=Paper=
{{Paper
|id=Vol-1690/paper47
|storemode=property
|title=MusicWeb: Music Discovery with Open Linked Semantic Metadata
|pdfUrl=https://ceur-ws.org/Vol-1690/paper47.pdf
|volume=Vol-1690
|authors=Alo Allik,Mariano Mora-Mcginity,Gyorgy Fazekas,Mark Sandler
|dblpUrl=https://dblp.org/rec/conf/semweb/AllikMFS16
}}
==MusicWeb: Music Discovery with Open Linked Semantic Metadata==
https://ceur-ws.org/Vol-1690/paper47.pdf
MusicWeb: music discovery with open linked
semantic metadata
Alo Allik, Mariano Mora-Mcginity, György Fazekas, and Mark Sandler
Queen Mary University of London,
{a.allik, m.mora-mcginity, g.fazekas, mark.sandler}@qmul.ac.uk
Abstract. This demo presents MusicWeb, a novel platform for linking
music artists within a web-based application for discovering associations
between them. MusicWeb provides a browsing experience using connec-
tions that are either extra-musical or tangential to music, such as the
artists’ political affiliation or social influence, or intra-musical, such as
the artists’ main instrument or most favoured musical key. The platform
integrates open linked semantic metadata from various Semantic Web,
music recommendation and social media data sources. The connections
are further supplemented by thematic analysis of journal articles, blog
posts and content-based similarity measures focussing on high level mu-
sical categories.
Keywords: Semantic Web, Linked Open Data, music metadata, seman-
tic audio analysis, music information retrieval
1 Introduction
MusicWeb is a music discovery platform which offers users the possibility of ex-
ploring editorial, cultural and musical links between artists. It gathers, extracts
and manages metadata from many different sources. The connections between
artists are based on YAGO categories such as style, geographical location, in-
strumentation, record label, but also more obscure links, for instance, artists
who have received the same award, have shared the same fate, or belonged
to the same organisation or religion. The connections are further enhanced by
thematic analysis of journal articles and blog posts, content-based music infor-
mation retrieval similarity metrics and proximity measures in a 2-dimensional
mood space. Information about artists is collated and processed from several dif-
ferent web knowledge content resources and presented for the user to navigate
in a faceted manner [5]. MusicWeb as a discovery platform is subtly different in
function from recommender systems like dbrec [7], which suggests music obtained
from DBpedia by computing a measure of semantic distance as the number of
indirect and distinct links between resources in a graph. This demo intends to
show the methods of linking artists employed in the system and how these could
help overcome issues such as infrequent access of lesser known artists in large
music catalogues (the “long tail” problem) or the difficulty of recommending
artists without user ratings in systems that employ collaborative filtering (“cold
start” problem) [3].
Fig. 1. Example of a MusicWeb artist page.
2 System overview
The core functionality of the platform relies on available SPARQL endpoints as
well as various commercial and community-run APIs. More recently, novel ser-
vices complement the platform to provide alternative ways to forge connections
using natural language processing and machine learning methods. The front por-
tal includes suggested links to selected artists and a search functionality from
where users can navigate to individual artists pages. Each artist page contains a
biography, a playlist of online audio and a selection of Youtube videos, as shown
in Fig. 1. The MusicWeb API uses a number of LOD resources and Semantic
Web ontologies to process and aggregate information about artists:
MusicBrainz1 provides reliable and unambiguous identifiers for entities in mu-
sic publishing metadata, including artists.
DBPedia2 is used for building profiles and querying socio-cultural links between
artists.
SameAs.org3 manages URI co-references which is useful for mapping Mu-
sicBrainz identifiers to DBpedia entities.
YouTube API is used to query associated video content for the artist panel.
Last.fm4 provides recommendations based on crowd-sourced user listening habits.
YAGO [4] semantic knowledge base enables collation of Wikipedia categories
1
http://musicbrainz.org
2
http://dbpedia.org
3
http://sameas.org
4
http://last.fm
for linking artists.
the Music Ontology [8] provides main concepts and properties for describing
musical entities, including artists, on the Semantic Web.
AcousticBrainz5 service, which gathers crowd-sourced acoustic information
about music, facilitates content-based similarity calculation.
3 Artist similarity
There are many ways in which artists can be considered related. MusicWeb
uses Semantic Web technologies and linked data to facilitate faceted searching
and displaying of information [6]. This is done by modeling artist similarities in
four different domains: socio-cultural, research and journalistic literature, crowd-
sourced tag statistics and content-based information retrieval.
