11institutetext: Department of Cultural Heritage, University of Bologna, Via
Degli Ariani, 1, 48121, Ravenna, RA, Italy 22institutetext: Department of
Classical Philology and Italian Studies, University of Bologna, Via Zamboni,
32, 40126, Bologna, BO, Italy

# Developing Application Profiles for Enhancing Data and Workflows in Cultural
Heritage Digitisation Processes

Sebastian Barzaghi 11 0000-0002-0799-1527 Ivan Heibi 22 0000-0001-5366-5194
Arianna Moretti 22 0000-0001-5486-7070 Silvio Peroni 22 0000-0003-0530-4305

###### Abstract

As a result of the proliferation of 3D digitisation in the context of cultural
heritage projects, digital assets and digitisation processes – being
considered as proper research objects – must prioritise adherence to FAIR
principles. Existing standards and ontologies, such as CIDOC CRM, play a
crucial role in this regard, but they are often over-engineered for the need
of a particular application context, thus making their understanding and
adoption difficult. Application profiles of a given standard – defined as sets
of ontological entities drawn from one or more semantic artefacts for a
particular context or application – are usually proposed as tools for
promoting interoperability and reuse while being tied entirely to the
particular application context they refer to. In this paper, we present an
adaptation and application of an ontology development methodology, i.e. SAMOD,
to guide the creation of robust, semantically sound application profiles of
large standard models. Using an existing pilot study we have developed in a
project dedicated to leveraging virtual technologies to preserve and valorise
cultural heritage, we introduce an application profile named CHAD-AP, that we
have developed following our customised version of SAMOD. We reflect on the
use of SAMOD and similar ontology development methodologies for this purpose,
highlighting its strengths and current limitations, future developments, and
possible adoption in other similar projects.

###### Keywords:

OWL ontology development 3D digitisation cultural heritage FAIR principles
application profiles

##  1 Introduction

In recent years, there has been a growing recognition of the importance of 3D
digitisation within the cultural heritage field and its impact on society [4].
This is primarily attributed to the benefits this technology introduced for
cultural heritage preservation, reconstruction, documentation, research, and
valorisation [10]. In addition, digitisation offers contexts and opportunities
to renew the discourse on the alignment, integration, and reuse of data and
processes in the cultural heritage field, both from a methodological
perspective and from a technical point of view [37].

However, digitisation poses several challenges as methods and technologies
continue to develop and the amount of 3D data grows exponentially. These
challenges include a severe lack of documentation on cultural heritage data,
its digitisation processes, data clutter, lack of standardisation and
interoperability, reproducibility issues, and an overall complex
implementation into functional, usable systems [62]. Therefore, prioritising
the adoption and implementation of FAIR principles to digital objects and
digitisation processes is also becoming significantly more critical [55].

Whenever possible, these cultural heritage data must be described according to
standard specifications and shared metadata schemas [61]. In particular, both
the harmonisation of digitisation data based on established models and the
development of efficient methods and standardised practices for data
documentation promote interoperability with external systems and encourage
their reuse [38]. For this reason, institutions such as galleries, libraries,
archives, and museums (GLAM) have developed _semantic artefacts_ (crosswalks,
ontologies, etc.) [15] designed to connect with external resources and
streamline interoperability and reuse across different datasets [58].

In the context of cultural heritage, one of the most known, shared, and used
semantic artefacts is the CIDOC Conceptual Reference Model (CIDOC
CRM)111http://www.cidoc-crm.org/cidoc-crm/ [20]. It is an international ISO
standard that provides an ontology related to cultural heritage and museum
documentation, which can be extended with additional modules for covering
particular descriptions at hand. It has been developed to cover a massive
spectrum of the domain of cultural heritage information. However, due to its
complexity and dimension, it may be challenging to adopt and manage in a
project dealing with cultural heritage information. Indeed, on the one hand,
the application contexts where CIDOC CRM is adopted usually need to use just a
subset of the classes and properties it defines. On the other hand, sometimes
we also need additional entities – from one or more of its extensions – to
describe specific contextual information at hand. Thus, in real projects
related to cultural heritage information, specific _application profiles_ of
CIDOC CRM are proposed to reduce the cognitive effort in understanding and
using it.

