Information Systems & Semantic Web University of Koblenz Landau, Germany Semantic Multimedia Web Ansgar Scherp Basierend auf Folien von Carsten Saathoff,

Information Systems & Semantic Web

University of Koblenz ▪ Landau, Germany

Semantic Multimedia Web

Ansgar Scherp

Basierend auf Folien von Carsten Saathoff, Raphael Troncy und Lynda Hardman

ISWeb - Information Systems & Semantic Web

Carsten [email protected]

MMDB 02.06.08Slide 2

Was bisher geschah...

MMDB als Erweiterung von ORDBMS Information Retrieval als Basis für Queries Feature Extraktion um Inhalt zu beschreiben Feature Transformation um kompaktere Darstellung zu

bekommen Fokus auf low-level features Distanzen und Ähnlichkeiten Indizierung von Features für schnellen Zugriff




Probleme traditioneller MMDB

Datenstrukturen und Schemata meist proprietär MMDB typischerweise für eine Applikation aufgesetzt. Spätere Integration mit anderen Applikationen schwer Ad-Hoc Integration eher unmöglich

Starker Fokus auf Low-Level Features Semantische Lücke

• Kein direktes Mapping zwischen Low-Level Features und Semantik des Bildes

Retrieval primär über Ähnlichkeit• Fast alle Studien zeigen, dass Nutzer dadurch nicht

zufriedengestellt werden• Nutzer wollen semantisch Anfragen




Metadaten




Metadaten (2)

Stichworte

GPS InformationKamera DatenDatum




Metadaten

Daten über Daten Autor, Creation-Date, Keywords, ...

Wie repräsentieren? Relationales Schema XML Semantic Web Technologien

Metadaten sollten interoperabel sein Web Desktop Intranets Viele Applikation müssen kommunizieren




Überblick

Semantic Web + Multimedia Semantische Lücke Canonical Process for Multimedia Production MPEG7 und COMM

Probleme mit MPEG7 Core Ontology on Multimedia (COMM)

Linked Open Data KAT – K-Space Annotation Tool

Semi-Automatische Effiziente Annotation Szenarien




Semantic Web auf einer Folie

EmployeeEmployee

PostDocPostDoc ProfessorProfessor

PersonPerson

rdfs:subClass rdfs:subClass

rdfs:subClass

cooperatesWithcooperatesWith

rdfs:Range rdfs:DomainOntology

<swrc:Professor rdf:ID="person_sst"> <swrc:name>Steffen Staab </swrc:name>...</swrc:Professor>

http://www.uni-koblenz.de/~staab

rdf:typerdf:type

Meta-data

<swrc:PostDoc rdf:ID="person_sha"> <swrc:name>Siegfried Handschuh</swrc:name>

...</swrc:PostDoc>

Web page

http://www.deri.ie/~shaURL

<swrc:cooperatesWith rdf:resource = "http://www.uni-koblenz.de/~staab/#person_sst"/>

swrc:cooperatesWith




Semantic Web for Multimedia

IsWeb @ Bad Kreuznach 2007

WIAMIS 2008 in Klagenfurt

Multimedia Ontology

http://kodemaniak.de/foaf.rdf

depicts

depicts

„Carsten Saathoff“

Domain Ontology

hasName

rdf:type

Researcher

ResearchMeeting:=>=1 depicts.Researcher

ResearchMeeting

Zeig mir alle Bilder von Research Meetings!

http://isweb.uni-koblenz.de/

http://wiamis2008.org




Überblick








Semantische Lücke

0010EE -> bläulich

0033FE -> bläulich

Visuell ähnlich! Aber semantischunterschiedlich!




Semantische Lücke

Visuell ähnlich, semantisch ähnlich, aber...

Italien USA




Ebenen der Semantik

Generische Objekte

Generische Szene

Spezifische Objekte

Spezifische Szene

Abstrakte Objekte

Abstrakte Szene

Eine Person

Personen unterhaltensich

Churchill

Churchill, Roosevelt, Stalinsitzen zusammen

Churchill, Premierminister, GB, ...

