Semantic Search

Semantic Search(Semantische Suche)Bonn, 12. März 2013

Dr. Harald SackHasso-Plattner-Institut for IT-Systems Engineering

University of Potsdam

Donnerstag, 14. März 13

vfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut Potsdam

■ HPI was founded in October 1998 as a public-private-partnership

■ HPI research and teaching is focussed onIT Systems Engineering

■ 10 professors and 100 scientific coworkers■ 450 bachelor / master students ■ Winner of CHE-ranking 2010

Hasso Plattner Institute for IT Systems EngineeringUniversity of Potsdam

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■Research Topics■Semantic Web Technologies■Ontological Engineering■ Information Retrieval■Multimedia Analysis & Retrieval■Social Networking■Data/Information Visualization

■Research Projects:

Research Group Semantic Technologies & Multimedia Retrieval


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Semantic Search

Inhalt:■ Introduction■ Media Analysis ■ Semantic Analysis■ Semantic Search■ Explorative Search■ Realization

Albrecht Dürer: Melancholia I, 1514


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

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The ‘Google Dilemma‘Donnerstag, 14. März 13


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Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, Workshop ,Corporate Semantic Web‘, XInnovations 2011, Berlin, 19. Sep. 2011Google Multimedia Search


Harald Sack, Hasso-Plattner-Institute for IT-Systems Engineering, LDW 2011, Magdeburg, 30. Sep. 2011

Content Based Retrieval is Based on Textual Metadata

Google Multimedia SearchDonnerstag, 14. März 13


Seach by Media Content



The Ordinary Archive is a Small World...

Jules Verne



But, wouldn‘t it be nice, if.....

Jules Verne

...but maybe you are also interested in- George Melies (2 videos)- Mark Twain (1 video)- H.G. Wells (2 videos)- science fiction (11 videos)- adventure (20 videos)- France (101 videos)- Moon (33 videos)- literature (434 videos)- art (1.205 videos)



(Traditional)Information Retrieval



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(Simplified) Information Retrieval Model

(acc. to 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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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 World Wide Web

• The World Wide Web is a distributed hypermedia system that

•consists of multimedia documents and• is connected 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

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Information Gathering via Web Crawler (Robot)Search Engines in the WWW



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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

• based on 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


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Semantic Search




Step 1: Digitization of analog media

Step 2: Annotation with (text-based) metadata

Searching a (Multi) Media Archive

Step 3: Content-based Retrieval based on available metadata



Today: Manual Annotation



VisualConceptDetection

Text Recognition

Visual Analysis

Automated Media Analysis

Face Detection

Face Detection

Audio-Mining

structuralanalysis

AutomatedSpeech

Recognitionaudio event detection

Logo Detection



■Result: multimedia data with spatio-temporal Annotations

temporal metadata (e.g. MPEG-7) ... <Video> <TemporalDecomposition> <VideoSegment> <TextAnnotation> <KeywordAnnotation> <Keyword>Astronaut</Keyword> </KeywordAnnotation> </TextAnnotation> <MediaTime> <MediaTimePoint> T00:05:05:0F25 </MediaTimePoint> <MediaDuration> PT00H00M31S0N25F </MediaDuration> </MediaTime> ... </VideoSegment> </TemporalDecomposition> </Video> ...

time




■Result: multimedia data with spatio-temporal Annotations

time


spatial metadata (e.g. MPEG-7) ... <SpatialDecomposition> <TextAnnotation> <KeywordAnnotation> <Keyword>Astronaut</Keyword> </KeywordAnnotation> </TextAnnotation> <SpatialMask> <SubRegion> <Polygon> <Coords> 480 150 620 480 </Coords> </Polygon> </SubRegion> </SpatialMask> ... </SpatialDecomposition> ...


vfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut PotsdamDonnerstag, 14. März 13


• Authoritative Metadata• structured data• semi-structured data

• natural language text • Non-authoritative Metadata

• (free) user tags and comments• restricted vocabularies

• (Media) Analysis Metadata• low level features• high level features

• etc.

How to Determine the Meaning of Metadata?

SemanticAnalysis

reliability

context

pragmatics

location dependency

accuracy

timedependency

level ofabstraction


4242 42 4224424242 42 424231

Semantic Search



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


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4242 42 4224424242 42 4242Multimedia OntologiesSemantic Metadata

• Localize a region → Draw a bounding box

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



4242 42 4224424242 42 424233

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

“Neil Armstrong“

mpeg7:SpatialMask

mpeg7:polygon

mpeg7:Coords

Multimedia OntologiesSemantic Metadata


Neil Armstrong

Astronaut

is a

Person

is a

Science Occupation

subClassOf

Employment

subClassOf

Entities

Ontologies

has an

,Neil Armstrong‘ is more than just a character string

Kosmonautsame as

Juri Gagarin

is a

is NOT a


Where does the knowledge come from...?