Application profiles are explicitly designed around data sharing and reuse
[66] since they foster the adoption of (part of) existing models already
developed by the community for describing a specific context, usually after
years of work and considering different nuances an application context may
have. They are constructed either by selecting or extending elements from one
or more existing semantic artefacts, combining them, or optimising their use
for a particular context or application [29] without having to start from
scratch [2].

Usually, application profiles adopt a conservative approach to modelling,
using only those elements that are necessary to avoid over-engineering and
possible logical friction that may arise between different models used
together. Therefore, application profile developers should be careful not to
overextend their schemas [66] and try to reuse them as much as possible
without adding new elements. This approach maximizes their potential as
practical tools for promoting interoperability between models and for
harmonising data description practices, thereby improving their adherence to
the FAIR principles [64].

Of course, developing an application profile for a given model is not
straightforward. Careful investigation and consideration are needed to
identify which parts of the source model apply to the working scenario and how
to select them appropriately. In addition, documentation accompanied by
several examples on how to structure and query data that is compliant with and
appropriately developed for the application profile should also be provided,
necessary to enable a final user to understand how to interact with such data.

As part of a line of research of a project related to describing cultural
heritage information using Semantic Web technologies, we have experimented
with ontology development methodologies to guide the development of
application profiles for a given context. This article describes our
experience in using and adapting the _Simplified Agile Methodology for
Ontology Development_ (SAMOD) [49], an agile ontology development methodology
originally devised for developing new ontologies and adopted by the community
of practice for ontology development, to define an application profile of
CIDOC CRM. The context of the application of this activity lies in a study we
are involved in that concerns the digitisation of cultural heritage objects
organised within specific physical and/or temporal environments (e.g. an art
collection, a museum room, an exhibition). In particular, we focus on a case
study concerning the creation of a FAIR-compliant _digital twin_ of a
temporary exhibition (ended in May 2023) entirely dedicated to the figure of
Ulisse Aldrovandi (1522-1605), naturalist, botanist, and one of the fathers of
natural history studies.

The rest of the paper is structured as follows. In Section 2, we introduce the
most relevant related works on application profiles and development
methodology. In Section 3, we present the setting of our work and, in
particular, the application profile developed using SAMOD. In Section 4, we
discuss some aspects of this development, and, in Section 5, we introduce some
of the main lessons we learnt during the process. Finally, in Section 6, we
conclude the paper by sketching out some future works.

##  2 Related works

Challenges related to cultural heritage digitisation include standardisation,
heterogeneous data, and interoperability issues [62]. A practical approach to
tackle these challenges employs metadata schemas that establish a shared
vocabulary for describing information within the domain of discourse [36]. In
most projects, the development of these schemas can be done either from
scratch or by reusing existing models [53], depending on the requirements of
the project [11].

If there are no existing metadata schemas that can cover the project
requirements, it may be necessary to develop a new model with specific classes
and properties, often by taking inspiration from existing models or even
directly extending them in a way that heavily modifies their original scope,
up to developing a new model altogether. For example, Homburg et al. [31]
discuss the development of an ontology based on the PROV Ontology (PROV-O)
[35] for describing 3D cultural heritage objects capturing and processing
workflow with detailed metadata documentation. However, model reuse is
typically encouraged when possible to foster semantic interoperability and
data integration, either directly (by reusing existing standards as they are),
indirectly (by developing new models and then aligning them to existing
standards), or through hybrid approaches [11]. In the field of cultural
heritage, the models that are reused the most (either directly or indirectly)
for documenting cultural heritage objects include the CIDOC CRM and the
Europeana Data Model (EDM) [33].

CIDOC CRM and EDM aim to harmonise how cultural institutions organise their
data by aligning them to a wider-arching standard. For example, reflecting on
the challenges and approaches in building digital monitoring for cultural
heritage, Messaoudi et al. [40] present the use of CIDOC CRM, the CRM Digital
extension (CRMdig) [23] and the CRM Scientific Observation model (CRMsci) [22]
to correlate cultural heritage data taken from direct acquisitions for a
comprehensive understanding of historical building conservation states.
Another project, named 3D-ICONS, produced a metadata schema for 3D content
documentation and alignment with the Europeana infrastructure [18], relying on
a previous iteration based on EDM and further enhanced with modelling
capabilities to describe 3D digitisation processes by reusing CRMdig. Other
works, such as [13, 12, 2, 45, 30], have provided particular data models and
application profiles based on CIDOC CRM and CRMdig to address specific
descriptive requirements.