Big Three, Yalta KonferenzWWII, ...

Wis

sen




Überblick








Overview of Canonical Processes




Example 2: Vox Populi Video Sequences Generation

Stefano Bocconi, Frank Nack

Interview with America video footage with interviews and background material about the opinion of American people after 9-11 http://www.interviewwithamerica.com

Example question:What do you think of the war in Afghanistan?

“I am never a fan of military action, in the big picture I don’t think it is ever a good thing, but I think there are circumstances in which I certainly can’t think of a more effective way to counter this sort of thing…”

http://www.interviewwithamerica.com/




Vox Populi Premeditate Process

Analogous to the pre-production process in the film industry Static versus dynamic video artifact

Output Script, planning of the videos to be captured Questions to the interviewee prepared Profiles of the people interviewed:

education, age, gender, race Locations where the interviews take place

Premeditate




Vox Populi Annotations

Contextual Interviewee (social), locations

Descriptive Question asked and transcription of the answers Filmic continuity, examples:

• gaze direction of speaker (left, centre, right)• framing (close-up, medium shot, long shot)

Rhetorical Rhetorical Statement Argumentation model: Toulmin model




Vox Populi Statement Annotations

Statement formally annotated: <subject> <modifier> <predicate> E.g. “war best solution”

A thesaurus containing: Terms on the topics discussed (155) Relations between terms: similar (72), opposite (108),

generalization (10), specialization (10) E.g. war opposite diplomacy




Toulmin Model

ClaimData

Qualifier

Warrant

Backing

Condition

Concession

57 Claims, 16 Data, 4 Concessions, 3 Warrants, 1 Condition




Vox Populi Query Interface

Query

Construct Message




Vox Populi Organize Process

Using the thesaurus, create a graph of related statements nodes are the statements

(corresponding to video segments)“war best solution”,“diplomacy best solution”,“war not solution”

edges are either support or contradict

support

contradict

war best solution

war not solution

diplomacy best solution

Organize




Result of Vox Populi Query

I am not a fan of military actions

War has neversolvedanything

I cannot think of a more effective solution

Two billionsdollar bombson tentsDistribute

Publish




Vox Populi Processes




Canonical Processes 101

Canonical: reduced to the simplest and most significant form possible without loss of generality

Formalization of each process in UML diagrams Process Process artifacts Process actors External world artifacts




Create Media Asset

Process where media assets are captured, generated or transformed

C apturing Devic e<<process actor>>

C apturing<<process>>

1..*1..*

<<involves>>

G eneration Program<<process actor>>

G eneration<<process>>

1..*1..*

<<involves>>

Editing Program<<process actor>>

Transforming<<process>>

1..*1..*

<<involves>>

Premeditate A rtifac ts(from Premedita te )

<<process artifact>>

C reation A c tor<<process actor>>

Message(from Construct M essage)

<<process artifact>>

Media A sset<<media asset>>

C reate Media A sset<<process>>

<<input>>

1..*1..*

<<involves>>

<<input>>

1..*1..*<<output>>




Semantic Annotate

V oc abulary Term<<term>>

Semantic A nnotate<<process>>

V oc abulary<<schema>> <<input>>

Semantic A rtifac t A nnotation<<annotation>>

desc ribed in term s of

<<output>>

link

A ny Proc ess A rtifac t<<process artifact>>

A nnotate<<process>>

**

<<input>>

A rtifac t A nnotation<<annotation>>

1..*1..*

<<output>>

The annotation uses some controlled vocabularies Subject matter annotations of your photos Rhetorical annotations in Vox Populi




Package

Process where process artifacts are logically and physically packed

Physic al Package<<process>>

Logic al Pac kage<<process>>

Input given by a user<<external w orld artifact>>

Package<<process>>

**<<input>>

A ny Process A rtifac t<<process artifact>>

**<<input>>

Multimedia Package<<composite a rtifact>>

1..*1..*

<<output>>

**

contains




Canonical Processes Possible Flow




Sum Up

Community agreement, not “yet another model”Large proportion of the functionality provided by multimedia

applications can be described in terms of this modelInitial step towards the definition of open web-based data

structures for describing and sharing semantically annotated media assets




Überblick








MPEG7

ISO Standard der MPEG Community Einheitliches Format zu Speicherung von Multimedia

