts

Man on the Moon

mpeg7:spatial_decomposition Reg1

mpeg7:StillRegion

rdf:type

mpeg7:depicts

dbpedia:Astronaut

mpeg7:SpatialMask

mpeg7:polygon

mpeg7:Coords


4242244242


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Named Entity Recognition

Astronaut Person

Neil Armstrong

Science Occupation

Employment

is a is a

subClassOf

subClassOf

Entities

Classes

Named Entity Recognition„locating and classifying atomic elements...intopredefined categories such as names, persons, organizations, locations, expressions of time,quantities, monetary values, etc.“C.J.Rijsbergen, Information Retrieval (1979)



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Astronaut Person

Neil Armstrong

Science Occupation

Employment

is a is a

subClassOf

subClassOf



4242244242


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Astronaut Person

Neil Armstrong

Science Occupation

Employment

is a is a

subClassOf

subClassOf

„Armstrong was the first man on the Moon.“ Text

Entity Mapping



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Astronaut


Person

Neil Armstrong

Science Occupation

Employment

is a is a

subClassOf

subClassOf

Text

rdfs:label Neil Armstrong

rdf:type dbpedia-owl:Astronaut

rdf:type foaf:Person


4242244242


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Text

http://dbpedia.org/resource/Neil_Armstrong


Entity Mapping

How do I find the right entity?


http://dbpedia.org/resource/Diego_Ribas_da_Cunha

http://dbpedia.org/resource/Diego_Ribas_da_Cunha

4242244242


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In natural language text• nouns correspond to semantic concepts / entities• verbs correspond to semantic relations

Identify nouns in natural language text:• determination of language• Part-of-Speech Tagger• Word Stemming • e.g. with

http://gate.ac.uk/



http://gate.ac.uk/

http://gate.ac.uk/

4242244242


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Text


Armstrong, Florida

Determine possible Entity Mapping Candidates

Armstrong, Ontario

Armstrong County, Texas

Armstrong Tunnel

Louis Armstrong

Armstrong Tools

Armstrong (moon crater)

Armstrong (car)

The Armstrongs

Craig Armstrong

Anton Armstrong

Edward Armstrong

Gary Armstrong

George Armstrong

The Armstrong Twins

Ian Armstrong

+ 400 more...




4242244242


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Text

TemporalContext

SpatialContext

ProvenanceContext

Context provides information for• Disambiguation• Reliability• Trustworthiness

StructuralContext

UserContext

Context Dimensions for Audiovisual Media


4242244242


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Text


Determine Named Entities (nouns) from text

Armstrong

man

moonCreate all possible Sets of Mapping Candidates

• We have to examine the Context to understand the semantics



4242244242


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Text„Armstrong was the first man on the Moon.“ Text

Create all possible sets of Mapping Candidates

Armstrong Man MoonGeorge Armstrong Custer

Neil Armstrong

The Armstrong Twins

Armstrong, Florida

Armstrong, Ontario

Armstrong Automobile

Joe Armstrong

Armstrong County, Texass

Armstrong Gun

Craig Armstrong

Armstrong (Moonkrater)

Louis Armstrong

Armstrong Tunnel

Louis Armstrong International Airport

Armstrong‘s Theorem

Sir Thomas Armstrong

Ian Armstrong

HumanBill Man

Bob Man

David Man

Homer Man

Louise Man

Halber Man

Man ärgere Dich nichtMan Computer

Peter van Man

Daniel Man

Man (album)

Der Moon (Oper)

Moon

Moon Nickel CompanyBrunner Moon

Bernard Moon

Peter Moon

Julian Moon

Ludwig Moon

Violet MoonMoon Technologies

Robert Moon

Henry Moon

Alfred Moon

Chava Moon



4242244242


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Armstrong man moon

(1) Co-occurence Analysis(2) Semantic Analysis(3) Machine Learning

Armstrong, Florida man (Album) Moon Technologies

‣For all possible Combinations do:‣Determine the probability of the co-occurence of a

term combination in an arbitrary text document corpus, as e.g. in the wikipedia

‣Select the entity combination with the maximum probability of co-occurence

? ?


4242244242


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Armstrong man moon

(1) Co-occurence Analysis(2) Semantic Analysis(3) Machine Learning

George Armstrong Custer

Neil Armstrong

Armstrong, Florida

Armstrong, Ontario

Armstrong Gun

Craig Armstrong

Armstrong (Moonkrater)

Louis Armstrong

Sir Thomas Armstrong

Human

Bob Man

David Man

Homer Man

Louise Man

Half Man

Dead Man WalkingMan Machine

Man (album)

The Moon (Opera)

moon (planet)

Moon Nickel CompanyBrunner Moon

Bernard

Peter Moon

Julian Moon

Ludwig Moon

Henry Moon

Alfred Moon

Chava Moon



How to use semantic data in Retrieval?