To facilitate reuse and integration, these standards provide valuable
mechanisms for integrating values from external vocabularies. This allows
using very broad classifications such as those made by CIDOC CRM (like the
concept _Man-Made Object_ and _Information Object_), while still allowing for
a more precise characterisation of these entities using types defined in
external vocabularies [44]. However, the diversity and uniqueness of cultural
objects and their digital counterparts make it challenging to establish all-
encompassing standards for describing them. Although many schemas and
application profiles have been developed to describe numerous and nuanced
scenarios in the cultural heritage domain, their application varies
significantly between institutions. Therefore, some measures must be taken to
ensure data integration, according to recommendations outlined in protocols or
policies by communities or institutions [60].

While the precise techniques may vary across specifications, the process for
developing an application profile typically involves selecting a collection of
metadata elements from one or more schemas, potentially expanding the
specification’s base element vocabulary with locally defined elements, and
deciding on a set of helpful value vocabularies to pair with these elements
[46]. Indeed, over the past two decades, efforts have been made to compile a
more structured methodology for application profile development. In 2007, the
Singapore Framework for Dublin Core Application Profiles was presented as a
framework for designing metadata schemas based on Dublin Core. Wu et al. [70]
describe a similar, relatively linear development process, which entails
analysing current metadata standards to identify potentially reusable terms,
developing a draft set of elements to be discussed with stakeholders, honing
the initial draft set in response to feedback and discussion, and developing
and documenting the profile. Curado Malta & Baptista [17] further expand on
this framework by providing additional room for scope and stakeholder
definition, activity validation, and the iterative creation of deliverables
that document the processes. Similarly, Miksa et al. [42] illustrate the
methodology used to define an application profile for describing machine-
actionable data management plans, focusing on the iterative production of
development deliverables (requirements, list of existing models, and
prototypes) and their evaluation through stakeholders’ feedback.

Since developing a metadata schema is very similar to developing an ontology
[32], adopting ontology development methodologies can bring significant hints
to the cause. More generally, the creation of application profiles can benefit
from the well-established processes already in place for the creation of
semantic models [16], especially considering that reuse and integration have
now become significant steps in many ontology development processes [67]. As a
matter of fact, some existing ontology development methodologies that consider
how to perform integration and reuse have already been discussed thoroughly
[3, 59, 1, 72]. In particular, highly-structured and sequential methodologies
such as METHONTOLOGY [25], Pinto & Martins’ methodology [52], Ontology
Development 101 [47], NeOn [63], and OntoDI [72] can be valuable tools for
procedurally developing application profiles. For example, Minamiyama et al.
[43] describe the workflow for developing an application profile by using
Ontology Development 101 [47] as a development process for selecting and
combining existing terms from different ontologies.

Alongside these more traditional and sequentially-oriented approaches, agile
methodologies such as the Simplified Agile Methodology for Ontology
Development (SAMOD) [49], eXtreme Design [8], and GO-FOR [56] have emerged as
well, offering quick and iterative processes that focus on the importance of
incorporating changes and leveraging existing knowledge as much as possible to
foster ontology integration and reuse.

##  3 Pilot study

The work presented in this paper is set in the context of the Project CHANGES
(”Cultural Heritage Active Innovation For Next-Gen Sustainable
Society”)222https://sites.google.com/uniroma1.it/changes/. CHANGES is an EU-
funded project that started in December 2022 and involves 11 universities, 4
research institutions, 3 schools for advanced studies, 6 companies, and 1
centre of excellence. Following the European guidelines on Cultural Heritage
(CH), the project aims at increasing, at the Italian level, the curation and
management of cultural heritage artefacts in all forms, expanding the
involvement of the general public, making more sustainable the exhibition
potential, and including crucial social functions (accessibility,
inclusiveness, critical thinking, participation, enjoyment, sustainability)
into the cultural heritage environment. One of the lines of research (called
Spokes) of CHANGES is dedicated to the use of digital and virtual technologies
for the preservation, exploitation, and enhancement of CH objects in museums
and art collections. In particular, one of the goals of this Spoke is to
devise a workflow of approaches and methods for the acquisition, processing,
optimisation, metadata inclusion, and online publication of 3D assets
representing objects in CH environments, to be applied in the context of nine
”core” case studies that involve several cultural institutions as
representative of the different museum contexts in Italy. During the past
year, we have worked on a pilot study, introduced in previous works [5, 6], to
identify the guidelines for implementing such workflow. This pilot study
consists of digitising a temporary museum exhibition centred on the scholarly
and scientific collection of the Renaissance Italian naturalist and botanist
Ulisse Aldrovandi. This exhibition included more than 200 objects of various
types (books, maps, specimens, scientific tools, etc.) documented in an
extensive metadata catalogue. In addition, we have also kept track of the
activities carried out during the whole digitisation process.

