=Paper=
{{Paper
|id=Vol-3246/04_paper-8069
|storemode=property
|title=Towards a Knowledge Graph Representation of FAIR Music Content for Exploration and Analysis
|pdfUrl=https://ceur-ws.org/Vol-3246/04_paper-8069.pdf
|volume=Vol-3246
|authors=Emanuele Storti
|dblpUrl=https://dblp.org/rec/conf/ercimdl/Storti22
}}
==Towards a Knowledge Graph Representation of FAIR Music Content for Exploration and Analysis ==
https://ceur-ws.org/Vol-3246/04_paper-8069.pdf
Towards a Knowledge Graph Representation of FAIR Music
Content for Exploration and Analysis
Emanuele Storti
Dipartimento di Ingegneria dell’Informazione, via Brecce Bianche 60121, Ancona, Italy
Abstract
This paper introduces the ontological model for a FAIR digital library of music documents which
takes into account a variety of music-related information, among which editorial information on
documents and their production workflow as well as the score content and licensing information.
The model is complemented with annotations (e.g. comments, fingering) on music documents
produced by end-users, capable to add a social layer over the framework which enables the
building of user-centric music applications. As a result, a machine-understandable knowledge
graph of music content is defined, which can be queried, navigated and explored. On top of
this, novel applications could be designed, like semantic workplaces where music scholars and
musicians can find, analyse, compare, annotate and manipulate musical objects.
Keywords
music score, FAIR Data, Linked Data, Knowledge Graph,
1. Introduction
Digital repositories for musical content have long been used as systems to categorize
information on documents related to the musical domain. While some of them only
act as metadata catalogs for documents stored in physical libraries, others also host
digital versions of the corresponding documents. For instance, the International Music
Score Library Project (IMSLP)1 and the Sheet Music Consortium2 describe digitized
music documents which typically originate from printed sources, e.g. in the form of
scanned images, fully encoded scores or other formats. On the other hand, repositories
like MusicBrainz3 are focused on storing information on music production, including
metadata on records, artists, performers, and relations among them and to external
vendors.
Often through collaborative efforts, some of such repositories have reached significant
dimensions and now include up to millions of documents. However, they mostly operate
as information silos, storing only a particular kind of information with customised access
TPDL2022: 26th International Conference on Theory and Practice of Digital Libraries, 20-23 September
2022, Padua, Italy
Envelope-Open e.storti@univpm.it (E. Storti)
Orcid 0000-0001-5966-6921 (E. Storti)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 Interna-
tional (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
http://imslp.org/
2
https://digital.library.ucla.edu/sheetmusic/
3
https://musicbrainz.org/
rules and data/metadata representation models. Even in case APIs are enabled to
access metadata, e.g. for IMSLP and MusicBrainz, they are defined through customized
interfaces which do not refer to commonly used standards.
While standard formats for digital representation of scores (e.g., MIDI or MusicXML)
have been proposed and are widely adopted by several communities, the current lack of
shared solutions for organizing data in musical repositories, like standardized vocabularies,
has a great impact on interoperability among different sources and data exchange. As
such, in order to integrate disparate sources of information, a manual alignment of
heterogeneous datasets needs to be performed on a case-by-case basis, which is an
error-prone and a time consuming solution.
Unlike other application domains, the musical domain is witnessing only recently
a number of proposals for the definition of uniform vocabularies for metadata, also
pushed by international efforts towards Open and FAIR (Findable, Accessible, Inter-
operable, Reusable) Data [1], especially for libraries maintained by public institutions
and foundations. Publishing according to the FAIR principles, which are becoming a
requirement for public funding in many countries, means to assign each document a
unique digital identifier (DOI), providing a rich set of metadata (Findability), granting
the access to (meta)data through common protocols, possibly with authentication and
authorisation mechanisms (Accessibility), referring to widely adopted standards, possibly
expressed through formal formats which are machine-understandable (Interoperability),
and ensuring reuse by declaring the licence and the provenance (Reusability). In this
sense, the principles can refer both to metadata (e.g., the description of a score in terms
of author, title, date of publication, publisher, number of parts, tonality) and data (the
score symbolic content in terms of parts, measures, notes).
