Music folksonomies have an inherent loose and open semantics, which hampers their use in structured browsing and recommendation. In this paper, we present a method for automatically obtaining a set of semantic facets underlying a folksonomy of music tags. The semantic facets are anchored upon the structure of the dynamic repository of universal knowledge Wikipedia. We illustrate the relevance of the obtained facets for the description of tags.
Music is a complex phenomenon that can be described according to multiple facets. Descriptive facets of music are commonly de ned by experts (e.g. stakeholders in the music industry) in professional taxonomies. Multifaceted descriptions are especially useful for music browsing and recommendation. For instance, recommendations of the Pandora Internet radio use around 400 music attributes grouped in 20 facets,1 as for instance Roots (e.g. \Afro-Latin Roots"), Instrumentation (e.g. \Mixed Acoustic and Electric Instrumentation"), Recording techniques (e.g. \Vinyl Ambience"), or In uences (e.g. \Brazilian In uences"). 1http://en.wikipedia.org/wiki/List_of_Music_ Genome_Project_attributes WOMRAD 2010 Workshop on Music Recommendation and Discovery, colocated with ACM RecSys 2010 (Barcelona, SPAIN) Copyright c . This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
However, there exists no consensual taxonomy for music.
Previous research showed the music industry uses
inconsistent taxonomies [
An alternative strategy for describing music consists in relying on the broadness of the web and making use of the \wisdom of the crowds". Many music websites allow users themselves to assign their own descriptive tags to music items (artists, albums, songs, playlists, etc.). For instance, users of the website Last.fm tagged the band Radiohead as \90s", \00s", \alternative", \post-punk", \britpop", \best band ever", among other things. The combination of annotations provided by thousands of music users leads to the emergence of a large body of domain-speci c knowledge, usually called folksonomy. Due to its informal syntax (i.e. direct assignment of tags), the tagging process allows the collective creation of very rich tag descriptions of individual music items.
When compared to taxonomies de ned by experts, music folksonomies have several advantages. First, completeness, they ideally encompass all possible \ways to talk about music", including both lay and expert points of view. Second, due to the continuous nature of the tagging process, folksonomies tend to be well updated. Third, they usually incorporate both commonly accepted and generic concepts, as well as very speci c and local ones.
It seems reasonable to assume that folksonomies tend to encompass various groups of tags that should re ect the underlying semantic facets of the domain including not only traditional dimensions (e.g. instrumentation), but also more subjective ones (e.g. mood). However, the simplicity and user-friendliness of community-based tagging imposes a toll: there is usually no way to explicitly relate tags with the corresponding music facets. For instance, a user may assign a number of tags related with music genre without ever actually explicitly specifying that they are about \music genre". For providing a exible browsing experience, this is a signi cant disadvantage of folksonomy-based classi cation in relation to classi cation based on taxonomies, where the information about which facets are being browsed can be made explicitly available to the user.
In this paper, we approach an essential research question that is relevant to bridging this gap: Is it possible to automatically infer the semantic facets inherent to a given music folksonomy? A related research question is whether it is then possible to classify elements of that music folksonomy with respect to the inferred semantic facets?
We propose an automatic method for (1) uncovering the set of semantic facets implicit to the tags of a given music folksonomy, and (2) classify tags with respect to these facets. We anchor semantic facets on metadata of the semistructured repository of general knowledge Wikipedia. Our rationale is that as it is dynamically maintained by a large community, Wikipedia should contain grounded and updated information about relevant facets of music, in practice.