One of the main goals to address within the pilot study – and, more broadly,
within all the research done in the context of the Spoke – is to create a data
model compliant with the Semantic Web technologies to use for metadata and
provenance description and, thus, enable all the research objects produced in
the acquisition and digitisation workflow, i.e. the _digital twins_ of all the
CH objects in the temporary exhibition, to be compliant with FAIR principles.
However, as anticipated in Section 1, one of the requirements for the data
model was to reuse existing standards in the CH domain that guided us on
adopting CIDOC CRM as a starting point, considering its pervasiveness in CH
research. Thus, we focused on the definition of an application profile of such
a standard, detailed in the following subsections.

###  3.1 Development with SAMOD

We have decided to adopt SAMOD [49] as the methodology for creating the
application profile necessary to describe the information that comes from the
acquisition and digitisation process of Aldrovandi’s temporary exhibition.
SAMOD is an agile ontology development methodology published in 2016, which
has gained extensive popularity in the community in the past eight years, as
quantitatively measured, for instance, in Google Scholar with a total of 120
citations (as of 14 April 2024). Indeed, it has been used extensively in our
research group for developing several ontologies, including the _Semantic
Publishing and Referencing Ontologies_ [51], the _United Nation Document
Ontology_ [50], and the _Medieval Manuscript Ontology_ [7]. Also, it has been
adopted by other research groups for developing several ontologies, such as
the _Scientific Events Ontology_ [24], the _ITDT Ontology_ [41], and the
_Business Event Exchange Ontology_ [14].

As illustrated in Figure 1, the process introduced by SAMOD consists of three
main steps within a continuous iteration cycle:

  1. 1.

collect requirements (in the form of motivating scenarios, competency
questions, and glossary of terms) of a small situation and develop a _modelet_
, a self-contained ontological model that formalises the requirements. The
documented requirements, the modelet, and the supplementary resources
prepared, such as competency questions, diagrams, data, and query examples,
are then packed into a test case that will be checked against formal and
rhetorical requirements333For more details about all the passages of the
workflow, please see [49].;

  2. 2.

_merge_ the modelet with the model underdevelopment obtained as a result from
the previous iterations, if any, and then recheck the test case;

  3. 3.

_refactor_ the overall model obtained, including the diagrams, data, and query
examples created before, and finally recheck the test case. In this step, it
is recommended that concepts and relations defined in other relevant models be
reused by either including external entities or providing
alignment/harmonisation to them to maximise interoperability.

Figure 1: A visual summary of SAMOD, an agile ontology development methodology
based on three steps.

We have changed the last step of SAMOD to adapt the methodology to be used
also for creating application profiles. Indeed, as anticipated, the _refactor
step_ aims at maximising the reuse of existing ontologies, but it provides
total freedom in choosing which external ontologies to use. Instead, in our
work, we have forced the last step of the methodology to use explicitly only a
set of predetermined ontologies, identified in advance as appropriate to
address the descriptive needs of our scenario to model. In particular, after
an extensive analysis, we have identified CIDOC CRM and its FRBR-based
extension (FRBRoo, version 2.4)444http://iflastandards.info/ns/fr/frbr/frbroo/
[21] as primary models for describing the CH objects included in the temporary
exhibition, and an extension of CIDOC CRM, i.e. CRMdig [23], and the Getty’s
Art & Architecture Thesaurus (AAT) [28] for describing the acquisition and
digitisation process we performed. The intended goal was to create a full
application profile reusing only selected terms from existing standard models,
minimising the possible redundancies in the original models and, thus,
adopting them as intended from the requirements collected during the execution
of SAMOD, without introducing any new vocabulary or terms.