As also argued by several authors (e.g. [2, 3]), these principles are meant to support
the transition towards a more interconnected and open Web of musical Data, capable to
empower both user and machines to more easily retrieve musical resources and, whenever
possible, combine them to produce integrated views over disparate datasets, derivative
works and innovative applications. Among them, smarter and more flexible search engines
capable to query both the metadata of a score, its symbolic content, together with its
publishing record, e.g. to retrieve the editorial details of scores composed by “Johann
Sebastian Bach” between 1723 and 1750, in the key of “D major”, which include a
violin part starting with a given rhythmic pattern, i.e. 16 semi-quavers. On top of this,
novel applications could be designed, like semantic workplaces where music scholars and
musicians can find, analyse, compare, annotate and manipulate musical objects.
Towards this objective, this paper reports on ongoing work on the development of a
FAIR representation of metadata and data related to music digital content. The proposed
model, in the form of a knowledge graph, integrates existing standards and ontologies
for the representation of metadata on music objects (work, scores, records) and the
content of music scores to provide a homogeneous view over disparate data sets. As a
result, a machine-understandable graph of music content is defined, which can be queried,
navigated and explored.
The rest of this work is structured as follows: Section 2 summarizes relevant work in
the Literature focusing on semantic representation of musical content. In Section 3 an
integrated model is proposed, on top of which queries integrating various information can
be run, as exemplified in Section 4. Finally, Section 5 concludes the work and discusses
future work.
2. Related work
The use of different representation mechanisms, file formats and schemas for the large mass
of available documents on the web, bring interoperability issues that make integration
of data challenging. To overcome such shortcomings, semantic technologies have been
exploited to define vocabularies, taxonomies or ontologies providing the terminology that
can be used to annotate documents. Represented in a formal and unambiguous format,
such models enable the definition of machine-readable descriptions and ultimately the
representation of knowledge in a processable way. In this context, the term “Linked
Data” refers to a set of semantic technologies and publication practices that are used
to create a graph of interconnected datasets. A distinguished principle of this approach
is that each data element has a unique identifier (URI) over the web that can be
reused by other datasets. To make an example, in DBPedia4 , a project aiming to
extract structured content from Wikipedia and publish them as Linked Data, the URI
“https://dbpedia.org/resource/Johann_Sebastian_Bach” represents Johann Sebastian
Bach. If digital libraries reuse such an URI to refer to Bach, instead of redefining a
custom identifier, their integration would be much facilitated. Furthermore, according to
the Linked Data principles, datasets must be accessible through standard protocols such
as HTTP and must be represented through standard and self-documenting languages like
RDF [4]. This language enables to represent information as a set of basic statements (or
triples) in the form subject-property-object. The union of the triples generates a so-called
knowledge graph. Finally, data can be queried at Web scale through the SPARQL
language. In the following, some ontologies for the representation of music metadata and
content are summarized.
Music ontology [5] is a modular and extensible ontology to formally represent music-
related information, that has been adopted by several projects including BBC Music and
DBTune. Its main purpose is to provide the terminology to interlink different online
catalogues. While a basic level of detail only deals with purely editorial information,
a second level introduces the concept of event, which is used to describe a workflow
involving the composition of a musical work, its arrangement, performances of such
an arrangement and recordings of the performances. Music ontology builds on FOAF,
a vocabulary for describing people, groups of people and organisations, on the event
ontology, a vocabulary for describing events, on the timeline ontology and the Functional
Requirements for Bibliographic Record (FRBR) (discussed in Section 3).
Several formats have been proposed for the symbolic content of music score. Among
them, MIDI5 , originally presented in 1981, has been the most popular technical standards
for a communication protocol and digital interface enabling a variety of digital system
4
http://dbpedia.org
5
https://www.midi.org/specifications/file-format-specifications
Figure 1: Incipit of the Brandenburg Concerto No.5 in D major, Johann Sebastian Bach.
to record, edit and play music. While MIDI is more focused on connectivity and music
playback than to actually representing symbolic content, more recently MusicXML[6]
has been proposed as an XML-based format for encoding western musical notation. It
is intended for the exchange of music documents across different scorewriters and other
applications. The Music Encoding Initiative (MEI)6 is a community-driven, open-source
effort to define a system for encoding musical documents in a machine-readable structure.
Like MusicXML, MEI is encoded as an XML language, but includes a more advanced
representation of notations beyond the common western one (e.g. mensural and medieval
neume notations).
More recently, some ontologies have been proposed providing RDF vocabularies to
describe the symbolic content of music scores. The MIDI Linked Data Cloud [7] proposes
to use the Linked Data approach to interconnect symbolic music descriptions contained
in MIDI files, while MusicOWL [8] is an ontology including classes and properties to
fully represent a MusicXML score in RDF.
