Submitted on: 13.07.2018

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Linked Data: Metadata Schemata of the Music Scores of Jose Maceda

Collection

Sonia M. Pascua Faculty. University of the Philippines School of Library and Information Studies

E-mail address: sonia@slis.upd.edu.ph, smpascua@up.edu.ph

Copyright © 2018 by Sonia M. Pascua. This work is made available under the terms of the Creative

Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0

Abstract:

The purpose of the study is to provide a structural framework to organize and classify digitally

converted collections of the 23 music scores of Jose Maceda Compositions (JMC). Additionally, this

study explores a solution to the problem and the need to represent concepts in a computing medium.

It accounts the current state of metadata-creation practices across digital repositories and collections

for a comparative evaluation regarding usage and representation capabilities.

Keywords: Linked Data, Metadata, Schemata, Cataloging, Jose Maceda Collection, Music Ontology.

As we gear towards establishing Web 3.0, the Semantic Web, professionals in the field of

information are working hand in hand to develop the methodologies on how to put

intelligence out of our search. That user can find what they are looking for from the vast

WWW and not a misleading resource (Cleveland 2011). When Tim Berne’s Lee developed

the web, he never realized how his works would grow to the point that it’s unmanageable.

After 20 years of building the Web, in his TED talk in 2009 about “The Next Web,” he

introduced the idea of the OPEN DATA initiative to unlock the potential of the web and to

provide human a useful and intelligent data. The technology to realize this call is what he

called Linked Data

The purpose of the study is to provide a structural framework to organize and classify

digitally converted collections of the 23 music scores of Jose Maceda Compositions (JMC).

JMC is located at the UP Center for Ethnomusicology Library of the College of Music of the

University of the Philippines and houses a collection of sound documents comprising around

2,000 hours of recordings on reel to reel tapes and cassettes. This collection is the main

holding of the University of the Philippines (UP) Center for Ethnomusicology, founded in

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1997 by José Maceda himself. Due to its universal significance, the collection was inscribed

on the International Register of the Memory of the World Programme in 2007.

Now in its digital form, the JMC music scores collection, as proposed by this study is

represented in a format that allows relationships to be discovered in knowledge repositories

to improve the searching and retrieval of data. Linked Data is used as metadata schemata to

the needed information solution of the Center. It provides the editorial databases and storage

mechanism for content standards and controlled vocabularies which in ways promote an

optimal level of data extensibility, consistency, and information sharing. The use of the

Resource Description Framework (RDF) and compatible documents and formats to express

the music scores constructs the JMC Music Score Ontology (JMCMSO). It aims to provide a

model for publishing structured music score data on the Semantic Web. By extending not

only from the existing Music Ontology (MO) but also from other available extensions in the

Linked Open Data, JMCMSO has a unique approach of starting the data description from

essential topics and building them into a broader framework, covering all topics within the

music score domain. This also allows other ontologies to be plugged on top of the JMCMSO

namespaces and referenced as music scores modules, which is used to extend the ontology.

JMCMSO, as this research’s metadata description, is called, enables representation of data in

RDF triples, Simple Protocol and RDF Query Language (SPARQL) for databasing and Arc2

Framework, a PHP semantic tool. It also becomes extensible to define its format, data types,

and routines for the module of the Music Scores Ontology. Additionally, it discusses how it is

used comparative evaluation of the metadata schemata across digital repositories and

collections by enriching search results from the web data. Adoption of the methodology to

the entire collection of the Jose Maceda Collection (JMC) is recommended for future work.

Subtheme of the session

JMC is located at the UP Center for Ethnomusicology (UPCE) Library of the College of

Music of the University of the Philippines and houses a collection of sound documents

comprising around 2,000 hours of recordings on reel to reel tapes and cassettes. This

collection is the main holding of the University of the Philippines (UP) Center for

Ethnomusicology, founded in 1997 by José Maceda himself. Due to its universal

significance, the collection was inscribed on the International Register of the Memory of the

World Programme in 2007. .JMC is currently catalogued by an existing library system that

provides a unique accession number for each item and a brief description. It uses MS Excel

for data storage, and is only accessible within the Department of Ethnomusicology’s local

intranet.