Music tags have recently been the object of increasing
attention by the research community [
To our knowledge, however, few e orts have been
dedicated to the particular task of automatically identifying the
relevant facets of music tags. In their work on inferring
models for genre and artist classi cation, Levy et al. apply
dimensionality reduction techniques to a data set of tagged
music tracks in order to obtain their corresponding
compact representations in a low-dimensional space [
Cano et al. use the WordNet ontology to automatically
describe sound e ects [
Our method consists in using metadata from Wikipedia to infer the semantic facets of a given music folksonomy. This is performed in two steps. In the rst step, we specialize the very large network of interlinked Wikipedia pages to the speci c domain of the music folksonomy at hand. This is done by maximizing the overlap between Wikipedia pages and a list of frequent tags from the folksonomy. As the resulting network still represents a very large number of nodes, in a second step, we focus on the most relevant ones (node relevance being de ned as an intrinsic property of the network). This step also includes additional re nements. 3.1
Wikipedia pages are usually interlinked, and we use the links between two particular types of pages (i.e. articles and categories) to construct a music-related network. Concretely, we use the DBpedia knowledge base (http://dbpedia. org/) that provides structured, machine-readable descriptions of the links between Wikipedia pages (DBpedia uses the SKOS vocabulary, in its 2005 version).2 In particular, we make use of two properties that connect pages in DBpedia: (1) the property subjectOf, that connect articles to categories (e.g. the article \Samba" is a subjectOf of the category \Dance music", and (2), the property broaderOf, that connect categories in a hierarchical manner (e.g. the category \Dance" is a broaderOf of the category \Dance music", which is a broaderOf of the category \Ballroom dance music").
We start from the seed category \Music" and explore its neighbourhood from the top down, checking whether connected categories can be considered relevant to the music domain. A category is considered relevant if it satis es any of the two following conditions:
It is a tag from the folksonomy, such as for example \Rock and Roll". (This condition will be referred to as isMusical ); At least one of its \descendants" is a tag from the folksonomy and the substring \music" is included in the title or the abstract of the corresponding Wikipedia article. (This condition is further referred to as isTextMusical.)
The \descendants" of a category are fetched from DBpedia using the two connecting properties previously described. These descendants can be either \successors" (i.e. all direct subjectOf and broaderOf of this category), or successors of successors, and so on. This iterative search is limited by a maximum depth, empirically xed to a value of 4. Indeed, experiments with smaller values yielded a signi cant reduction of the tag coverage, while experiments with greater values did not increase signi cantly the coverage.
If any of the previous conditions is satis ed, the category, its successors and their edges are added to the network. Otherwise, the category and all incident edges are removed. The algorithm proceeds iteratively (following a Breadth-First search approach) until no more categories can be visited. A summarized version of the method for obtaining a music-related network is described in algorithm 1. 3.2
Once the network of music-related categories is built, the next step is to nd the nodes that are potentially more relevant to the network than others.
We invert the direction of the edges of the network in order to point back in the direction of the most generic category, i.e. \Music", and we compute the PageRank of the 2http://www.w3.org/TR/2005/ WD-swbp-skos-core-spec-20051102/
C else
N end (V
E Data: C = ;, a list of categories (a queue, initially empty); N = (V; E), a directed network with a set of nodes V and a set of edges E (initially empty); Result: N , network with music nodes; C C [ \M usic"; while C 6= ; do c f irst element of C; C C c; if (c isMusical) _ ((at least one descendant of c isMusical) ^ (c isTextMusical)) then
(V V [ c [ successors(c) N
E E [ edges between c and successors(c) C [ successors(c)
V
E
c
all edges incident in c
end
Algorithm 1: Pseudo-code for the creation of a network
of music-related categories from Wikipedia.
resulting network. PageRank [
In order to capture general yet complementary facets of music, we aim at reducing semantic overlap as much as possible by applying the following lters: Stub Filter: We remove all categories with substring \ by " and \ from ". We noticed that many categories in Wikipedia are actually combinations of two more general categories, as for instance \Musicians by genres", which is halfways between \Musicians" and \Music genres" (see also gure 1). Further, we also remove categories that include \ music(al) groups" (e.g. \Musical groupsfrom California" that has hundreds of connected categories, hence a high PageRank). Most of these categories are used as stubs, even sometimes explicitly so we also excluded categories with the word \stub". Over-Specialization Filter: We exclude all categories that include lexically a more relevant category. Many relevant categories are specializations of other more relevant ones, this occurs mostly with concepts related to anglophone music, which are described in great detail in Wikipedia (e.g. \American Musicians" includes \Musicians" that has a higher PageRank).