###  3.2 The final application profile

Overall, we have executed 8 iterations of the SAMOD methodology, detailed at
https://github.com/dharc-org/chad-ap/. The application profile, named
_Cultural Heritage Acquisition and Digitisation - Application Profile_ (CHAD-
AP, https://w3id.org/dharc/ontology/chad-ap), is implemented as an OWL
ontology and can be logically split into two separate abstract modules: the
_Object Module_ (OM), dedicated to describing the CH objects, and the _Process
Module_ (PM), for describing the acquisition and digitisation process. CHAD-AP
reuses 25 classes (out of 145), 28 object properties (out of 247, not counting
inverse properties), 5 data properties (out of 26), and 81 individuals (out of
56670) from the high-level standards considered, thus drastically reducing the
number of ontological entities a user should know for understanding the data
of the application context in consideration.

####  3.2.1 Object Module

As shown in Figure 2, a CH object is described in OM according to the
_Functional Requirements for Bibliographic Records_ (FRBR) data model [65],
which uses several descriptive layers for its representation. In particular,
the _Work_ (frbroo:F1_Work) represents the _essence_ or conceptualisation of
the CH object. It begins with a conception event (frbroo:F27_Work_Conception)
occurring within a specific time span (crm:E52_Time-Span), which can be
expressed as either a precisely defined period with exact starting and ending
date times (crm:P82a_begin_of_the_begin and crm:P82b_end_of_the_end) or a
fuzzy label if its extent is not known precisely
(crm:P82_at_some_time_within). Each work is associated with a series of titles
(crm:E35_Title), each classified according to a particular type
(crm:E55_Type), which can be an _original title_ (aat:300417204) or an
_exhibition title_ aat:300417207. Furthermore, a Work can be part of a larger
Work (frbroo:F15_Complex_Work), like a series of printed volumes, which is
classified under a particular type (crm:E55_Type).

Figure 2: A diagram of the CHAD-AP Object Module (OM).

The _Expression_ (frbroo:F2_Expression) is the realisation of a Work, and
refers to the intellectual _content_ of the object. It is generated through a
creation event (frbroo:F28_Expression_Creation) that consists of a series of
smaller activities (crm:E7_Activity), each conducted by one or more agents
(crm:E39_Actor) and characterised by a specific type (crm:E55_Type) that
defines, implicitly, the role assumed by the agent for that activity. For
example, if the agent is identified as the author of the Expression, the
activity type is represented as _writing_ (aat:300054698). Also, creation
events employ various creation techniques (crm:E55_Type). For example,
aat:300054196 is used to express _drawing technique_. An Expression can also
be associated with one or more subjects defining its contents. In CHAD-AP, a
generic _concept_ is represented with the class crm:E73_Information_Object
with the type aat:300404126 (i.e. _subject_) explicitly specified.

Finally, the _Manifestation Singleton_(frbroo:F4_Manifestation_Singleton)
represents the physical instance of the CH object. It is accompanied with
descriptive components like labels (expressed with the property
crm:P3_has_note), types (crm:E55_Type), and identifiers (crm:E42_Identifier).
It may be linked to a curation activity (crm:E87_Curation_Activity) carried
out by a keeper (crm:E39_Actor) who manages a collection
(crm:E78_Curated_Holding) the object belongs to, and that is located in a
specific place (crm:E53_Place). Manifestations can consist of other
Manifestations and may depict the Expression of another related object, such
as a video displaying a manuscript or a tablet showing a picture of a
specimen. In addition, Manifestations are associated with copyright or
licensing statements (represented through the combination of
crm:E73_Information_Object having type aat:300435434, i.e.
_copyright/licensing statement_), linked with the document introducing the
actual license or right statements through the property
crm:P70i_is_documented_in. Whenever possible, instances of crm:E39_Actor and
crm:E53_Place are also linked with existing authority records with the
property crm:P48_has_preferred_identifier.

