4 STATE OF THE ART
The state of the art presented here features work done
in an effort to produce interoperable vocabularies for
the expression of Cultural Heritage data. It includes
W3C languages, semantic data models, thesaurus and
ontology resources, and ontology building
methodologies.
Opening cultural heritage on the web relies on
W3C standards such as OWL
12
(Web Ontology
Language), a Semantic Web language designed to
represent rich and complex knowledge about things,
groups of things, and relations between things,
SKOS
13
(Simple Knowledge Organization System), a
data model for sharing and linking knowledge
organization systems on the Web. SKOS can be used
to capture much of the semantics of existing thesaurus
of museums and other memory institutions thesauri.
Let us also quote DC (Dublin Core), a metadata
schema based on 15 essential properties to describe
online and physical resources
14
.
Semantic Data Models for the Cultural Heritage
domain have to be taken into account. In particular,
CIDOC-CRM, a meta-ontology for the representation
of concepts for the use of museum and cultural
heritage specialists (Cidoc, 2003). It provides a
semantic framework to building a mapping between
different cultural heritage resources reducing their
heterogeneity (Doerr, 2003). Our work not only aims
to build an ontology for museum publishing open
museum data, but also aims to build a multilingual
terminological knowledge base. From a
terminological point of view, we need to build a more
‘granular’ ontology for knowledge representation of
Chinese ceramic vases. Let us also quote EDM, the
common data model that was built in order to
harmonize data from different providers of
Europeana (Doerr et al., 2010). It is used for the
representation of concepts in the cultural heritage
domain. It is not a fixed schema that dictates the way
of representing data, but rather a conceptual
framework (or ontology) to which more specific
models can be attached, and interoperability between
them enhanced.
As far as ontological resources that the TAO CI
project can benefit, let us quote AAT (The Art &
Architecture Thesaurus), a structured resource that
can be used to improve access to information about
art, architecture, and other material culture through
rich metadata and links, hoping to provide (along with
other Getty vocabularies) a powerful conduit for
research and discovery in digital art history and
related disciplines
15
(Soergel, 1995). The AAT
comprises over 250,000 terms on architectural
history, styles, and techniques. Our ontology has been
linked with AAT in order to provide more
information for our terms in the ontology.
Kerameikos
16
is a “collaborative project dedicated to
defining the intellectual concepts of pottery following
the tenets of linked open data and the formulation of
an ontology for representing and sharing ceramic data
across disparate data systems.” (Gruber & Smith,
2014). Let us also quote Ontoceramic, which is an
OWL ontology for ceramics classification (Cantone
et al., 2015). Lekythos
17
is an another project that
aims at representing concepts in the domain of
ancient Greek pottery, but having natural language
terms in the domain as its starting point.
According to (Ushold, 1998), “An [explicit]
ontology may take a variety of forms, but necessarily
it will include a vocabulary of terms and some
specification of their meaning (i.e., definitions).” For
domain experts, identifying and defining concepts in
ontology also presents a challenge for which ontology
building methodology can bring useful aids.
Ontology building methods can be based on objective
criteria, e.g., clarity, coherence, extensibility, etc.
(Gruber, 1995), software engineering methods
(Fernández-López, 1999), text-based construction
(Zouaq & Nkambou, 2009), modular design approach
(Özacar et al., 2011), ontological engineering
(Suárez-Figueroa et al., 2012), unsupervised domain
ontology learning method (Venu et al., 2016) , based
on Formal Concept Analysis (Nong et al., 2019), etc.
Let us quote some methodologies focusing on the
stages which compose them. METHONTOLOGY
(Fernández-López et al., 1997) includes seven
stages: specification, knowledge acquisition,
conceptualization, integration, implementation,
evaluation, and documentation. On-To-Knowledge
Methodology (Sure et al., 2004) includes the
following phases: feasibility study, kick-off,
refinement, evaluation, and application & evolution.
NeOn methodology (Suárez-Figueroa et al., 2015)
provides nine scenarios for developing ontologies.
12
https://www.w3.org/TR/2004/REC-owl-features-20040210/
13
https://www.w3.org/TR/skos-reference/#notes
14
https://dublincore.org/schemas/ Schemas are machine-
processable specifications that define the structure and syntax
of metadata specifications in a formal schema language
.
15
https://www.getty.edu/research/tools/vocabularies/aat/
about.html
16
http://kerameikos.org/
17
http://o4dh.com/lekythos
An Ontology of Chinese Ceramic Vases
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