Similarly, in [9] a framework is proposed for extracting knowledge from music scores
that can be inferred from music notation, e.g. phrases, cadences, dissonances. With a
different objective, the Music Theory Ontology [10] aims at defining basic theoretical
musical concepts to build a model useful for music education and analysis.
On top of the mentioned approaches, some frameworks have been proposed to support
specific applications for analysis. As an extension of the MIDI Linked Data Cloud,
the HaMSE ontology [11] is devised to support musicological analysis by harmonizing
different representations (audio and score with a mutual alignment), and by including
musicological features such as chord progressions, rhythmic patterns or intervals. With a
focus on annotation of music performances, in [12] the MELD framework is introduced,
augmenting the MEI-encoded score elements with real-time annotation of a score during
a performance. Following an approach that is complementary to the present work, the
Audio Commons Ontology [13] builds on the Music Ontology for the representation
of audio content in the broader context of audio production and sharing, following an
approach towards the interoperability of different repositories.
6
https://music-encoding.org/
3. Semantic model of the framework
This section is devoted to introduce the main components of the model used for the
representation of information, in the context of an online digital library of music doc-
uments. The model takes into account editorial information on documents and their
production workflow as well as the score content and licensing information. The model is
complemented with annotations (e.g. comments, fingering) on music documents produced
by end-users, capable to add a social layer over the framework which enables the building
of user-centric music applications. In order to be fully compliant with the FAIR princi-
ples, both metadata and data are represented by referring to open shared vocabularies
represented in the RDF language. Hence, the final model stems from the integration of
such standards and ontologies, as also reported in the following subsections:
• generic metadata on documents and other resources are expressed through Dublin
Core properties;
• metadata on music works, scores, recordings, performances and the workflow for
their creation are represented through the Music Ontology;
• information on provenance is represented through the PROV-O ontology;
• the content of music scores is represented through the MusicOWL ontology;
• licensing information is represented through the Music Ontology and the Creative
Commons schema;
• information related to user content, which can be attached to any music information
in the model is represented through the Web Annotation Vocabulary.
To avoid redefining specific terms to express values of metadata, URIs from a number
of further external resources have been reused. They include DBPedia and Wikidata7
for artists’ names, the Tonality ontology8 to represent the tonalities (e.g. E minor), the
Music Vocabulary9 which includes a taxonomy of music forms (e.g. Concerto, Sonata)
and a taxonomy of ensemble types (e.g., Ensemble, Orchestra).
3.1. Core document metadata
Dublin Core (DC)[14] is a set of 15 metadata properties for describing generic resources
on the web, either physical or digital, formulated by the Dublin Core Metadata Initiative
(DCMI). In the context of Linked Data, Dublin Core is one of the most popular vocabular-
ies in RDF and is extensively used for resource description and to provide interoperability
for metadata vocabularies among different datasets in a variety of domains.
Among the metadata properties, a resource can be characterized in terms of a title, a
creator, one or more subjects useful for descriptive or classification purposes, a textual
description, a publisher, a publication date, a type, a format, a source to specify one or
more resources from which the resource is derived, the language, specification of rights
held in and over the resource. Other properties are defined by DCMI Metadata Terms,
7
https://www.wikidata.org/wiki/
8
http://purl.org/ontology/tonality/key/
9
http://www.kanzaki.com/ns/music
which extends Dublin Core with further terms, e.g., to specify that a resource isPartOf
another resource, versioning information, the licence and the rightsHolder among others.
An example of music digital library using some Dublin Core properties is the Sheet Music
Consortium.
3.2. Document production workflow
Aspects related to music production are defined through classes and relations from
the Music Ontology. Like several other ontologies focusing on the representation of
musical catalogs, Music Ontology is built on top of a generic and flexible model named
Functional Requirements for Bibliographic Record (FRBR), proposed by the International
Federation of Library Association (IFLA). The model is aimed to describe documents
and their evolution and is particularly suited for both physical and digital resources. The
representation of a musical object is done in FRBR at various levels of abstractions, from
the generic concept to the specific realization, through the following main elements:
• Work is an abstract concept representing an artistic creation, independently of its
concrete realizations, e.g. the Brandenburg Concerto No.5 by J. S. Bach.
• An Expression is the realisation of the artistic content of a Work. For instance,
each version of the score for Bach’s concerto that has been published is a different
expression of the same work. It can be realized through one or more Manifestations.