####  3.2.2 Process Module

Figure 3 introduces the entities for defining a 3D digitisation workflow as a
sequence of activities classified in two main categories. On the one hand, we
have the acquisition activity (crmdig:D2_Digitization_Process) involving the
digitisation of a CH object (crm:E24_Physical_Human-Made_Thing) to produce its
digital equivalent (crmdig:D9_Data_Object). Similarly to its physical
counterpart, the digital CH object can be associated with copyright statements
or licenses (crm:E73_Information_Object with aat:300435434 as its type). The
acquisition occurs within a time span (crm:E52_Time-Span) with defined
starting and ending date times, and engages various agents, including
individuals (crm:E21_Person) and institutions (crm:E74_Group) responsible for
the activity. During the acquisition, a series of techniques (crm:E55_Type)
can be used, such as _photogrammetry_ (aat:300053580) or _structured light
scanning_ (aat:300391312), along with tools (crmdig:D8_Digital_Device) like
_digital cameras_ (aat:300266792) and _structured light scanners_
(aat:300429747).

Figure 3: A diagram of the CHAD-AP Process Module (PM).

On the other hand, we have software activities, each representing a specific
stage of digitisation workflow (crmdig:D10_Software_Execution). Such stage is
denoted by its type (crm:E55_Type), such as _processing_ (aat:300054636),
_modelling_ (aat:300391447), and _optimisation_ (aat:300386427). It involves
the manipulation of the digital CH object (crmdig:D9_Data_Object) produced
previously as input and the production of a new version of that digital CH
object (crmdig:D9_Data_Object) as output. The activity also occurs within a
defined time span (crm:E52_Time-Span) with precise start and end date times,
engages various agents (crm:E21_Person for people and crm:E74_Group for
institutions), and uses software to produce an output (crmdig:D14_Software).

####  3.2.3 Additional material

All the material produced in SAMOD, particularly the exemplar dataset created
by translating the examples in all the motivating scenarios in RDF, has also
been used to prepare additional tools to support the creation of data that is
compliant with CHAD-AP. Indeed, during the acquisition and digitisation
workflow, we also created two tabular datasets: one for storing bibliographic
data of the CH objects included in the temporary exhibition, and another for
data concerning the acquisition and digitisation process. Both datasets are
represented by the two abstract models OD and PD presented in the previous
sections. The table structure was conceptualised compliantly with CHAD-AP,
defining column names, expected cell data, and controlled values for specific
columns (e.g. object type). To facilitate the process of data entry in terms
of technical skills, timing, and collaborative needs, both tables were hosted
on Google Spreadsheet. The two tables were populated in parallel, paying great
attention to consistency.

Starting from these two tabular datasets, we have exported them in CSV format,
and we have used them and all the material developed during the application of
SAMOD to prepare two distinct RDF Mapping Language (RML) [19] documents that
implement the conversion from CSV into RDF. In detail, in a preliminary
analysis of the input CSV data, any critical issues in direct conversion are
identified. In particular, we have extended RML conversion rules with
additional Java functions to handle specific conversion needs, e.g. when a
cell of the input table describes more than one entity. We use MATEY [68] to
facilitate the creation of mapping rules as a human-readable YARRRML mapping
file to be converted into RML. Java555https://github.com/RMLio/rmlmapper-
java.git or Python666https://github.com/anuzzolese/pyrml.git software are used
to produce the output leveraging the mapping document. The production of RDF
data from CSV raw data is an essential component of the process, as it allows
limitations to be found in CHAD-AP’s descriptive capabilities with respect to
a real dataset, potentially missed by the examples generated during the
application profile production phase.

##  4 Discussion

The success of the use and adoption of ontologies, application profiles, and
other semantic artefacts often depends on their perceived quality [39]. While
we can use specific tools – such as OntoClean [27] and OOPS! [54] – for
measuring, to some extent, some qualitative dimensions, the adoption of
ontology development methodologies is one of the most appropriate ways to
guarantee and attest such quality. Indeed, the development of such
methodologies was introduced, as it happened in the software domain, to
engineer properly ontological constructs according to a defined application
scenario.

SAMOD is no exception. It was developed taking inspiration from ontology unit
testing [69] and uses test cases [9] as an essential tool to ensure the
soundness and quality of the ontology produced with it. In particular, SAMOD
relies on Test-Driven Development applied to ontology engineering [34] to
systematically evaluate both the modelet produced during the first step of the
iteration and the actual model finalised after the iteration, according to
different dimensions concerning the model, the data that are produced
following that model, and the formal (i.e. SPARQL) queries proposed for
obtaining answers to specific competency questions. The experimentation we
introduced in this paper was to use SAMOD to produce application profiles from
one or more existing models available for a particular domain of interest. As
anticipated in the previous section, SAMOD is a well-known and used
methodology for ontology development. However, as far as we know, this is one
of the first efforts to reuse and adapt it to create application profiles from
large data models. Although, currently, direct evidence of reuse beyond our
internal community remains limited, our results suggest substantial potential
for broader adoption. Indeed, numerous national and European projects centred
around the digitisation of cultural heritage share our objective of enhancing
its reuse and interoperability.

