An Ontology for Modeling Cultural Heritage Knowledge in Urban Tourism

Authors Alexander Pinto Irvin Dongo Regina Ticona-Herrera Yudith Cardinale

Plaintext

Received May 10, 2022, accepted May 30, 2022, date of publication June 2, 2022, date of current version June 15, 2022.
Digital Object Identifier 10.1109/ACCESS.2022.3179664

An Ontology for Modeling Cultural Heritage
Knowledge in Urban Tourism
ALEXANDER PINTO 1 , YUDITH CARDINALE 2,3 , IRVIN DONGO 2,4 ,

AND REGINA TICONA-HERRERA1,4
1 Computer Science Department, Universidad Católica San Pablo, Arequipa 04001, Peru
2 Electrical
and Electronics Engineering Department, Universidad Católica San Pablo, Arequipa 04001, Peru
3 Escuela Superior de Ingeniería, Ciencia y Tecnología, Universidad Internacional de Valencia, 46002 Valencia, Spain
4 ESTIA Institute of Technology, University of Bordeaux, 64210 Bidart, France

Corresponding author: Yudith Cardinale (yudith.cardinale@campusviu.es)
This work was supported by the Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica—FONDECYT as an
Executing Entity of Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC) through the Project RUTAS:
Robots for Urban Tourism, Autonomous and Semantic-Based under Grant 01-2019-FONDECYT-BM-INC.INV.

ABSTRACT Urban tourism information available on Internet has been of enormous relevance to motivate
the tourism in many countries. There exist many applications focused on promoting and preserving the
cultural heritage, through urban tourism, which in turn demand a well-defined and standard model for
representing the whole knowledge of this domain, thus ensuring interoperable and flexible applications.
Current studies propose the use of ontologies to formally model such knowledge. Nonetheless, most of
them only represent partial knowledge of cultural heritage or are restrictive to an indoor perspective (i.e.,
museum ontologies). In this context, we propose the ontology CURIOCITY (Cultural Heritage for Urban
Tourism in Indoor/Outdoor environments of the CITY), to represent the cultural heritage knowledge based
on UNESCO’s definitions. CURIOCITY ontology has a three-level architecture (Upper, Middle, and Lower
ontologies) in accordance with a purpose of modularity and levels of specificity. In this paper, we describe
in detail all modules of CURIOCITY ontology and perform a comparative evaluation with state-of-the-art
ontologies. Additionally, to demonstrate the suitability of CURIOCITY ontology, we show several touristic
services offered through a framework supported in the ontology. The framework includes an automatic
population process, that allows transforming a museum data repository (in CSV format) into RDF triples
of CURIOCITY ontology to automatically populate the CURIOCITY repository, and facilities to develop a
set of tourism applications and services, following the UNESCO’s definitions.

INDEX TERMS Automatic population, cultural heritage, ontology, ontology evaluation, urban tourism.

I. INTRODUCTION public causing knowledge dissemination, covering more
Urban tourism is one of the promising areas for the spaces, going beyond countries borders (e.g., web pages, wiki
development of social and economic activities in urban envi- pages, virtual spaces, on-line information centres) [11], [12],
ronments [2]–[4]; thus it has become one of the core part and supporting tourist planning (e.g., e-tourism, recommen-
of cultural heritage in many countries. Cultural heritage dation systems) [13]–[20].
includes representations of the value systems, beliefs, tra- The huge amount of data that can be managed in such
ditions, and lifestyles of communities; it expresses stories information tools and services, demands the use of more
through the time and space, about a society and its culture. complex knowledge. Resources and objects that describe a
Urban tourism is a way to transmit and learn about cultural specific heritage (e.g., a collection, a museum, a historical
heritage of countries [5], [6]. Moreover, cultural heritage is site) are related to their designer, creator, or owner, related to
supported by communication and information technologies its convenience and function in a given time, and also can be
for its preservation [7]–[10], being accessible to a wider related and extended to other knowledge organizations, inside
and outside of the cultural heritage domain. All these rela-
The associate editor coordinating the review of this manuscript and tions generate an even more complex network of knowledge.
approving it for publication was Alba Amato . In this sense, it is evident the necessity of a well-defined and