Metadaten Struktur Features Semantik

Extrem (!) umfangreich Darauf basierend MPEG21 mit Fokus auf DRM etc. Hat im Gegensatz zu MPEG1-4 nichts mit Kodierung zu tun




MPEG7 (2)

Basiert auf XML MPEG7 Beschreibung ist eine Hierarchie

Deskriptoren beschreiben Eigenschaften von Multimedia Daten Struktur

• Video -> Shots -> Frames • Bilder -> Segmente

Semantik Low-Level Features

Um Flexibilität zu gewahren, können Deskriptoren sehr vielseitig kombiniert werden.




Big Three




Issues<Mpeg7> <Description xsi:type="ContentEntityType"> <MultimediaContent xsi:type=„ImageType"> <Image> <SpatialDecomposition>

<StillRegion id=„SR1“> <TextAnnotation> <KeywordAnnotation xml:lang="en"> <Keyword>Churchill</Keyword> </KeywordAnnotation> </TextAnnotation></StillRegion>

<StillRegion id=„SR2“> <Semantic> <Label> <Name>Roosevelt</Name> <Label> </Semantic></StillRegion>

<StillRegion id=„SR3“> <Semantic> <Definition>  <StructuredAnnotation> <WhatObject> <Name xml:lang="en">Stalin</Name> </WhatObject> </StructuredAnnotation> </Definition> </Semantic> </StillRegion>...

How do you formulate a query to get images

showing Churchill et al.?

First Shot (Xpath)://StillRegion[.//Keyword=“Churchill” or

.//Keyword=”Roosevelt” or

.//Keyword=”Stalin”]

Winston ChurchillRecognizer

Franklin RooseveltRecognizer

Josef StalinRecognizer




Probleme mit MPEG-7?

Annotationen sind nicht interoperabel! Mehrdeutigkeiten Mehrere Möglichkeiten um semantisch identische Annotationen

zu beschreiben Deskriptoren können auf viele Arten kombiniert werden

Komplexe Anfragen müssen alle Alternativen beachten




Capabilities and Maturity Levels

Integration Automation

Former Situation Current Situation Future / Desired Situation

no standard, no vocabulary manual 1:1 agreement on

format and semantics tight coupling of data and

applications

standard vocabulary manual 1:1 agreement on

mpeg-7 vocabulary tight coupling of data and

applications

standard vocabulary pre-defined meaning ad-hoc coupling of data

and applications CORE ONTOLOGY

Nächster Teil der VL

Formerly

MPEG-7

COMM




Ontology Stack

Core Ontologies

Domain Ontologies

Foundational Ontologies

Foundational Ontologies Span across multiple fields, each covering multiple domains Modelling of the most abstract concepts like event, object, ...

Core Ontologies Situated in one field, but spans multiple domains Can base on foundational ontologies Examples fields: events, annotation, communication, ...

Domain Ontology For a specific domain, e.g., fishery, human body, etc.




MPEG-7

COMM

Requirements on a high quality

MM OntologyChallenge

BuildingBlock

Legend




TextDescriptor

MusicManager

CompoundDocument

Requirements for COMM

ReusabilityDesign a core ontology for any multimediarelated application

MPEG-7-ComplianceSupport most important description tools

ExtensibilityEnable inclusion of further

• description tools(even those that are not part of MPEG-7!)

• media types Separation of Concerns

Clear separation of domain knowledge andknowledge about structure

ModularityEnable customization of multimedia ontology

High degree of axiomatization Ensure interoperability throughmachine accessible semantics Churchill

RecognizerJosef StalinRecognizer

FaceDetector

PhotoManager

AuthoringTool

SemanticAnnotation

decomposition visual descriptors

audio descriptors ...