The manual information retrieval system in JMC has been raised by its Librarian and library

staff to be tedious and scrupulous. They wish to have a better system that can aid them in the

efficient storage of description and fast assistance in their user’s search and quest for right

and appropriate material. Data extensibility, consistency, and information sharing appear to

be some of the significant challenges not only in JMC but the field of cataloging. Likewise,

there is a disparity in the metadata elements in the current metadata schemas, existing library

standards and RDF, a metadata model.

Relevant literature

Ontologies are described as electronic dictionaries that enable software to understand the

meaning of words (Yamaguchi, T., 1998). Ontologies are considered as higher-level

structures in which the concepts involved in a domain, along with all the possible semantic

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variations that a concept may have set out and specified (Sanchez, Cavero, et al., 2009). In

Sanchez, Cavero, et al. (2009) research, it is said that as a consequence of the ontologies'

concept-specifying task, they are used as elements for creating correspondences between

variables or processes of two or more systems. Hence, the ontologies can be used as

intermediaries facilitating interoperability.

Ontologies provide a classification of adaptive systems concerning to semantic technology

(Torre, 2009). Torre stated that ontologies define the vocabularies and terms used in a field

of knowledge. It is an aggregation and analysis of data to produce consistent interpretation

(Vugrala, et al., 2015) that contains specifications of the concepts that are needed to

understand a domain and the relationships between those concepts. Data can be mapped onto

ontology to put organization among information to create an explicit state for a shared

conceptualization.

Currently, some ontology related languages on the Web are XML, XML Schema, RDF, and

RDF Schema, all of which still lack expressive power (Alesso, 2009). The need not only for

humans but also for machines to process the content of information is the limitations of the

said languages. Applications need languages with dynamic reasoning capability for them to

be able to perform automatic data exchange and interpretation, thus going beyond the basic

semantics of XML Schema and RDFS.

In 2003, Web Ontology Language (OWL) was developed when W3C began addressing the

need for more expressive and reasoning ontology language and worked on the final

unification of the disparate international ontology effort into a standardized ontology (Alesso,

2009). It provides the machine-based systems that allow mathematical techniques to be

applied. It also adds more vocabulary for describing properties and classes: among others,

relations between classes (e.g. disjointness), cardinality (e.g. "exactly one"), equality, richer

typing of properties, characteristics of properties (e.g. symmetry), and enumerated classes

(W3C OWL, 2010).

Frequently used in information retrieval, OWL being their main applications are the

expansions of queries, semantic indexing of documents and the organization of search results,

they also provide lexical item that allows conceptual normalization and precise description of

specification on a particular domain (subject matter) (Yamaguchi, 007). The goal for

specifying ontologies is to add semantics to the definition of a concept and be able to capture

consensual knowledge so that it can be shared and reused by researchers in further

development of metadata schemata. (Gomez-Perez, 2004) and machine-readable (Gruber,

1993) definitions.

When talking about web ontologies, one should be able to understand first the coining

principles of Semantic Web. Semantic web comes from Sir Tim Berners-Lee, the inventor of

WWW, URI’s, HTTP, HTML. He first introduced this 20 years after he invented the web

(Berners-Lee, 2004) when he urged everyone to join the Linked Open Data (LOD) initiative.

As a planned extension of the Web, Semantic Web purposely aimed to establish

understanding in machines as how humans do (Berners-Lee, 2011) and make it easier for

both to collaborate. It offers users ability to work on shared knowledge by constructing

meaningful representations on the web. It is a web of data that can be processed by the

machine (Carvalho, 2011) and is now an important framework for the next generation of Web

architecture (Alesso, 2009). Fig. 1 shows the big picture of the layers of the Semantic Web.

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Fig 1. Semantic Web Layer Cake

Semantic Web languages was built upon XML and climb up to Markup Language Pyramid

(Allesso, 2009) as illustrated above.. It implements logic inferences and rule systems

developed by World Wide Web Consortium (W3C) and defines and links data using RDF

and OWL for effective discovery, automation, integration, and reuse across different

applications (Allemang, 2008).