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
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standard model for representing the knowledge managed by CURIOCITY ontology, along with CURIOCITY frame-
these on-line services. Semantic Web seems to be a clear solu- work represent novelty solutions for researchers and experts
tion, from which we can take its organizational and relational in this area from several perspectives: (i) new opportunities
capacity. It proposes concepts and tools such as ontologies, arise for the development of flexible, intelligent, and inter-
with the aim of creating a consensus of standard definitions operable services and applications in the tourism domain;
and structures, in order to describe resources and define their (ii) new frontiers are opened for the integration of urban
relationships. Ontologies formalize complex knowledge net- tourism in other domains, such as finance, sociology, urban-
works aiming to facilitate the process of sharing and reusing ism, by integrating CURIOCITY ontology with other ontolo-
information; they provide semantics of information sources gies; and (iii) participate in the linked open data to contribute
that can be processed by computers and be communicated and gain benefits.1
among different agents, both human and machines. Thus, The remainder of this paper is organized as follows.
an ontology is a formal way of capturing valid knowledge Section II presents a brief review about cultural heritage
from a particular domain. Hence, this formal modeling of concepts. In Section III, we discuss underlying standards and
knowledge in a specific domain allows the development of related work. Section IV introduces CURIOCITY ontology.
interoperable services, which can be easily adapted to the Section V presents and discusses the results of the evaluation
particular requirements of different users. of CURIOCITY ontology. Section VI presents a study case
In the context of cultural heritage, some studies have through the CURIOCITY framework services. Section VII
proposed ontologies to represent its partial knowledge. discuss our conclusions and future work.
Thus, there exist ontologies to represent museums [21]–[26],
improve exhibitions [27], [28], represent touristic points of II. UNESCO’s DEFINITION OF CULTURAL HERITAGE
interest [29]–[32], or represent curatorial narrative [33]. This UNESCO defines heritage as ‘‘our legacy from the past, what
diversity of purposes means that ontology design, although we live with today, and what we pass on to future genera-
it can start from some common or standard basis, must be tions’’. The concept of heritage is in continuous evolution;
adapted to optimally capture particular characteristics, which thus, richness and complexity of cultural heritage are evi-
can be influenced by social aspects, by project’s technolog- denced by the semantic evolution of this concept. UNESCO
ical requirements (e.g., distributed and distant data sources), classifies Cultural Heritage into two categories, Tangible and
by end users’ adaptability requirements (e.g., web page vis- Intangible, and besides that, defines Natural Heritage and
itors or robot guide systems), among many other variables. Armed Heritage categories, as follows [37]:
Moreover, most experiences about knowledge modeling of • Cultural Heritage:
cultural heritage in museums, are usually found within an – Tangible Cultural Heritage:
indoor perspective; however, cultural heritage concept is
∗ Movable Cultural Heritage: paintings, sculp-
dynamic, thus it encompasses other concepts with not only
tures, coins, manuscripts.
cultural value, but also aesthetic, academic, economic, and
∗ Immovable Cultural Heritage: monuments,
recreational values, which are relevant to a society. In addition
archaeological sites.
to this, urban tourism perspective, points out visitor’s interests
∗ Underwater Cultural Heritage: shipwrecks,
are broad within city environments, which conform urban
underwater ruins and cities.
tourist centers with their own cultural heritage, with partic-
ular features and relationships, and therefore, they require a – Intangible Cultural Heritage: oral traditions, per-
different knowledge organization. forming arts, rituals.
To overcome these limitations, in a previous work we • Natural Heritage: natural sites with cultural aspects.
have proposed the ontology CURIOCITY ( Cultural Heritage • Armed Heritage: heritage in the event of armed conflict.
for Urban Tourism in Indoor/Outdoor environments of the Loulanski [38] considers a previous classification, which
CITY) [34], to represent the cultural heritage knowledge includes other concepts like Handicrafts, Documentary,
based on UNESCO’s definitions. In this work, we describe in Digital and Cinematographic Heritage, Languages, Festive
detail its three-level architecture (Upper, Middle, and Lower Events, Music and Songs, Traditional Medicine, Litera-
ontologies) in accordance with a purpose of modularity and ture, Culinary Traditions, and Traditional Sports and Games.
levels of specificity. Based on a methodological process [35], Loulanski also defines a spectrum of cultural heritage values
we also perform an evaluation taking into account our cat- in detail, such as:
egorization of the cultural heritage knowledge [34], and • Cultural values, which consider that appreciation and
compared it with state-of-the-art ontologies. Additionally, conservation of heritage generate distinctiveness feel-
to demonstrate the utility and suitability of CURIOCITY ings at local, regional, and national levels.
ontology, we show several touristic services offered through • Educational and Academic values, that provide a way
a framework supported in the ontology [36]. The framework to understand the past of our own culture and with this
includes an automatic population process and provides facil- knowledge to plan our future.
ities to develop a set of tourism applications and services,
following the UNESCO’s definitions. 1 https://lod-cloud.net/