• A Manifestation represents a particular physical or electronic embodiment of an
expression, e.g. the specific formats in which a particular edition of a score can be
available: in textual form, as a scanned PDF, in MusicXML. A Manifestation is
exemplified by one or more Items.
• An Item is a particular instance of a Manifestation, for instance a specific copy of
a record.
The Music Ontology interconnects such elements through the notion of event, e.g. a
Composition is an event made by a MusicArtist producing a MusicalWork. This last
represents an abstract entity and not a particular concrete realization of it (e.g., a
published score or a recording). An Arrangement is an event which produces a score out
of a work. A Performance produces a Sound which can be recorded. A Recording event
takes a work as input and produces a Signal which can then be published as a Record. A
PublishedScore represents a concrete score (i.e., a manifestation), which has a title, a
licence, a publication date, a publisher, and may be available in different formats.
Dublin Core properties have been used to specify the title of a musical work or a
score, the composition or recording date, the format of a score (as a MIME type), while
possible derivation from other resources are represented through the PROV-O10 property
wasDerivedFrom.
The following code shows a fragment of the RDF triples representing the metadata
for the published score of the “Brandenburg Concerto No.5” by Johann Sebastian Bach,
with a title and a date related to its creation, a public domain licence, two publishers
10
https://www.w3.org/TR/prov-o/
and Leipzig as a publishing location. The namespace before the URIs are shorthand for
the full URI namespace of the corresponding ontology (“mo” stands for the full Music
Ontology namespace, “cc” for the Creative Commons schema, “dc” for Dublin Core,
“dbpedia” for DBPedia). Please note that, whenever possible, the value of properties are
URIs taken from external sources. In some cases, e.g. the names of the publishers, or the
title, a simple string (Literal) was used.
:pscoreBrConcert5 rdf:type mo:PublishedScore;
mo:licence cc:PublicDomain.
dc:title ‘‘Brandenburg Concerto No.5 in D Major’’;
dc:date ‘‘1851’’^^xsd:gYear;
mo:publisher [ a foaf:Agent;
rdfs:isDefinedBy dbpedia:Bach_Gesellschaft];
mo:publisher [ a foaf:Agent;
rdfs:isDefinedBy dbpedia:Breitkopf_&_Härtel];
mo:publishing_location dbpedia:Leipzig.
3.3. Score content
The content of a score is represented through the MusicOWL ontology, which provides
the terminology for the RDF representation of MusicXML documents. This format was
chosen over others because MusicXML is one of the most popular and widely adopted file
format for music content encoding and sharing. Indeed, several scorewriter applications
include import/export tools to/from this format (e.g. Cubase, MuseScore, Finale),
making the production and the sharing of MusicXML files easier, and several digital
libraries provide MusicXML file sources11 .
The ontology includes classes to represent one or more ScorePart, which includes a
Staff, which in turn has Voices. A score part include a set of Measures, which can have
multiple NoteSets, i.e. containers of notes. A Note is characterized by a Duration (e.g.
1/4) and specifies a natural value (the pitch) and a possible modifier (e.g., sharp, flat,
double sharp). A fragment of the beginning of the lead violin part for the Brandenburgen
Concerto No.5 (see also Figure 1) is represented as follows:
:measure1 a mso:Measure;
mso:hasNoteSet :noteset1.
:noteset1 a mso:NoteSet;
mso:hasNote :note1, :note2, :note3, :note4;
mso:duration mso:Quarter;
mso:nextNoteSet :noteset2.
:note1 a chord:Note;
chord:natural note:D.
:note2 a chord:Note;
chord:natural note:D.
11
A partial list is available at https://www.musicxml.com/music-in-musicxml/.
:note3 a chord:Note;
chord:natural note:F;
chord:modifier chord:sharp.
:note4 a chord:Note;
chord:natural note:F;
chord:modifier chord:sharp.
3.4. Licensing information
A relevant information for any resource published in a FAIR repository is the specification
of the licence, which determines what operations are permitted or prohibited on the
resource and what requirements are set for users. Particularly, the FAIR principle R1.1
requires that (meta)data are released with a clear and accessible data usage license.
Licensing information for a published score or a record have been described through
the mo:licence property, specifying the licence by relying on the Creative Commons
schema. In such a way, it is possible to declare specific CC licenses by combining
different terms, thus making distinct aspects of the licence machine-understandable.
The following snipped of RDF assigns a Creative Commons licence named Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) to a published
score.
<#publishedscore> mo:licence :cc-by-nc-nd-4.