<Mpeg7> ...</Mpeg7>

<Mpeg7> ...</Mpeg7>




Is MPEG-7 a good Basis for a high Quality Ontology?

Shortcomings of badly modelled ontologies [Oberle et al., 2006]:1) Conceptual ambiguity

• Difficulties in understanding themeaning of concepts and theirrelations

2) Poor axiomatization• Axiomatization of well defined

concepts is missing3) Loose Design

• Presence of modelling artefacts(concepts without ontological meaning)

Shortcomings mainly hinder Extensibility Interoperability

Especially 1) and 2) are major shortcomings of MPEG-7 1-to-1 translations from MPEG-7 to OWL/RDFS (e.g.

[Hunter, 2003a]) will not result in high quality ontologies!

<StillRegion id=„SR1“> <TextAnnotation> <KeywordAnnotation xml:lang="en"> <Keyword>Churchill</Keyword> </KeywordAnnotation> </TextAnnotation></StillRegion>

<StillRegion id=„SR2“> <Semantic> <Label> <Name>Roosevelt</Name> <Label> </Semantic></StillRegion>

<StillRegion id=„SR3“> <Semantic> <Definition>  <StructuredAnnotation> <WhatObject> <Name xml:lang="en">Stalin </Name> </WhatObject> </StructuredAnnotation> </Definition> </Semantic> </StillRegion>




MPEG-7

COMM

Requirements on a high quality

MM Ontology

Quality of Ontologies

Challenge

BuildingBlock

Legend




How to Design a High Quality Multimedia Ontology?

Approach from [Oberle, 2005], [Oberle et al., 2006]:Use a well designed foundational ontology as a modelling basis to avoid shortcomings

Foundational ontologies provide Formal precision Domain independence Broad scope

Building upon foundational ontologies prevents easy inclusion of modeling artefacts reduces conceptual ambiguity inherit rich axiomatization




Methodology

MPEG-7

COMM

Requirements: High Quality MM Ontology


Quality Measures for Ontologies

Reference Ontologie

MPEG-7Compliance

Challenge

BuildingBlock

Legend




Methodology for Design Pattern Definition

Identification of most important MPEG-7 functionalities[Arndt et al., 2007]: Decomposition of multimedia content into segments Annotation of segments with meta data (e.g. visual descriptor,

media information, creation & production, …) General:

• Identify repetitive structures and describe them at an abstract level

• Describe digital data by digital data at an arbitrary level of granularity

Additional patterns are needed for: Complex data types of MPEG-7 Semantic annotation by using domain ontologies

Interface between reusable multimedia core and domain specific knowledge




DOLCE Design Patterns: OIO

Foundational ontology DOLCE+DnS Ultralight Aims at capturing the most essential aspects in the world Defines disjunctive upper classes

Event, Object, Quality and Abstract Follows a pattern-oriented approach for ontology design

2 design patterns (extensions) that are especially important for MPEG-7: Ontology of Information objects (OIO): Formalization of information

exchange Information object represents pure abstract information (message) Relevance for multimedia ontology:

• MPEG-7 describes digital data (multimedia information objects) with digital data (annotation)

• Digital data entities are information objects




DOLCE Design Patterns: D&S

Descriptions & Situations (D&S): Formalization of Context

Relevance for multimedia ontology:

Meaning of digital data depends on context

Digital data entities are connected through computational situations (e.g. input and output data of an algorithm)

Algorithms are descriptions

Annotations and decompositions are situations that satisfy the rules of an algorithm / method




Methodology

MPEG-7

COMM

Requirements: High Quality MM Ontology


Quality Measures for Ontologies

Reference Ontologie

Identification of repetitive structures

MPEG-7Compliance

Pattern definition through

Specialization

Challenge

BuildingBlock

Legend

Repr. of Context

Repr. of Information




Ontology of Information Objects (OIO)

social-object information-encoding-system

information-object description

situationagent particular information-realization

ordered-by

realized-by

interpretedBy

about

setting

satisfiesexpressedBy

Ontology of Information Objects (OIO)




