mouviz

MOUVIZ - WP2 Status

Innsbruck, playence KG

14.06.12

Víctor Méndez

José Manuel López Cobo

mouvizAgenda

• Introduction: User needs

• What can we provide?

• Interlinking

– Natural language approach

– Basics

– 1st Semantic approach

– 2nd Semantic approach

• MOUVIZ Search Engine

– Architecture

– Interlinking engine

– Content Augmentation Collector

• Demo

• Results

– Corpus & setups

– Measures

• Conclusions

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mouvizIntroduction: User needs

• Users want to find what they are looking for.

• MOUVIZ dataset is limited to few information. – It is needed a way to offer

richer information experience to user. • Language • Data (Specially visual data)

• It is hard to populate data and be updated. – Needs an atomatic mechanism.

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Positive user experience is a key factor in applications.

mouvizWhat can we provide?

• Searchs over MOUVIZ dataset.

• External resources about music:

– Linked Data

• Data can be extracted showed:

– Interlinking

– Augmented Content

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mouvizInterlinking: Natural Language

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mouvizInterlinking: basics

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mouvizInterlinking: 1st Semantic Approach

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• Detection with first level relations

DBPedia: Generic domain ontology (DBPedia – L1)

MusicBrainz: Music domain ontology (MusicBrainz – L1)

mouvizInterlinking: 2º Semantic Approach

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• Detection with 2 levels:

MusicBrainz

Music domain ontology

Translated to a common model

Reduction of Noise

mouvizMOUVIZ Search Engine: Architecture

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mouvizMOUVIZ Search Engine: Interlinking Engine

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mouvizMOUVIZ Search Engine: Content Augmentation Collector

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mouvizDEMO

• Retrieve count of tracks per genre

• Retrieve count of tracks per artist in a genre

• Retrieve track list by genre, artist and album

• Retrieve information from one artist

• Retrieve interlinking information from one artist

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mouvizResults: Corpus & Setups

• MOUVIZ Ontology:

– Avg. relationships: 10.68

– Max: 36

– Min: 2

• Manual annotation of the corpus against DBPedia and MusicBrainz.

– DBPedia: only well known artists.

– MusicBrainz: updated.

• Against 65K entities from these datasets.

• 3 Setups:

– DBPedia 1st relation level

– MusicBrainz 1st relation level

– MusicBrainz 2nd relation level

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mouvizResults: Measures

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Setups/Measures Precision Recall F-Measure Threshold

DBPedia - L1 0,38 0,71 0,50 8%

MusicBrainz - L1 0,75 0,92 0,83 8%

MusicBrainz - L2 0,81 0,99 0,89 1,50%

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0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

Precision Recall F-Measure

DBPedia - L1

MusicBrainz - L1

MusicBrainz - L2

mouvizConclusions (1/2)

• User can have a richer experience with Content Augmentation.

• Specific domain ontology: best results

– Generic Domain ontology: • Only well known artists and not updated.

• Without potential relationships (against MOUVIZ).

• Few number of relationships.

• More noise.

• Less relations and in more than one way (more complex structure to translate to a common graph model).

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mouvizConclusions (2/2)

• Best results: translation to a common model and depth to 2nd level.

• Difficulties found:

– Set a threshold.

– Normalize scoring.

• Future steps:

– Normalization/ Threshold

– Boosting of relationships/entities

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mouviz

Thank you.

Questions?