Web of Data = Linked Open Data


Where does the knowledge come from...?

:Neil_Armstrong rdf:type dbpedia-owl:Astronaut .

subject property object

:Neil_Armstrongrdf:type

dbpedia-owl:Astronaut



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Astronaut

Named Entity Mapping

Person

Neil Armstrong

Science Occupation

Employment

is a is a

subClassOf

subClassOf

rdfs:label Neil Armstrong

rdf:type dbpedia-owl:Astronaut

rdf:type foaf:Person


4242 42 4224424242 42 4242


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Semantic Multimedia Retrieval

Video Analysis /Metadata Extraction

timemetadata

metadatametadata

metadatametadata

e.g., person xylocation yzevent abc

e.g., bibliographical data,geographical data,encyclopedic data, ..

Entity Recognition/ Mapping

N. Ludwig, H. Sack: Named Entity Recognition for User-Generated Tags. In Proc. of the 8th Int. Workshop on Text-based Information Retrieval, IEEE CS Press, 2011


4242 42 4224424242 42 4242


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http://dbpedia.org/resource/Neil_Armstrong

Semantic AnalysisNamed Entity Mapping

„Armstrong landed the Eagle on the Moon.“ Text

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

Semantic Web Technologies , Dr. Harald Sack, Hasso Plattner Institute, University of Potsdam

Armstrong


4242 42 4224424242 42 4242Semantic AnalysisNamed Entity Mapping

„Armstrong landed the Eagle on the Moon.“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...

Neil Armstrong

Armstrong Bridge

Lance Armstrong

Armstrong, Ontario

Entity Candidate Generation




Determine possible Entity Mapping Candidates• linguistic analysis (POS tagging)• normalization• encoding and spelling• special (language dependent) characters• language dependent spellings• abbreviations, acronyms• type dependent spellings• alternative names and synonyms• fuzzy string mapping• ...

Entity Candidate Generation




Entity Selection is determined by• context• ambiguity of source data / mapping• accuracy /reliability of source data / mapping

Armstrong, Florida

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

Ian Armstrong

Neil Armstrong

Armstrong Bridge

Armstrong, Ontario

Entity Selection Process



TemporalContext

SpatialContext

Context Item

SocialContext

Contextual Description

ClassDiversity

Level of Structure

SourceReliability

SourceDiversity

Context Dimensions

Ambiguity Accuracy

influences influences

Relevance

determines

N.Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013


SEMEX Multimedia Context Model


Armstrong

George Armstrong Custer

Neil Armstrong

The Armstrong Twins

Armstrong, Florida

Armstrong, Ontario

Armstrong Automobile

Joe ArmstrongArmstrong County, Texass

Armstrong Gun

Craig Armstrong

Armstrong (Moon Crater)

Louis Armstrong

Armstrong Tunnel

Louis Armstrong International Airport

Armstrong‘s Theorem

Sir Thomas Armstrong

Ian Armstrong

Eagle Moon

Eagle (Bird)

Eagle (heraldry)

USCGC Eagle

The Eagle (2011 film)

Eagle (song)

John H. EagleEagle (typeface)

Eagle Falls (Washington)

Eagle (Moon Crater)

Eagle (comic)

Eagle (lunar module)

Eagle TV

Armstrong Tunnel

The Eagle (Pub)

War Eagle

The Eagle (newspaper)

Eagle (racehorse)

Angela EagleLinda Eagle

James Philipp Eagle

95 entities448 entities

Armstrong (British Columbia)Karen Armstrong

Curtis Armstrong

Gillian Armstrong Hilary Armstrong

William L. Armstrong

156 entities

Man on the Moon (film)

Moon (song)

Moon Son-Ri

C Moon

The Moon (Tarot card)

Edgar Moon

Moon OSMoon (Band)

Moon

Moon 44

Man on the Moon (soundtrack)

William Moon

Lottie Moon

Mr. Moon (song)

Man on the Moon (musical)

Darvin Moon

Moon 83

Francis MoonGary Moon

Robert Charles Moon

Black Moon

Allan Moon

Ban-Ki Moon

Fly me to the Moon (song)

