=Paper=
{{Paper
|id=None
|storemode=property
|title=LED: curated and crowdsourced Linked Data on Music Listening Experiences
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_74.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/AdamoudBB14
}}
==LED: curated and crowdsourced Linked Data on Music Listening Experiences==
https://ceur-ws.org/Vol-1272/paper_74.pdf
LED: curated and crowdsourced Linked Data on
Music Listening Experiences
Alessandro Adamou1 , Mathieu d’Aquin1 , Helen Barlow1 and Simon Brown2
1
The Open University, United Kingdom
{alessandro.adamou, mathieu.daquin, helen.barlow}@open.ac.uk
2
Royal College of Music, United Kingdom
simon.brown@rcm.ac.uk
Abstract. We present the Listening Experience Database (LED), a
structured knowledge base of accounts of listening to music in docu-
mented sources. LED aggregates scholarly and crowdsourced contribu-
tions and is heavily focused on data reuse. To that end, both the storage
system and the governance model are natively implemented as Linked
Data. Reuse of data from datasets such as the BNB and DBpedia is inte-
grated with the data lifecycle since the entry phase, and several content
management functionalities are implemented using semantic technolo-
gies. Imported data are enhanced through curation and specialisation
with degrees of granularity not provided by the original datasets.
Keywords: Linked Data, Crowdsourcing, Digital Humanities, Data Workflow
1 Introduction
Most research on listening to music focuses on investigating associated cognitive
processes or analysing its reception by critics or commercial indicators such as
sales. There is only sporadic research on the cultural and aesthetic position of
music among individuals and societies over the course of history. One obstacle
to this kind of research is the sparsity of primary source evidence of listening to
music. Should such evidence be compiled, we argue that the adoption of explicit
structured semantics would help highlight the interactions of listeners with a
range of musical repertoires, as well as the settings where music is performed.
With the Listening Experience Database (LED)1 , we aim at covering
this ground. LED is the product of a Digital Humanities project focused on
gathering documented evidence of listening to music across history and musical
genres. It accepts contributions from research groups in humanities as well as
the crowdsourcing community, however, the data management workflow is super-
vised to guarantee that minimum scholarly conventions are met. Being conceived
with data reuse in mind, LED is natively implemented as Linked Data. All the
operations in the data governance model manipulate triples within, and across,
named RDF graphs that encode provenance schemes for users of the system.
1
LED, online at http://www.open.ac.uk/Arts/LED
Several content management functionalities available in LED, such as content
authoring, review, reconciliation and faceted search, incorporate Linked Data
reuse. Reused datasets include DBpedia2 and the British National Bibliography
(BNB)3 , with music-specific datasets currently under investigation. Reused data
are also enhanced, as the LED datamodel is fine-grained and allows for describing
portions of documents and excerpts, which are not modelled in the datasets at
hand. LED therefore also aims at being a node by its own right in the Linked
Data Cloud, providing unique content and contributing to existing data too.
At the time of writing, the LED dataset stores about 1,000 listening experience
records contributed by 25 users, half of whom being volunteers from the crowd.
2 Related work
A similar effort in aggregating structured data in primary evidence was already
carried out for reading experiences [1], though the process was not data-driven
and the resulting Linked Data were only marginally aligned. We also acknowl-
edge a project being carried out, which gathers direct personal experiences of
young users of the system, albeit with a minimal data structure4 . We also drew
inspiration from earlier accounts of using DBpedia for music, such as the dbrec
recommender [3]. Crowdsourcing is also gaining the attention of the Semantic
Web community, with very recent attempts at tackling data quality aspects [4].
3 The Listening Experience Database
We define a listening experience (LE) as a documented (i.e. with a quotable
and citable source) engagement of an individual in an event where some piece
of music is played. In terms of conceptual modelling, a LE is a subjective event,
and one document describing it is the quoted evidence reported in the database.
The lifecycle of data in LED involves the roles of contributor, consumer and
gatekeeper, and states called draft, submitted, public and blacklisted. Every arti-
fact stored in the system exists in one or more of these states (except blacklisted
ones, which exclude all other states), and a state determines if a user with a
certain role can “see” an artifact or not. What these artifacts are, depends on
the specific phases in the workflow, which are transitions between these states.