In the context of Project CHANGES, other Spokes we are collaborating with have
been contacted to measure how they can take advantage of our approach for
creating specific application profiles or even reusing CHAD-AP, such as: Spoke
3 – dedicated to digital libraries, archives and philology – for describing
the digitisation of bibliographic and linguistic heritage; Spoke 6 – dedicated
to history, conservation and restoration of cultural Heritage – for the
creation of digital models to support the analysis, intervention, monitoring
and enhancement processes in relation to cultural heritage at risk; and Spoke
8 – dedicated to sustainability and resilience of tangible cultural Heritage –
for improving scientific procedures for tangible CH data capturing at large,
including documentation, dating, classification, recording, and measurements.
In addition to these internal collaborations, we are also involved with
several networks dedicated to CH and the Humanities (such as _OPERAS_
777https://operas-eu.org/) and discussing with other national projects
dedicated to the topic, such as _Humanities and cultural Heritage Italian Open
Science Cloud_ (H2IOSC)888https://www.h2iosc.cnr.it/. H2IOSC wants to adopt
CIDOC CRM as well for describing metadata of CH research data, and we started
a discussion on whether they may be interested in reusing CHAD-AP and,
eventually, extending it with additional concepts using SAMOD as a methodology
to develop a robust application profile for their case correctly.

##  5 Lessons learnt

In developing CHAD-AP with SAMOD, we have learned some lessons that may be
useful for future development. In this section, we introduce some of the main
ones that may guide the use of such ontology development methodologies for
application profiles.

Lesson 1: foster data integration and reusability. While our focus has
primarily centred on creating a digital twin of a temporary exhibition and
making its data FAIR-compliant, we think the adaptation of SAMOD for producing
application profiles can also be adopted in other case studies and domains of
application. The commitment to considering existing standards for a particular
community in the application of SAMOD, such as CIDOC CRM and related
extensions for the CH domain, enables the production of data tied up on
specific requirements mapped in the application profile that are inherently
interoperable with others developed externally by following the same high-
level and comprehensive standard, thus facilitating data integration and
reusability. The approach proposed in SAMOD seems to be implementable in other
ontology development methodologies that include, as a minimal step, re-
engineering and/or alignment with an existing data model. An example of these
methodologies is eXtreme Design [8], which includes a step dedicated to the
integration and fixing of the final into a broader context.

Lesson 2: scalability is key. Like other agile development methodologies,
SAMOD is based on rapid, iterative and continuous processes that lead to the
gradual construction of an ontology or application profile passing through a
series of smaller models, one for each iteration of the methodology, grafted
into a coherent formal logical system. When developing an application profile
for a given standard, requirements may be added in due time according to
different needs. On the one hand, the type of data to describe in a specific
application context may increase over time and, thus, they would require
appropriate extensions of the application profile to address such additional
needs. On the other hand, the high-level and comprehensive standard used in
the application profile can evolve as well, affecting the application profile
which may need modifications to be compliant. Only ontology development
methodologies that scale well and address these scenarios should be considered
for the creation and further extension/adaptation of application profiles. The
impression is that the more a methodology is organised on small iterative
steps, the better it allows a careful managing of such extensions and/or
modifications. Indeed, SAMOD favours the use of a limited number of
ontological elements per iteration to make the development process manageable.
These characteristics make SAMOD and similar methodologies suitable for the
systematic, timely and domain-agnostic development and modification of
application profiles, regardless of the models intended for reuse.

Lesson 3: relax axiomatisation in application profiles. The implementation of
an application profile as an OWL ontology can be performed by formally
importing all the standards one needs – in our pilot study, CIDOC CRM, CRMDig
and AAT – via owl:imports statements, and then documenting which parts of
these standards should be used and how to comply with the needs elicited in
the application profile. While this approach brings a solid logical foundation
to the application profile, which guarantees that its formal consistency can
be checked via automatic tools such as reasoners, it also results in a
substantial cognitive effort by the users interested in adopting the
application profile who need to understand which ontological entities of the
imported models are relevant for their goals and which ones, instead, are
there simply as a consequence of formal imports and add no value to the
application profile itself.