Semantic AnalysisNamed Entity Mapping

„Armstrong landed the Eagle on the Moon.“

Consider all entities within the same context


Select matching entities from all possible candidate entities: • Popularity based strategies• Linguistical strategies• Statistical strategies• Semantic based strategies

General Approach1. Make an assumption 2. Do the strategies support or contradict your assumption3. Make decision according to logical and probabilistic rules/constraints

Semantic AnalysisNamed Entity Recognition

N. Ludwig, H. Sack, “Named entity recognition for user-generated tags,TIR 2011

• reference text corpus(wikipedia)

• link graph (wikipedia)• semantic graph

(dbpedia)

Entity Selection Process


Armstrong

George Armstrong Custer

The Armstrong Twins

Armstrong, Florida

Armstrong, Ontario

Armstrong Automobile

Joe ArmstrongArmstrong County, Texass

Armstrong Gun

Craig Armstrong

Armstrong (Moon Crater)

Armstrong Tunnel

Louis Armstrong International Airport

Armstrong‘s Theorem

Sir Thomas Armstrong

Ian Armstrong

Eagle Moon

Eagle (Bird)

Eagle (heraldry)

USCGC Eagle

The Eagle (2011 film)

Eagle (song)

John H. EagleEagle (typeface)

Eagle Falls (Washington)

Eagle (Moon Crater)

Eagle (comic)

Eagle TV

Armstrong Tunnel

The Eagle (Pub)

War Eagle

The Eagle (newspaper)

Eagle (racehorse)

Angela EagleLinda Eagle

James Philipp Eagle

95 entities448 entities

Armstrong (British Columbia)Karen Armstrong

Curtis Armstrong

Gillian Armstrong Hilary Armstrong

William L. Armstrong

156 entities

Man on the Moon (film)

Moon (song)

Moon Son-Ri

C Moon

The Moon (Tarot card)

Edgar Moon

Moon OSMoon (Band)

Moon 44

Man on the Moon (soundtrack)

William Moon

Lottie Moon

Mr. Moon (song)

Man on the Moon (musical)

Darvin Moon

Moon 83

Francis MoonGary Moon

Robert Charles Moon

Black Moon

Allan Moon

Ban-Ki Moon

Neil Armstrong

Eagle (lunar module)

Moon

Louis Armstrong

Fly me to the Moon (song)

Semantic AnalysisNamed Entity Recognition

„Armstrong landed the Eagle on the Moon.“

N. Steinmetz, H.Sack: Semantic Multimedia Information Retrieval Based on Contextual Descriptions, 2013

Entity Selection Process(Semantic) Graph Analysis


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

Semantic Search



vfm - Seminar: Metadatenmanagement in Medienunternehmen, 05. September 2012, Bonn Jörg Waitelonis, Hasso-Plattner-Institut PotsdamTurmbau zu Babel, Pieter Brueghel, 1563

How to use semantic metadata in retrieval?



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(One of many Definitions...)Semantic Search

• 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


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

Semantic metadata enable improvement of traditional keyword-based retrieval by(1) Query String Extension/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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Semantic SearchQuery String Extension

• Keyword-based search does not deliver all search results that are relevant for a query, because synonyms and metaphors might describe the queried content.

• Extension of the original query string (Query Extension)• from dictionaries and thesauri

• extend query with synonyms, hyponyms (specializations), etc.• from domain ontologies

• extend query with meronyms (part-of), related concepts, etc.

Original query string: Bank

possible extensions: Bank ∨ depository financial institution ∨ credit union ∨ acquirer ∨ federal reserve ∨ ... increase recall



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Semantic SearchQuery String Refinement

• Keyword-based search does also deliver search results that are not relevant for a query, because query terms and document terms might be ambiguous.

• Refinement of the original query string (Query Refinement)• from dictionaries and thesauri

• disambiguate polysemic terms with hypernyms (generalizations)• from domain ontologies

• disambiguate polysemic terms with holonyms

Original query string: Bank

possible refinements: (1) Bank ∧ financial institution (2) Bank ∧ incline ∧ slope ∧ side (3) Bank ∧ container (4) Bank ∧ deposit ∧ repository increase precision



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Semantic SearchCross Referencing

• Provide search results that do not literally contain the query string but are closely related to the query by content• Apply domain ontologies for determining related concepts• Appy statistical analysis of large (text) document

corpora

dbpedia:Neil_Armstrong

dbpedia:Apollo_11

dbprop:mission

Neil Armstrong NER

dbprop:mission

dbprop:mission

query string

dbpedia:Buzz_Aldrin

dbpedia:Michael_Collins


http://www2.diego10.com.br/diego













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Semantic SearchExploratory Search