:cc-by-nc-nd-4 a cc:Licence;
cc:permits cc:Reproduction;
cc:permits cc:Distribution;
cc:prohibits cc:CommercialUse;
cc:requires cc:Attribution;
rdfs:seeAlso ;
3.5. Annotation of scores
Fingering is the process of mapping each note on a score to a fingered position on some
instrument. Apart from didactic music notes, fingering indications are typically not
reported in published scores, leaving them to execution choices. Nonetheless, especially for
music students, finding the most effective fingering may be tricky and often crucial in the
educational process. As well as fingering, other comments or notes are very often reported
in music scores to annotate information which is relevant for the performer during the
educational process or the performance. However, no standard representation of this
information has been developed on the topic, also because of its rather informal structure.
For this reason, we rely on the Web Annotation Data Model [15] which describes a
structured model and format to enable annotations on generic target resources to be
shared and reused across different hardware and software platforms. In particular, we
refer to classes and properties defined by the Web Annotation Vocabulary12 . The class
12
https://www.w3.org/TR/annotation-vocab/#bib-annotation-model
Figure 2: Fragment of the knowledge graph describing the concept of composition, musical work
and score, with a focus on a published score.
oa:Annotation is used to declare an annotation and its metadata, including the creator
and the datetime. The annotation is linked to a target and a body. The former is any
given musical element in a score, being it a note, a noteset, a measure, a part or the whole
score, or others. On the other hand, the latter is the actual content of the annotation,
specifying its value, its format as well possible further metadata, e.g. language. In the
following, an example is shown of a fingering indication which is created by a user and
attached to a note:
:note1 a chord:Note ;
mso:hasOctave ‘‘4’’^^xsd:int ;
chord:natural note:D.
:anno1 a oa:Annotation ;
dcterms:creator :user1;
dcterms:created ‘‘2022-06-15T17:31:00.000’’^^xsd:dateTime;
oa:hasTarget :note1 ;
oa:hasBody [
a oa:TextualBody;
rdf:value ‘‘2’’ ;
dc:format ”text/plain” ] .
4. Querying the music content
The representation of the model through a RDF knowledge graph enables the possibility
to make queries to extract relevant information, that can involve different aspects of the
musical content. For instance, queries on bibliographic information of the document, e.g.
about the creator or the publisher, can be combined with information on the production
process, performances, as well as with information on the structure of the score, e.g. its
parts, its specific melodic/harmonic/rhythmic content. Furthermore, information on user
comments and annotations can be integrated as well. Queries are expressed through the
SPARQL language, which enables easy data access and interoperability with external
applications.
As an example of queries that can be expressed on the graph, the following one asks
for published scores authored by “Ludwig van Beethoven” which are released under the
public domain licence, for which a PDF version is available.
SELECT ?pscore
WHERE {
?c mo:composer dbpedia:Ludwig_van_Beethoven.
?c mo:produced_work ?work.
?work mo:arranged_in ?arr.
?arr mo:produced_score ?pscore.
?pscore mo:licence cc:PublicDomain.
?pscore mo:available_as ?pscore_pdf.
?pscore_pdf dc:format ?format.
FILTER (?format = mime:application/pdf).
}
To make a further example, the following query searches for scores including a measure
with a quarter and a semiquaver, and extracts comments on the measure.
SELECT ?pscore ?measure ?value
WHERE {
?pscore mso:movement ?mov.
?mov mso:hasScorePart ?part.
?part mso:hasMeasure ?measure.
?measure mso:hasNoteSet ?ns1.
?measure mso:hasNoteSet ?ns2.
?ns1 mso:nextNoteSet ?ns2.
?ns1 mso:hasDuration ?dur1.
?dur1 a mso:Quarter.
?ns2 mso:hasDuration ?dur2.
?dur2 a mso:16th.
?ann oa:hasTarget ?measure.
?ann oa:hasBody ?body.
?body rdf:value ?value.
}
5. Discussion
This paper introduced the ontological model for a FAIR digital library of music documents
which takes into account a variety of music-related information. The resulting RDF
model has the shape of a knowledge graph, where all the information can be explored and
queried according to the Linked Data approach, relying on standard tools and protocols.
As an example, Figure 2 shows a graphical representation of a fragment of the knowledge
graph describing the “Brandenburg Concerto No. 5” by Johann Sebastian Bach.