Tag Filter: We remove all categories that are tags. Our objective is to uncover music facets that are implicit to the tags that make up a folksonomy. In general, tags are elements of such facets, not the facets themselves.
We experimented our method on a large dataset of artist tags, gathered from Last.fm during April 2010. The dataset consists of around 600,000 artists and 416,159 distinct tags. This dataset was cleaned in order to remove noisy/irrelevant data: (1) tags were edited in order to remove special characters such as spaces, etc.; (2) tags were ltered by weight3, only tags with a weight 1 were kept; and (3) tags were ltered by popularity, keeping only tags with popularity 10, i.e. keeping only tags that were assigned to at least 10 artists. As a result, the nal dataset consists of 582,502 artists, 39,953 distinct tags, and 9.03 tags per artist.
After running both stages of our method, we obtained a list of 333 candidate facets. Table 1 contains the top-50 facets, ordered by pagerank (top to bottom, left to right).
In order to assign a set of facets to a given Last.fm tag, we process the subnetwork of Wikipedia pages specialized to the Last.fm folksonomy (obtained in section 3.1), as described in algorithm 2 (Note that this process is restricted to tags that can be matched to one of the nodes in the network). 3i.e. Last.fm \relevance weight", which goes from 0 to 100 Data: C = ;, a list of categories (initially empty); F , a list of top-N music facets; t, a Last.fm tag; Result: T F , list of facets applied to tag t; iter 1; T F = ;; while (F 6= ;) _ (iter maxIter) do
C C [ predecessors(t); if (9f 2 (F \ C)) then
T F T F [ f
F F f end iter iter + 1 end Algorithm 2: Pseudo-code for assigning Wikipedia facets to Last.fm tags
Given a Last.fm tag t, we look at its \predecessor" categories c, or more formally: predecessors(t) = fcj(t broaderOf (c)) _ (t subjectOf (c))g: If any of these predecessors is a top-N facet, it is then assigned to t. The process continues iteratively until no more facets can be assigned to the tag, or a maximum number of iteration (maxIter) is exceeded. We empirically set this value to 8. This condition can be interpreted as the maximum distance in the network between a tag and a facet.
Table 2 presents a small subset of the obtained facets, followed by a subset of their corresponding list of top tags. Top tags are chosen based on the distance (in number of successive edges in the music network) to the given facet.
The relevance Rtf of a music facet f to a tag t is computed as the normalized inverse distance dtf {in number of successive edges{ between t and f : 1 Rtf = Pdtf 1 i dti
For example, in gure 1, given the tag bulgarian hip-hop, our method starts navigating through the predecessors of this tag until nally reaching two music facets: Music genres and Music geography: bulgarian hip-hop: {(Music_genres, 0.4),
(Music_geography, 0.6)}
Although potentially more complete and up-to-date than taxonomies, music folksonomies lack structured categories, a particularly relevant aspect to browsing and recommendation. In this paper, we addressed the problem of uncovering the underlying semantic facets of the Last.fm folksonomy, using Wikipedia as backbone for semi-structured semantic categories.
There are many avenues for future work. First and foremost, we intend to evaluate the relevance of the obtained facets via systematic evaluations of tag classi cation. We will also study the distributions of music facets with respect to artist popularity. Further work should also relate to evaluating the usefulness of the obtained facets in a number of tasks, such as music recommendation, or tag expansion. We also intend to release the data (and code used to obtain it) in order to stimulate its use by fellow researchers. 6.
Thanks to Oscar Celma (BMAT), Eduarda Mendes Rodrigues (FEUP) and anonymous reviewers for useful comments. This work was partly supported by the Ministerio de Educacion in Spain, and the Fundaca~o para a Ci^encia e a Tecnologia (FCT) and QREN-AdI grant for the project Palco3.0/3121 in Portugal.