RDF generally is the framework for how to describe resources in the Web (Sanchez, 2007). It

is also the model for data interchange in the Web and provides the foundation for publishing

and linking data (W3C, 2014). In accordance to RDF, resources in an ontology are described

into two main components: entities or classes and relationships. These components represent

real-world concepts. Combination of classes and relationships is known as triples. Notation

for classes is oval while for relationships is an arrow. Fig. 2 below shows a triple with an

empirical example.

Fig 2. Triple Model

As shown above, a triple is composed of subject - predicate - object. In an RDF statement,

subjects and predicates are represented by uniform resource identifiers (URI’s) while objects

are either URI’s or literals (Kalyanpur, 2006). Triples are the basic component of ontologies.

They can be merged to provide the comprehensive view of the real-world scenario within an

ontology.

Ontologies are extensible that instead of rewriting lines of codes new relationships can be

added to the existing ontologies. Furthermore adding relationships defines new concepts in an

ontology which produces RDF graphs. RDF graphs are sets of subject-predicate-object

triples (W3C RDF11-Concepts, 2014). Fig. 3 shows how new concepts have branched out

from another concept through relationships and form RDF graph.

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Fig 3. Extensible Triple Model / RDF Graph

There are several serialization formats used to express RDF. Examples are Triples and Graph

which are shown above. Others are Turtle, N3, RDF/XML, Microdata, RDFa, and JSON-LD.

These formats may have their syntax or source code; however, RDF as a model governs the

expression of the logically equivalent statements across formats.

RDFS is a semantic extension and improved RDF. It provides data-modeling vocabulary in

XML to RDF data. It facilitates the definition of basic ontology elements such as classes and

hierarchy of classes, properties, domain and range of properties (Fonou-Dombeu, 2011).

Using the RDFS, it is easy to subsequently define additional properties within a domain

without the need to re-define the original description of classes. It is focused on building

taxonomies (class hierarchies) which in turn aid in inference and search. See Fig. 4 for RDF /

RDFS layer that shows the extension of RDF to RDFS.

Fig 4. RDF/RDFS separate layers (Antoniou, 2004)

One benefit of the RDF / RDFS property-centric approach is that it allows anyone to extend

the description of existing resources, one of the architectural principles of the Web (Berners-

Lee, 1998).

Design or methodology

Music is a very specialized domain that needs dedicated experts to be identified. It requires

skilled professional and consultant in the field of music to capture features and content for an

ontology. Music Ontology (MO) is an ontology created by music domain experts and has

started building its large framework from basic topics of music-related information in the

semantic web. It allows easy link to its own namespaces and references in one of its Music

Ontology Modules (Gangler, 2010) Thus, this study utilizes the MO framework and extend it

by defining a Music Ontology extension module capturing features of the music scores of

Jose Maceda Compositions and other collection in the future and make it available to the

community.

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The MO extension module defined in this study is constructed based from the Knowledge-

Engineering Methodology. Fundamentally, guidelines of the Ontology Creation Process

(NSIO, 2004) are employed. Aforementioned is composed of a set of activities that concern

the ontology development process, the ontology life cycle, as well as the methodologies,

tools, and languages required for building ontologies (Mizoguchi, 2004). Branching

traditionally from the field of philosophy - metaphysics, Ontology Engineering has paved a

new meaning different from its first notion where it refers to the conceptualization of

existence (Alesso, 2009). It becomes an explicit specification of the conceptualization of the

domain; a way of representing a common understanding of a domain (Gruber, 1993),

especially in Information Science. It is currently referred to the organization of data into a

hierarchical structure containing relevant objects and their relationships (Sharman, 2007).

While in Artificial Intelligence, it is the development of ontology applications for knowledge

management, natural language processing, e-Commerce, and new emerging technologies

such as Semantic Web (OEG, 2015).

For the discussion, the author herein adapted the main areas involved in the methodology of

the MO framework together with the steps stated in ontology creation process. Fig. 5 below

are followed especially in building the terms and dictionary of JMC Music Score Ontology

(JMCMSO).

