mouviz meeting presentation
TRANSCRIPT
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
14.06.12 MOUVIZ Project 2
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.
14.06.12 MOUVIZ Project 3
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
14.06.12 MOUVIZ Project 4
mouvizInterlinking: 1st Semantic Approach
14.06.12 MOUVIZ Project 7
• Detection with first level relations
DBPedia: Generic domain ontology (DBPedia – L1)
MusicBrainz: Music domain ontology (MusicBrainz – L1)
mouvizInterlinking: 2º Semantic Approach
14.06.12 MOUVIZ Project 8
• Detection with 2 levels:
MusicBrainz
Music domain ontology
Translated to a common model
Reduction of Noise
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
14.06.12 MOUVIZ Project 12
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
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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).
14.06.12 MOUVIZ Project 15
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
14.06.12 MOUVIZ Project 16