Another approach we adopted in our work is to sacrifice the formal
completeness guaranteed by the import statements and reintroduce just the
relevant ontological terms of the high-level standards and the minimal amount
of related axioms (subsumptions, property domain/range declarations, etc.),
when appropriate in the application profile, providing provenance information
about where such terms have been taken from via rdfs:isDefinedBy annotations.
This approach grants us the appropriate level of focus on the application
profile. Also, it enables us to enrich the textual definition of each
ontological entity (for instance, via rdfs:label, rdfs:comment and
dc:description) by providing a textual and/or graphical description that
concerns the use of the entity in the considered application context, instead
of having a broad and high-level description of it fitting a plethora of
different scenarios. It is worth noticing that while performing this selection
we must not change the semantics defined in the original high-level standards,
e.g. changing the subsumption hierarchy and restricting/changing the domain
and range of properties, since the logical consistency of the application
profile with the original standards must be preserved.

Lesson 4: bring in needed formal axiom when testing. While it is crucial to
reduce users’ cognitive effort by removing unnecessary semantics from the
application profile, the fully-fledged logical entailment defined in the high-
level standards is necessary for carefully evaluating whether the
compositional structure of the application profile does not introduce any
inconsistency. Thus, since SAMOD relies on several testing phases during each
iteration, we decided to force imports of the standards while running SAMOD
just to enable a formal checking of the application profile against the
definition included in the standards and then remove such imports from the
final ontology released at the end of every iteration. However, as a
consequence of this work, we have already started to experiment with
alternative approaches for implementing semantically sound tests without the
necessary use of such formal imports. For instance, we have considered
enhancing some of the tests on the exemplar data produced in SAMOD iterations
using Shapes Constraint Language (SHACL) [48], beneficial to check the
integrity and quality of the underlying structure, to some extent, of the
model and the exemplar data.

##  6 Conclusions

This paper presented our work on adapting and applying an ontology development
methodology, i.e. SAMOD, to create application profiles of large standard
models. Using an existing pilot study we have developed in the context of a
national project dedicated to the use of digital and virtual technologies to
preserve and valorise cultural heritage, we have introduced CHAD-AP, an
application profile encoded in OWL. We built CHAD-AP by following our
customised version of SAMOD to represent, in machine-actionable format, the
information related to cultural heritage objects and the processes concerning
their acquisition and digitisation. We have reflected on the strengths of our
approach, focusing on possible environments for reuse and the lessons we
learnt from carrying out our research. SAMOD has proven to be an excellent
methodology for creating highly interoperable and reusable application
profiles.

In the future, we aim to push the adoption of SAMOD to define application
profiles in other relevant and related projects dedicated to cultural
heritage. Concerning CHAD-AP, we are currently also investigating possible
further developments to improve it by covering in-depth information on the
digitisation process that some software (e.g. Metashape [71]) makes available
in semi-structured texts to describe technical aspects such as configuration
information, calibration specifics, geometric registration parameters, and
processing parameters that are employed throughout the entire process. Also,
the CIDOC CRM working group has recently released a stable updated version of
FRBRoo, known as _LRMoo_ [57]. Much like FRBRoo did with FRBR, LRMoo
integrates the _IFLA Library Reference Model_ (LRM) into CIDOC CRM, serving as
its extension for describing bibliographic information in the cultural
heritage domain. Since such a change is not fully back-compatible with the
previous version of the standard, we plan to carry out a new iteration of
SAMOD to modify CHAD-AP to comply with this change.

##### Supplemental Material Statement:

The full documentation of the application profile development process is
available from GitHub at https://github.com/dharc-org/chad-ap/. The model
itself is documented at https://w3id.org/dharc/ontology/chad-ap. Due to the
project still being underway, the raw datasets are currently unavailable, but
they will be published on Zenodo, as per the Data Management Plan of the Spoke
4 (Dataset number 28) [26].

#### Acknowledgments

This work was funded by Project PE 0000020 CHANGES - CUP B53C22003780006, NRP
Mission 4 Component 2 Investment 1.3, Funded by the European Union -
NextGenerationEU.

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