The most important feature of a search engine is that users can find what they are looking for and; additionally, that they have a rich visual experience accessing all data they need. The MOUVIZ dataset is mainly focused on names of artists, albums and tracks, so we cannot provide enough information to the user unless we provide a mechanism of population with that information. Users need a richer experience, for example users are more comfortable if the interface has information in their native language and if they have access to photos and navigate through interesting information. But it is hard to populate the MOUVIZ dataset with more information and to be updated with all information about artists. Therefore, there is a need of an automatic mechanism to retrieve this information. In this project, I researched on an Interlinking Engine to provide augmented content to users by making an automatic relation between entities from our dataset and other Linked Open Data datasets, so we can use the information from other datasets to enrich our content. Also I research on searches with explicit and implicit scoring from users, where an user can give us his relevance scoring for a resource but we can capture some actions from user and identify an implicit scoring from this actions.

1. mouviz
MOUVIZ - WP2 Status
Innsbruck, playence KG
14.06.12
Víctor Méndez
José Manuel López Cobo

2. mouviz Agenda
• 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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3. mouviz Introduction: 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.

4. mouviz What 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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5. mouviz Interlinking: Natural Language
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6. mouviz Interlinking: basics
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7. mouviz Interlinking: 1st Semantic Approach
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• Detection with first level relations
 DBPedia: Generic domain ontology (DBPedia – L1)
 MusicBrainz: Music domain ontology (MusicBrainz – L1)

8. mouviz Interlinking: 2º Semantic Approach
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• Detection with 2 levels:
 MusicBrainz
 Music domain ontology
 Translated to a common model
 Reduction of Noise

9. mouviz MOUVIZ Search Engine: Architecture
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10. mouviz MOUVIZ Search Engine: Interlinking Engine
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11. mouviz MOUVIZ Search Engine: Content Augmentation Collector
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12. mouviz DEMO
• 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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13. mouviz Results: 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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14. mouviz Results: 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%
-
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

15. mouviz Conclusions (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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16. mouviz Conclusions (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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17. mouviz
Thank you.
Questions?