Semantic metadata enable an improvement of traditional keyword-based retrieval by(1) Query String Refinement

enables more precise or more complete search results(2) Cross Referencing

enables to complement search results with additional associated or similar information

(3) Exploratory Search enables visualization and navigation of the search space

(4) Reasoningenables to complement search results with implicitly given information



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Searching is not always

just searching



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I‘m looking for the book „Brave New World“ by Aldous Huxley in the first German edition...

Brave New World. - Aldous H U X L E Y.

- The Albatros Continental Library, 47

(Hamburg usw., Albatros Verlag, 1933)

257 S. 8“

II 1, 2506, 34548



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I really liked „Brave New World“ by Aldous Huxley but how should I find what to read next...?



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Exploratory Search• What, if the user does not know, which query string to use?• What, if the user is looking for complex answers ?• What, if the user does not know the domain he/she is looking for?• What, if the user wants to know all(!) about a specific topic?

• ...,Browsing‘ instead of ,Searching‘• ...to find something by chance, i.e. Serendipity• ...to get an overview• ...enable content based navigation



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Gather knowledge about dbpedia:Brave_New_Worldand decide, which interesting fact to follow....

http://dbpedia.org/page/Brave_New_World

Enable Exploratory Search based on Linked Open Data








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dbpedia:Brave_New_World

dbpedia-owl:author

dbpedia:Aldous_Huxley

dbpe

dia-

owl:a

utho

r

dbpedia-owl:au

thor

dbpedia-owl:author



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dbpedia:Brave_New_World

dbpedia-owl:author


dbpe

dia:

onto

logy

/influ

ence

s

dbpedia:H._G._Wells

dbpedia:ontology/in

fluences

dbpedia:George_Orwell

dbpedia:ontology/influences

dbpedia:Michel_Houellebecq



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dbpedia:H._G._Wells dbpedia:George_Orwell dbpedia:Michel_Houellebecq

dbpedia-owl:notableWork

dbpedia:Les_Particules_élémentaires


dbpedia:Nineteen_Eighty-Four


dbpedia:The_Time_Machine



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dbpedia-owl:author


...and now please surprise me.....SERENDIPITY

dbpedia:Tim_Berners-Leerdf:type

dbpedia:World_Wide_Web

dbpprop:inventor

Yago:EnglishExpatriatesInTheUnitedStates

rdf:type

rdf:type

dbpedia:Patrick_Stewart

dbpedia:Star_Trek:_The_Next_Generation

dbpedia-owl:starring



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Explorative Search

dbpedia-owl:mission

dbpedia:Neil_Armstrong

dbpedia:Apollo_11dbpedia-owl:mission

category:Apollo_program

dcterms:subject

dbpedia:Apollo_13

dcterms:subject

yago:Space_accidents_and_incidents

rdf:type

rdf:type

dbpedia:Space_Shuttle_Challenger

dbpedia-owl:mission

dbpedia:Buzz_Collins

dbpedia:Michael_Collins


Exploratory Search and Serendipity

•Find something that you were not looking for on purpose ...

dbpedia:Buzz_Collins

dbpedia:Cookie_Monster

dbpedia:Strictly_Come_Dancing



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Exploratory Search with yovisto

Waitelonis, Sack: Augmenting Video Search with Linked Open Data, in Proc. I-Semantics , Graz 2009.

http://mediaglobe.yovisto.com:8080/





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http://mediaglobe.yovisto.com:8080/mggui/#start





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4. Semantic Web Anwendungen4.2 Linked Data Engineering4.3 Semantic Search

Literature

• T. Heath, Ch. BitzerLinked Data - Evolving the Web into a Global Data Space,Morgan & Claypool, 2011.


http://www.amazon.de/gp/product/1608454304/ref=as_li_ss_tl?ie=UTF8&tag=moresemantic-21&linkCode=as2&camp=1638&creative=19454&creativeASIN=1608454304

http://www.amazon.de/gp/product/1608454304/ref=as_li_ss_tl?ie=UTF8&tag=moresemantic-21&linkCode=as2&camp=1638&creative=19454&creativeASIN=1608454304


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□Bloghttp://semweb2013.blogspot.com/

□Webseitehttp://www.hpi.uni-potsdam.de/studium/lehrangebot/itse/veranstaltung/semantic_web_technologien-3.html

□bibsonomy - Bookmarkshttp://www.bibsonomy.org/user/lysander07/swt1213_13

4. Semantic Web Anwendungen4.2 Linked Data Engineering4.3 Semantic Search




http://www.hpi.uni-potsdam.de/studium/lehrangebot/itse/veranstaltung/semantic_web_technologien-3.html






http://www.bibsonomy.org/user/lysander07/swt1213_13




Download - (13) Semantic Web Technologies - Linked Data & Semantic Search

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