• Provide additional search results that do not necessarely contain the query string but are related to the query by content or also are related to the search results achieved by the direct query• Apply domain ontologies and heuristics to determine the

relevance of facts

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dbpedia:Apollo_11

category:Apollo_program

dbpedia:Apollo_13

dcterms:subject

yago:Space_accidents_and_incidents

rdf:type

rdf:type

dbpedia-owl:mission


dbpedia:Space_Shuttle_Challenger

dcterms:subject



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Semantic SearchReasoning

• Provide additional search results (and information) that do not necessarely contain the query string but are related to the query by content, whereby the relation may not be a direct one, but can be derived via entailment. • Apply domain ontologies, resoning algorithms and

heuristics to find new facts and determine the relevance of facts

95



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Semantic SearchReasoning

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dbpedia:Apollo_11

dbpedia-owl:mission

category:Missions_to_the_Moon

dcterms:subjectcategory:Exploration_of_the_Moon

dcterms:subject

category:Spaceflight

skos:broader

dbpedia:Moon category:Animals_in_Space

dcterms:subject skos:broader

Example: query string= Neil Armstrong

(Hard) questions to solve via reasoning:•Will there be the Moon or documents about the Moon in the search results?• How is Neil Armstrong related to the Moon? (is he?)•Was Neil Armstrong (really) on the Moon?• ...

category:Moon

skos:broader



596

Semantic Search


http://www.gocomics.com/calvinandhobbes/Donnerstag, 14. März 13

http://www.gocomics.com/calvinandhobbes/

http://www.gocomics.com/calvinandhobbes/


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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


http://dbpedia.org/page/Ernest_Hemingway






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dbpedia:Brave_New_World

dbpedia-owl:author

dbpedia:Aldous_Huxley

dbpedia-owl:author

dbpedia-owl:author

dbpedia-owl:author



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dbpedia:Brave_New_World

dbpedia-owl:author


dbpedia:ontology/influences

dbpedia:H._G._Wells


dbpedia:George_Orwell


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: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


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

71

Semantic Search

Inhalt:■ Introduction■ Multimedia Analysis ■ Semantic Analysis■ Semantic Search■ Explorative Search■ Realization



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Entity Based Search

• linguistic ambiguities of traditional keyword based search can be avoided

• enables high precision and high recall retrieval

http://www.yovisto.com/labs/autosuggestion/

• Query string refinement / extension• entity auto-suggestion• interpretation of natural language queries

J. Osterhoff, J. Waitelonis, H. Sack, Widen the Peepholes! Entity-Based Auto-Suggestion as a rich and yet immediate Starting Point for Exploratory Search, IVDW 2012


http://mediaglobe.yovisto.com/mggui-dev2/

http://mediaglobe.yovisto.com/mggui-dev2/


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http://mediaglobe.yovisto.com:8080/mggui-dev2/

search facets

C. Hentschel, H. Sack, et al., Open up cultural heritage in video archives with mediaglobe, I2CS 2012


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

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



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Exploratory Search with yovisto

J. Waitelonis, H. Sack: Towards exploratory video search using linked data, MTAP Volume 59, Number 2 (2012), 645-672

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


http://www.springerlink.com/content/m45r495n634m0q05/

http://www.springerlink.com/content/m45r495n634m0q05/



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Semantic Search


Albrecht Dürer: Melancholia I, 1514



Contact:Dr. Harald SackHasso-Plattner-Institut für SoftwaresystemtechnikUniversität PotsdamProf.-Dr.-Helmert-Str. 2-3D-14482 Potsdam

Homepage:http://www.hpi.uni-potsdam.de/meinel/team/sack.htmlBlog: http://yovisto.blogspot.com/E-Mail: [email protected] Twitter: lysander07 / biblionomicon / yovisto Slides can be found at http://slideshare.com/lysander07/

more about Semantic Web Technologies t http://www.openhpi.de/

Thank you very much

for your attention!


http://www.hpi.uni-potsdam.de/meinel/team/sack.html

http://www.hpi.uni-potsdam.de/meinel/team/sack.html

http://yovisto.blogspot.com

http://yovisto.blogspot.com

mailto:[email protected]

mailto:[email protected]

http://slideshare.com/lysander07/

http://slideshare.com/lysander07/

http://www.openhpi.de







Education

Semantic Search