
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
55