Fig 5 Ontology Creation Process

1. Determine the scope and domain of the music terms as provided by the user

needs;

As known in other frameworks as ontology specification, this part states the purpose of

defining the ontology, its intended uses and users. This is done by consulting the librarian and

staff of the UPCE Library together with music domain experts and referring to existing music

knowledge base of the Center. The goal is to understand the domain clearly and to capture the

concepts, criteria and the needed validation of the terms to be included in JMCMSO. Below

are guide questions from Noy and McGuinness (McGuinness, 2001).

● What is the domain that the ontology will cover?

● What are we going to use the ontology for?

● For what types of questions the information in the ontology should

provide answers?

● Who will use and maintain the ontology?

2. Identify existing namespaces and use them together with the Music Ontology

(extension of MO) to define the music scores of Jose Maceda compositions;

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Covering knowledge acquisition, this step performs elicitation and extraction of knowledge

gathered from the first step. This results in the identification of terms together with their

inferences and definitions. It forms the vocabulary of the ontology.

Furthermore, in this section, timeline and event ontology outlined in MO and other ontologies

such as FRBR, FOAF, and Dublin Core Metadata Elements are used to describe the main

concepts and relationships that the music scores of JMC have. In ontology creation, it is

recommended to consider existing ontologies and vocabularies that someone else has done

and to check if they can be refined and extended for a particular domain and task. Reusing

existing ontologies is a requirement for systems that interact with other applications that have

already committed to particular ontologies or controlled vocabularies. Many ontologies are

already available in electronic form and can be imported into an ontology-development

environment that is in progress.

3. Defining the JMC Music Score Ontology (JMCMSO)

To contextualize the domain knowledge captured in the previous steps, they are turned into a

conceptual model. Unstructured knowledge and information are organized in this section to

make meaningful Intermediate Representations (IR) (Gomez-Perez, 2001) that capture and

describe the terms that will make up the definitions of proposed JMCMSO. Some

recommended IRs are data dictionary, classification graphs, tables of classes, attributes,

instances, properties and constraints (Berners-Lee, 2009).

Once the conceptual model is available, it can now be transformed to a formal or semi-

computable model. This is also called the formalization stage of an ontology life cycle that

establishes the structural relations of the concept to the term. Based on that formalization

below are the activities to be executed.

1) Concept to term definition

2) Definition of the relationship

3) Definition of the properties

Further description is made to a concept that has been defined using another vocabulary or

ontology. This is the guiding rule of the Linked Data where the unified database is achieved

by integrating ontologies which constitutes the overall semantic of a concept.

4. Identify a tool and use it to create queries

In this step, structured and formal models are transformed into a computational language. It

includes integration of JMC music score data and mapping of other ontologies to the

computable set of reasoning and inferences. This also facilitates the creation of linked data

within the ontology.

For JMCMSO, Arc2 PHP Framework the semantic tool suite used that provides the web-

standard language for ontology, web application interfaces and endpoint. Consequently,

MySQL is the platform used for databasing. The Endpoint included in the Arc2 package,

serves as the query engine to execute the SPARQL queries for validation of established

terms, relationships, and properties. Resulting RDF data is accessed through the web API, a

simple interface provided for the searching mechanism of JMC. RDF Validator is also used

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to provide various IR’s of the data including RDF/XML, N3, Microdata, RDFa, Jason-LD,

and graph.

5. Presentation of JMC Music Score Ontology (JMCMSO)

Web API of JMCMSO is developed and deployed. A manifesto is formulated and written for

inclusion to LinkedData.org of namespace “ms” of the JMCMSO.

Findings

Following the methodology stated above, below are the findings per phase.