Authoring. Contributors populate the knowledge base by entering data on
a LE and its associated entities. The entry forms are dynamic and provide sug-
gestions and autocompletion data from LED and external datasets in real time
(cf. Figure 1). Artifacts declared during this phase remain in a draft state, only
to enter a submitted state once the contributor submits the LE to gatekeepers.
Review. Privileged users with the gatekeeper role review a submitted artifact
and either promote it to the public state, or reject it for blacklisting, or demote it
2
DBpedia, http://dbpedia.org
3
British National Bibliography, http://bnb.data.bl.uk
4
Experiencing Music, http://experiencingmusic.com
to draft again, which they can do by either taking over the artifact and amending
its data themselves, or sending it back to the original contributor.
Reconciliation. Gatekeepers can align and merge duplicate artifacts that
are found to match. They can compare candidate duplicates with other artifacts
in LED and third-party data. This operation does not modify their state.
Faceted search. Consumers can navigate LE’s by filtering keyword search
results by bespoke criteria which are not necessarily stored in LED, but also
reused from third-party datasets. Only public artifacts contibute to searches.
(a) Listening experience submission. (b) Autocompletion from the BNB dataset.
Fig. 1: Example of data entry for “Pictures from Italy” by Charles Dickens.
With a native Linked Data implementation, we can immediately integrate
reuse with every stage of the data lifecycle starting with data entry, and eliminate
a posteriori revision and extraction phases from the workflow, thereby reducing
the time-to-publish of our data and having them linked right from the beginning.
Also, the named graph model of quad-stores can encode provenance information
with the granularity of atomic statements [2], thus lending itself to fine-grained
and complex trust management models.
To encode the above workflow entirely in RDF, we used the named graph
paradigm in order to represent states and artifacts. Deciding on the scale of
the latter was an issue: while we intended to give gatekeepers control on single
RDF triples (or quads, from the named graph perspective), and to contributors
a way to support the truth or falsehood of a triple, this can be complex and
time-consuming. Therefore, artifacts are encapsulated into LE’s, musical works,
literary sources, agents (e.g. people, groups or organisations) and places: these
are, for instance, the classes of artifacts that gatekeepers may want to review or
reconcile. However, LE’s remain the core artifacts of the sytem: only by creating
or editing them can their associated artifacts be generated.
The LED knowledge base is partitioned into data spaces, each belonging
to a user or role. Every contributor owns two RDF graphs, one for draft ar-
tifacts and one for submitted ones. Thus, we can keep track of which con-
tributors support a fact by reusing it (e.g. ). There is a single graph for public artifacts, and one
for blacklisted ones. Contributors have access to the graphs they own plus the
public graph; gatekeepers can access every user’s submitted graph and the pub-
lic and blacklist graphs. State transitions are realised by parametric SPARQL
queries that selectively move RDF triples across these graphs. Along with these
data spaces there are rules that determine the visibility of triples to each user,
depending on the content of their private graphs. In general, these rules assume
contributors have greater confidence in the facts in their possession, and when
missing, they should trust those provided by the community or other datasets.
4 Demonstration
The audience will be given a live demonstration of the LED system, but from
the point of view of users with the privileged roles of contributor and gatekeeper.
We will show the benefits of reusing data from indexed datasets during the entry
phase, as well as the implementation of our governance model in Linked Data
and its effects on the representation of a resource as seen by the general public
or a specific user. Data reuse and enhancement will be demonstrated through a
LE entry form to be auto-populated in real time and open to input by audience
members. To demonstrate the governance model, we will run two distinct entries
with shared data through the whole draft-submission-gatekeeping lifecycle. We
will then show how differently the shared data and their RDF representations
appear to each user, based on the trust and provenance policies in place.
References
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graphs, provenance and trust. In Allan Ellis and Tatsuya Hagino, editors, WWW,
pages 613–622. ACM, 2005.
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Horrocks, and Birte Glimm, editors, International Semantic Web Conference (2),
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