Example

Information Object „Graz Tourist Guide“Information Realization

http://cms.graztourismus.at/cms/ziel/42425/EN/

BookletInformation Encoding:

English German

About: Places, Buildings (e.g. Clock Tower)Agent: 1. iMedia Visitor / 2. Tourist Officer / 3. Graphics DesignerExpresses:

1. Walking Path through Graz2. Small-Size Tourist Guide3. Arrangement of Illustrations

http://cms.graztourismus.at/cms/ziel/42425/EN/




Descriptions & Situations (D&S)

social-objectconcept

parameter

role

course

description

situation

information-objectregion

endurant

perdurant

sequences

valued-bydefines

played-by setting

satisfies

expressed-by


requires

methodsocial-objectconcept

parameter

role

course

description

situation

information-objectregion

endurant

perdurant

sequences

valued-bydefines

played-by setting

satisfies

expressed-by


requires

method

Distinction between: DOLCE ground entities (regions, endurants, perdurants) Descriptive entities (parameters, roles, courses)

Descriptions Formalize context Define descriptive concepts

Situations Are explained by descriptions Are settings for ground entities




Putting it Together: Decomposition Pattern

digital-data

multimedia-data

output-segment-roleplays

processing-role

input-roleoutput-role

segment-decompositionalgorithm

segmentation-algorithm

setting

satisfies

situationmethod

input-segment-role

D&S / OIOdefines

mask-rolerequires

description

structured-data-description

localization-descriptor

plays

information-object role




Example

Image1 playsRole SegmentationInput

Segment2 playsRole SegmOutp



Segment3 playsRole SegmOutpSegment1 playsRole SegmInput

Via its role in a computational task the different parts may be arbitrarily nested and related to different computing algorithms

Querying for all subparts takes place along a well-defined pattern




Modular Architecture

Multimedia ontology consists ofCore module that contains the

design patternsModules that specialize the core

module for different media types

Modules that contain media independent MPEG-7 description tools such as media information or creation & production

Data type module that formalizes MPEG-7 data types e.g. matrices, vectors, unsigned-int-5, float-vector, probability-vector, …

DOLCE

Descriptions & Situation

Ontology of Information

Objects

Core

Visual Audio

Datatype

Media

Text / LingInfo

Domain Ontolog

Connected by SemanticAnnotation Pattern

Localization

Multimedia Knowledge (COMM)

Fundamental Knowledge about the World

Knowledge about a specific Domain




Does the Multimedia Ontology fulfil the Requirements?

Reusability MPEG-7-Compliance

Design patterns enable therepresentation of description tools

Extensibility Design patterns are media

independent possibility to include• further media types• arbitrary descriptors

Extensions of multimedia ontologywill not affect legacy annotations dueto DOLCE+D&S+OIO

Separation of Concerns Clear separation between domain

specific and multimedia relatedknowledge

Modularity Modular architecture allows customization

High degree of axiomatization Design patterns come with generic

axiomatization that is refined in derivedontology modules

ChurchillRecognizer


PhotoManager

COMM

One such extension has already beendone for Text Annotation.

Another one for compund documentannotation is currently developed!

Content & Media Annotation Pattern

Semantic Annotation Pattern

See slide before this slide!

OWL-DL version available for download.




Benefits of a DOLCE-aligned Multimedia Ontology

Usage of DOLCE enforces clean design Constraints prohibit arbitrary placement of MPEG-7 concepts into

DOLCE• Similar concepts will be placed on similar locations of the

taxonomy• Things that are different, have to be separated

(e.g. data and the perceivable content that is carried)

Extensibility due to underlying general taxonomy of DOLCEPossibility to describe multimedia domain at an

arbitrary level of detail (e.g. segments have pixels as atomic parts)

Rigorous application of the D&S and OIO patterns allows description of digital data in different contexts(e.g. data acting as input or output for an algorithm)




Benefits compared to MPEG-7

Linkage with domain ontologies allows meaningful semantic annotation of multimedia content Semantic part can be entirely replaced with a domain ontology Clear separation between domain ontologies and multimedia core

ontology through semantic annotation pattern

Easier queries Annotation pattern guarantees equal representation of all

annotations Complex data type pattern guarantees uniform access to nested data

No complex XML-structures to parse Multimedia ontology only uses restricted inventory of DOLCE

predicates

Higher interoperability through machine accessible semantics and underlying DOLCE axiomatization




http://en.wikipedia.org/wiki/Yalta_Conference

World War II

Yalta ...