1. Determine the scope and domain of the music terms as provided by the user needs;

Vocabularies and concepts used in defining JMCMSO came from the following

sources:

a- Music Domain Experts

UPCE domain experts were Dr. Ramon Santos, the director of the Center, Ms. Grace Ann

Buenaventura, the Librarian, and a library staff. They are the participants in almost all

activities involving the definition of the concepts and terms of the proposed JMCMSO and

their validation and evaluation of the retrieved data set versus the competency questions.

b- Knowledge base of the UPCE current system

UPCE Library is using Excel spreadsheet in storing all the bibliographic information of its

collection. It is used in defining the concepts and terms of JMCMSO.

c- Existing music ontologies

MO framework together with other ontologies like FOAF, Dublin Core, and FRBR are used

by this study to describe the main concepts and relationships that the music scores of JMC

have.

Bibliographic description of the music scores of Jose Maceda Compositions is saved and

stored in a spreadsheet which is made available to the users to find the location of the actual

music scores. Control + F (find) is used to search the music scores with limited search fields

or access points to use. Below are the only search fields available for music scores to be

found and Fig. 6 below is an excerpt of the bibliographic description of music scores in

Excel.

1. Accession Number

2. Type

3. Researchers

4. Country

5. Year

6. Description

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Fig 6. Excerpt of the bibliographic description of music scores of JMC in Excel

To understand the domain of the JMC’s music scores, Fig. 7 below are the 50 questions

which are based on the general queries of the users of the library. They were used to capture

the concepts to be defined in JMCMSO and to build the criteria in the validation of these

concepts that address the purpose of creating the ontology.

Fig 7. 50 Competency Questions

Based on the above competency questions, some concepts were exclusively present to the

music scores of JMC, thus giving value to the collection and additional knowledge that may

be considered as the contribution of the collection to the Music Ontology’s growing list of

terminologies. Concepts reflect the traditional music of Filipino ethnocentricity and its

richness on the musicality and culture.

2. Identify existing namespaces and use them together with the Music Ontology

(extension of MO) to define the music scores of Jose Maceda compositions;

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Fig 8. Conceptual Frame of JMCMSO

Fig. 8 below is the conceptual frame of the JMCMSO built on top of MO. Below are the

classes and properties that were used to extend MO into a music score module. These classes

and properties formulated the concepts and terms by which JMCMSO is defined.

Research shows namespaces used by MO to define its specification were also the same

namespaces that could be used to define JMCMSO. Table 1 is the excerpt of the concepts

from existing namespaces that MO used for its levels of abstraction. These concepts were

found relevant to the bibliographic description of the music scores.

Table 1 Concepts used in MO Framework

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3. Defining the JMC Music Score Ontology (JMCMSO)

For the knowledge acquisition, different concepts, attributes, and relationships that JMCMSO

needed were modeled. Fig. 9 shows the class diagram of the JMCMSO, formulated based on

the FRBR. This activity was able to identify the entities and the relationships that were later

analyzed so that components, properties, and functionalities were determined. All entities,

properties, and relationships together with the competency questions were the primary basis

for the initial terminologies of the proposed JMCMSO vocabulary. The result of this activity

was used to formulate the glossary of terms of JMCMSO.

Fig 9. Class Diagram of JMCMSO

Below are the JMCMSO concepts and attributes in the glossary of terms.

Concepts identified from the requirements captured by the competency questions were

translated into a data dictionary and formed the first terms of JMCMSO. These classes and

properties were transformed into a triples notation - which is the basic tenets of RDF. These

triples are the Subject-Predicate-Object notation which corresponded to classes and

properties, and used these terms to represent the concepts. The Director together with the

Librarian and the library staff helped in formulating these terms. Each notation creates a

statement showing how these terms as a subject are related to other terms which are the

properties, and objects. Table 2 below is the excerpt of the data dictionary of JMCMSO for

classes while Table 3 shows the attributes as defined in property assertion table.

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The triples above were also visualized into a graph in this study. This showed the

hierarchical form of the terms to represent the classes and properties after they were classified

and arranged based on their meanings. Elements of data have precedence and were consisted

of resources related to other resources, with no single resource. This graph gave the concept-

classification hierarchy that builds the concept taxonomy of the JMCMSO terms. Fig. 10 is

an excerpt of the graph model of JMCMSO.