This ongoing work represents the roots on which a user-centric framework for docu-
mentation, editing and exchange of documents will be built. Future steps will be devoted
to develop the application layer on top of the model, which will include graphical user
interfaces to enable user-friendly browsing and exploration of the graph, annotation of
scores, their analysis and sharing.
On the other hand, several challenges still need to be address that call for extensions
of existing model schemas. On the one hand, almost all ontologies for the symbolic
representation of scores currently focus only on the western music notation. As a
consequence, other music traditions cannot be fully represented by using such formats, as
some work point out (e.g. [16]). Furthermore, existing models do not take into account
novel notation symbols, which may be characteristic of specific musical instruments and
specific music communities, e.g. contemporary compositions often include non-standard
notations that can hardly be automatically understood by optical music recognition
systems and hence represented through existing models.
References
[1] M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak,
N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al., The FAIR
Guiding Principles for scientific data management and stewardship, Scientific data
3 (2016) 1–9.
[2] A. Hofmann, T. Miksa, P. Knees, A. Bakos, H. Sağlam, A. Ahmedaja, B. Yimwadsana,
C. Chan, A. Rauber, Enabling FAIR use of Ethnomusicology Data–Through
Distributed Repositories, Linked Data and Music Information Retrieval, Empirical
Musicology Review 16 (2021) 47–64.
[3] D. M. Weigl, T. Crawford, A. Gkiokas, W. Goebl, E. Gómez, N. F. Gutiérrez, C. C.
Liem, P. Santos, FAIR Interconnection and Enrichment of Public-Domain Music
Resources on the Web, Empirical Musicology Review 16 (2021) 16–33.
[4] O. Lassila, R. R. Swick, et al., Resource Description Framework (RDF) model and
syntax specification (1998).
[5] Y. Raimond, S. A. Abdallah, M. B. Sandler, F. Giasson, The Music Ontology., in:
ISMIR, volume 2007, 2007, p. 8th.
[6] M. Good, MusicXML for notation and analysis, The virtual score: representation,
retrieval, restoration 12 (2001) 160.
[7] A. Meroño-Peñuela, R. Hoekstra, A. Gangemi, P. Bloem, R. d. Valk, B. Stringer,
B. Janssen, V. d. Boer, A. Allik, S. Schlobach, et al., The MIDI Linked Data Cloud,
in: International Semantic Web Conference, Springer, 2017, pp. 156–164.
[8] J. Jones, D. de Siqueira Braga, K. Tertuliano, T. Kauppinen, MusicOWL: the Music
Score Ontology, in: Proceedings of the International Conference on Web Intelligence,
2017, pp. 1222–1229.
[9] S. S. Cherfi, C. Guillotel, F. Hamdi, P. Rigaux, N. Travers, Ontology-based annota-
tion of music scores, in: Proceedings of the Knowledge Capture Conference, 2017,
pp. 1–4.
[10] S. M. Rashid, D. De Roure, D. L. McGuinness, A Music Theory Ontology, in:
Proceedings of the 1st International Workshop on Semantic Applications for Audio
and Music, SAAM ’18, Association for Computing Machinery, New York, NY, USA,
2018, p. 6–14.
[11] A. Poltronieri, A. Gangemi, The HaMSE Ontology: Using Semantic Technologies to
support Music Representation Interoperability and Musicological Analysis, arXiv
preprint arXiv:2202.05817 (2022).
[12] D. Weigl, K. Page, A framework for distributed semantic annotation of musical
score: “Take it to the bridge!”, in: 18th International Society for Music Information
Retrieval Conference, International Society for Music Information Retrieval, Suzhou,
China, 2017.
[13] M. Ceriani, G. Fazekas, Audio Commons Ontology: a data model for an audio
content ecosystem, in: International Semantic Web Conference, Springer, 2018, pp.
20–35.
[14] S. Weibel, J. Kunze, C. Lagoze, M. Wolf, Dublin Core Metadata for Resource
Discovery, Internet Engineering Task Force RFC 2413 (1998) 132.
[15] R. Sanderson, P. Ciccarese, H. Van de Sompel, S. Bradshaw, D. Brickley, L. J. G.
Castro, T. Clark, T. Cole, P. Desenne, A. Gerber, et al., Open Annotation Data
Model, W3C community draft 8 (2013).
[16] P. Proutskova, A. Volk, P. Heidarian, G. Fazekas, et al., From Music Ontology
Towards Ethno-Music-Onthology, in: Proceedings of 21st International Society
for Music Information Retrieval Conference (ISMIR 2020), ISMIR press, 2020, pp.
923–931.