...History Ontology

“Creating a Multimedia Presentation” Revisited

SR1 SR2 SR3

Winston ChurchillRecognizer

Franklin D. RooseveltRecognizer


ChurchillRooseveltStalin

Sparql: select ?image where { ?image plays AnnotatedDataRole. ?x plays SemanticLabelRole. ?x rdf:type pol:President }

PhotoManager

AuthoringTool




Überblick








A Giant Graph Open to the World

Annotate the content (interpretation)Elephant, Ganesh, Thailande, Holidays, Chiang Mai

Link to knowledge on the Web:img foaf:depicts dbpedia:Ganeshdbpedia:Ganesh rdfs:label "Vinayaka"dbpedia:Ganesh skos:altlabel "Ganapati" dbpedia:Ganesh rdf:type wn:synset-Deities-noun-1dbpedia:Ganesh owl:sameas wn:synset-Ganesh-noun-1

<rdf:Description rdf:about="Ganesh.jpg"> <dc:title>An image of the Elephant Ganesh</dc:title> <dc:creator>Raphaël Troncy</dc:creator> </rdf:Description>




Linking Open Data Project

Expose open datasets in RDF

Set RDF links among the data items for different datasets

Over 2 billion triples, 3 millions links (March 2008)

http://richard.cyganiak.de/2007/10/lod/

http://richard.cyganiak.de/2007/10/lod/




Linked Open Data March 2009




Warum ist LOD wichtig?

• RDFa wird von der Google Suchmaschine verarbeitet• Content provider bieten nun RDFa an• Erhöht Click-trough-rate auf Webseiten (Werbeanzeigen)• Erhöht Ranking der Webseiten in Google

• SIOC Ontology zur Verlinkung von Online Communities wird genutzt von Yahoo! • Wird durch SearchMonkey eingesammelt• Tools um RDFa zu publishen

• Usw.




DBpedia

DBpedia is a community effort to: extract structured "infobox" information from Wikipedia interlink DBpedia with other datasets on the Web




DBpedia




Automatic Links Among Open Datasets

Processors can switch automatically from one to the other …




Take Home Message

Reuse what is there Of course, one could create RDF data manually …

… but that is unrealistic on a large scale Goal is to generate RDF data automatically when

possible and "fill in" by hand only when necessary• service to get RDF from flickr images

http://www.kanzaki.com/works/2005/imgdsc/flickr2rdf• service to get RDF from XMP

http://www.ivan-herman.net/cgi-bin/blosxom.cgi/WorkRelated/SemanticWeb/xmpextract.html

Expose what you make

http://www.kanzaki.com/works/2005/imgdsc/flickr2rdf






Überblick








KAT: K-Space Annotation Tool

Goal Efficient annotation of multimedia content Means to create semantically rich annotations

KAT provides framework for Executing analysis plugins Providing visualisation plugins

• Displaying/annotating content• Browsing

Interfaces with Core Ontology for Multimedia (COMM)• Provides the common model

Role based messaging to leverage reuse of components




Efficient Annotation

Reduce time required by user for annotating content

Integration of Automatic analysis methods

• Region labeling, object detection• Key Frame Extraction, Shot Boundary Detection

Automatic Organisation• Clustering

Inferencing• Based on formal domain ontologies

Semi-automation




Semantically Rich Annotations

Relational Annotation Express how depicted entities are related Example: Soccer Game

• Who is tackling whom?• Why was the penalty given?