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Fig 10. Graph Model of JMCMSO

Table 4 below is one of the excerpts of namespaces used in JMCMSO

mo - namespace for Music Ontology

ms - namespace for the Music Score of JMCMSO

foaf - namespace for Friends Of A Friend Ontology

rdf - Resource Description Framework

4. Identify a tool and use it to create queries

The RDF concept uses RDF databases to implement the schema. In this study, RDF syntax is

used to execute the proposed JMCMSO. It is used to translate the concepts and attributes

specified in the conceptual model into OWL classes and properties. These classes and

properties are encoded in an RDF/XML document as well as the logical relationships

identified from the previous steps using a text editor and saved into RDF stores. RDF stores

are what are queried typically in the web API provided by the Endpoint of the Arc2 PHP

Framework. In comparison to SQL, implementation of RDF and Simple Protocol and RDF

Query Language (SPARQL) has a higher level of standardization especially in the use of

query languages.

N-triples format is known for storing and transmitting data in RDF. It’s a line-based notation

for parsing RDF/XML and was what this study used to generate the schema of the web

ontology. They were known formats used for applications to parse and generate the code

easily. The Subject-Predicate-Objects triples notation for the identified terms of the proposed

JMCMSO were now transformed to RDF/XML syntax of N-triples. This was the

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representation of the proposed ontology in a computational language. It included the

integration of JMC music score data and its mapping to other ontologies to a computable set

of reasoning and inferences. This also facilitated the creation of linked data within the

ontology. See Table 5 for the excerpts of the schema of the JMCMSO.

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Shown in Fig. 11, descriptions in XML were loaded and saved in the created store located at

http://smpascuacom.com/ms/MusicScore#. In the store “ms” which stands for “Music Score”

is the created namespace for the proposed ontology. Consequently, the XML was loaded to

RDF database provided by the framework and executed the ontology. To verify the validity

of the syntax if it’s containing correct OWL codes, an RDF Validator was used. See Fig. 12

& 13 for the W3C RDF Validator and its results.

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5. Presentation of JMC Music Score Ontology (JMCMSO)

Triple stores containing the RDF of the data set of JMCMSO were accessible in MySQL

under the PHPMyAdmin. Both local and web hosting used PHPMyAdmin as a tool manager

for databasing. Together with the PHP functions the JMCMSO web API is accessible through

http://smpascua.com/JMCMSO/.

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SPARQL Endpoint of Arc2 Framework served as query tool to derive RDF data from the

RDF triple store. Inference engine embedded in said Endpoint enabled retrieval of data from

the knowledge base. See Fig. 15 for the screenshot of the SPARQL Endpoint interface.

Implications

To publish music-related information, this study proposed the definition of an ontology for

the music domain, thus defining the JMC Music Score Ontology (JMCMSO). With the

combined theoretical framework of Music Ontology and Ontology Creation Process, the

study was able to define and use a web ontology engineering framework that guided the

JMCMSO ontology development process, formulated the JMCMSO specifications,

vocabulary and data dictionary and design a simple API for JMCMSO.

Moreover this study was able to show how it’s able to utilize the semantic tool to implement

the JMCMSO and to validate the RDF data by executing SPARQL query that provided not

only the ontological representation and visualization of the data set of the defined JMCMSO

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but also the taxonomy and organization of the collection which was the need of JMC in their

ongoing digitization and digitalization.

The contribution of this study in the music information research is that JMCMSO is a music

information ontology that helped define music-related information in the following main

aspects:

• Editorial - information about artists, releases, performances, recordings, musical

works;

• Musicological - information about the actual content of a musical item, such as a

harmonic progression or a segmentation in musically relevant segments;

• Workflows - know-how for deriving new information by combining music

processing tools with existing information.

As a final event, JMCMSO, as an ontology supports the representation of complex

knowledge of musical scoring of JMC with the help of the domain experts in Musicology and

Ethnomusicology. As a knowledge representation, it defined and described more effectively

the classification, organization and domain while linking the music scores of JMC to a bigger

definition and database of music information, Music Ontology. Researchers in the field of

music will be able to reuse these music scores domain of knowledge for other domains and

applications.

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