Socio-cultural connections between artists in MusicWeb are primarily derived
from YAGO categories that are incorporated into entities in DBpedia. Many cat-
egories, in particular those that can be considered extra-musical or tangential to
music, stem from the particular methodology used to derive YAGO categories
from Wikipedia [4].
Literature-based linking is achieved by data-mining research articles and on-
line publications using natural language processing. MusicWeb uses Mendeley6
and Elsevier7 databases for accessing research articles that are curated and cat-
egorised by keywords, authors and disciplines. Online newspapers, music maga-
zines and blogs, on the other hand, constitute non-curated data. Relevant infor-
mation in this case must be extracted from the body of the text by Web-crawling
based on keywords or tags. The Alchemy API8 is then used for named entity
recognition and keyword extraction.
Crowd-sourced tags enable modelling similarity based on projected mood.
This method involves using the Semantic Web version of ILM10K music mood
dataset that consists of over 4000 unique artists [1]. The dataset is based on
crowd-sourced mood tag statistics from Last.fm users, which have been trans-
formed to 2-dimensional coordinates reflecting energy and pleasantness. The
similarity between artists is measured by first obtaining the average location of
each artist based on their track coordinates. The average locations then enable
computing distances between artists and using these as the similarity metric.
Content-based linking involves methodology of Music Information Retrieval
(MIR) [2] which facilitate applications that rely on perceptual, statistical, se-
mantic or musical features derived from audio using digital signal processing
and machine learning methods. These features may include statistical aggre-
gates computed from time-frequency representations extracted over short time
windows. Higher-level musical features include keys, chords, tempo, rhythm, as
well as semantic features like genre or mood, with specific algorithms to ex-
tract this information from audio. To exploit different types of similarity, we
5
https://acousticbrainz.org/
6
http://dev.mendeley.com/
7
http://dev.elsevier.com/
8
http://www.alchemyapi.com
model each artist using three main categories of audio descriptors: rhythmic,
harmonic and timbral. The features are obtained from the AcousticBrainz Web
service which provides descriptors in each category of interest. For each artist
in our database, we retrieve features for a large collection of their tracks in the
above categories, including beats-per-minute and onset rate (rhythmic), chord
histograms (harmonic) and MFCC (timbral) features.
4 Conclusion
MusicWeb is an emerging application to explore the possibilities of linked data-
based music discovery. It facilitates users to engage in interesting discovery paths
through the space of music artists. The aim is to gather in one application various
different approaches to music discovery and how they can benefit from linked
music metadata. The next steps are directed toward evaluating its potential
acceptance by end users, in particular, exploring which linking methods listeners
find most appealing or interesting, and which they would use more often.
MusicWeb is accessible online: http://musicweb.eecs.qmul.ac.uk/
References
1. Mathieu Barthet, György Fazekas, Alo Allik, and Mark B. Sandler. Moodplay:
an interactive mood-based musical experience. In George Kalliris and Charalampos
Dimoulas, editors, Proceedings of the Audio Mostly 2015 on Interaction With Sound,
AM ’15, Thessaloniki, Greece, October 7-9, 2015, pages 3:1–3:8. ACM, 2015.
2. Michael A. Casey, Remco Veltkamp, Masataka Goto, Marc Leman, Christophe
Rhodes, and Malcolm Slaney. Content-based music information retrieval: Current
directions and future challenges. volume 96. IEEE Proceedings, April 2008.
3. Ò. Celma. Music Recommendation and Discovery:The Long Tail, Long Fail, and
Long Play in the Digital Music Space. Springer Verlag, 2010.
4. M.S. Fabian, K. Gjergji, and W. Gerhard. YAGO: A core of semantic knowledge
unifying wordnet and wikipedia. In 16th International World Wide Web Conference,
WWW, pages 697–706, 2007.
5. Gary Marchionini. Exploratory search: from finding to understanding. Communi-
cations of the ACM, 49(9), 2006.
6. E. Oren, R. Delbru, and S. Decker. Extending faceted navigation for RDF data. In
ISWC, pages 559–572, 2006.
7. Alexandre Passant. dbrec - music recommendations using DBpedia. In Peter F.
Patel-Schneider, Yue Pan, Pascal Hitzler, Peter Mika, Lei Zhang, Jeff Z. Pan, Ian
Horrocks, and Birte Glimm, editors, The Semantic Web - ISWC 2010 - 9th Inter-
national Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11,
2010, Revised Selected Papers, Part II, volume 6497 of Lecture Notes in Computer
Science, pages 209–224. Springer, 2010.
8. Yves Raimond, Samer A Abdallah, Mark B Sandler, and Frederick Giasson. The
music ontology. In ISMIR, pages 417–422. Citeseer, 2007.