Ontologies provide means to express relations KAT aims at providing the means to efficiently create them

Event-Based annotation Events are prominent in multimedia Create and manage events Relate events and media Allow for event-based retrieval and exploration




Architecture

KAT-Core

Plugin Plugin

GUI

Plugin Viewregister

COMM

Repository Repository...

...

store

display




Szenarien

Effiziente Annotation von persönlichen Bildern Semi-Automatische, Semantische Annotation von Sport-

Ereignissen Browsing von Bildsammlungen im Web




Persönliche Bildsammlungen

Heutzutage typischerweise in Ordnern auf Festplatte Wenig Annotationen weil zu aufwendig Annotationen enthalten

Viel Hintergrund Wissen Gefühle und persönliche Momente

Frage: Wie kann ein Nutzer hier unterstützt werden? Automatische Annotation liefert nur einfache Semantik Daher: Clustering um Ereignisse zu finden User kann dann ganze Ereignisse annotieren Verwendung von NLP (Textanalyse) um semantische

Annotation zu erzeugen




Sportereignisse

Kombination verschiedener Algorithmen Highlight Detection: Goals, Corner-Shot, ...

• Features: Motion, Geräusche, sichtbare Konzepte Analyse von Minute-by-Minute Reports

• Liefert andere Ereignisse Ergebnisse oft nur global

Manuelles Refinement Zuweisen von Namen zu Spielern im Video/Bild Zuweisen von Rollen:

• Wer hat das Faul begangen, wer war Opfer Verknüpfen von Ereignissen

Ziel: möglichst vollständige Annotationen effizient erstellen




Browsen von Bildkollektionen

Flickr als COMM Maping von Tags in Wordnet

Wordnet: linguistische Ontologie Mapping von Geoinformationen nach Geonames

Ontologie von geographischen Informationen (Länder, Orte, ...)

Mapping nach dbpedia Wikipedia als maschienenlesbare Version

Tags bekommen Kontext Anzeige werwandter Bilder, komplexe Queries Ergänzen der Annotationen




91

KAT as basis for der User InterfaceObjectives

Explore and visualize semantic Web 2.0 data in real-time Acquainting oneself about an area of interest

Semantic data 1 billion triples from DBpedia, GeoNames, WordNet,

FOAF files and Flickr Very large, mixed-quality, semantically heterogeneous

Winner of Billion Triples Track, Semantic Web Conference 2008, Karlsruhe [ISWC2008]




92

SemaPlorer – Web 2.0 Content Brower

Search for

locations, persons and tags

Active facets like tags, location

Map showing locations, sights, pictures

Geo-referenced image from Flickr

Information on locations, persons, tags

SemaPlorer Live!http://btc.isweb.uni-koblenz.de/




Werbeblock

• HiWi-Jobs für SemaPlorer++• Interesse an Arbeit in einer Gruppe• Tätigkeit die Kenntnisse aus dem Studium (und darüber

hinaus) praktischen anwenden lässt• Spaß am “Tütfteln” • Entwicklung mit Java

• Mail mit Beschreibung an Erfahrungen / Lebenslauf an [email protected]




Werbeblock

• ImageAtlas II Projektpraktikum• Entwicklung einer Plattform zum Taggen und zur Diskussion

von Bildern der Kunstgeschichte und Bildwissenschaft• Zusammen mit dem Institut für Kunstwissenschaft

• Anmeldung: Jetzt über KLIPS• http://isweb.uni-koblenz.de/ -> Lehre -> WS09/10• Fragen? Mail an [email protected]




Werbeblock

• Diplomarbeiten• Beispiele für Themen

• Kontextsensitive Visualisierung von Events und Objekten auf der Karte

• Repräsentation von dynamischen organisationalen Prozessen am Beispiel des Notfallmanagements

• TripleRanked Faceted Browsing Interface of Linked Open Data

• Auch online unterhttp://isweb.uni-koblenz.de/interactiveweb

• Und weitere Themen …

Documents

Information Systems & Semantic Web University of Koblenz Landau, Germany Semantic Multimedia Web Ansgar Scherp Basierend auf Folien von Carsten Saathoff,