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• Economic values, to assure that historical environments and content, and their aggregation in large knowledge graphs
mean a contribution to economic development through (e.g., linked open data). Although there exit such kind of
tourism, and to represent how these values create a better research integrating e-tourism with semantic web, there is
environment for community development. still a gap from e-tourism and ontologies. A standard repre-
• Resource values, that consider that long life buildings sentation of the whole knowledge for e-tourism is missing.
mean better use of resources and energy. Actually, most ontologies considered in e-tourism services
• Recreational values, which represent historical environ- are specialized on POI or museums [49]–[53]. Following sec-
ments providing recreation and enjoyment. tions describe and comparatively evaluate POI and museums
• Aesthetic values, that reinforce the idea that historic ontologies and contrast them with our solution.
buildings contribute to the aesthetic quality of urban and
rural landscapes. B. POI ONTOLOGIES
Thus, the value or significance of cultural heritage is recog- Regarding ontologies for POI, several works have been pro-
nized beyond the cultural area, even within economic, social, posed in the literature for the context of tourism.
political, and scientific areas. The European Project ‘‘Harmonise’’ [29] proposes vari-
ous technologies to solve the interoperability problem in the
III. RELATED WORK tourist domain. To do so, they propose an ontology, called
The heterogeneity of concepts in cultural heritage domain has IMHO (Interoperability Minimum Harmonisation Ontology),
fostered the proliferation of different ontologies, particularly, that considers basic concepts used for representing the con-
in the context of tourism, to represent points of interest (POI) tent of information exchanges in tourism transactions [30].
and museum knowledge, that have been mainly used in the Another ontology proposed under an EU funded project,
context of e-tourism. In this section, we describe some recent is Qall-Me [31]. It is a domain-specific ontology for ques-
studies that highlight the use of semantic web for e-tourism tion answering in the domain of tourism. The tourism des-
and survey the most recent and representative ontologies tinations, tourism sites, tourism events and transportation
for POI and museums representations, which are the most are covered by this ontology. Qall-Me is aligned with two
important expressions for urban tourism. upper ontologies, WordNet2 and SUMO.3 In [32], an exten-
sion of the Qall-Me ontology is proposed, by adding a
A. E-TOURISM AND SEMANTIC WEB
new class SiteCategory and three object properties for
Many cities in the world are historic city centers and con- relationships, namely stronglyRelated, related, and
stitute one of the most important elements of the cultural weaklyRelated. Using these properties, several levels of
heritage. They are places that attract many visitors due to relationships among sites can be expressed. For example,
their relevance in terms of heritage. Actually, although cities a museum can be strongly related to a tourist office, while
are not necessarily historic centers, in general, part of a it is weakly related to an exhibition place.
tourist trip itinerary includes activities related to different The World Tourism Organization (UNWTO)4 was created
places of interest that can range from museums and parks to in 1975 for promoting the tourism, linked to the United
even medical centers [39]. Thus, urban tourism has become Nations a year later. As an effort of 20 years to standardize
one of the core part of cultural heritage in many countries, and normalize tourism terminologies, UNWTO proposed a
as recent studies express [2]–[4], [6], [40]. This trend has multi-language thesaurus (English, French, and Spanish) of
fostered the development of e-tourism systems, positioning the tourism domain in 2001. Terms very specific to tourism
them at the heart of much research that offer real benefits were also extensively defined for a better interoperability.
to users, organizations, and the business community. There Based on these concepts, Mondeca Tourism Ontology is
exist hundreds of studies in this regard, as recent reviews proposed. Tourism object profiling, tourism and cultural
highlight [18], [41]–[45]. objects, tourism packages, and tourism multimedia con-
Some of the efforts in e-tourism start to turn the interest on tent are described [55], [56]. HiTouch Ontology [55], cre-
using semantic web tools, since available data, content, and ated under the IST/CRAFT European program, and OnTour
services are becoming semantically annotated, which allow Ontology [31], developed by e-Tourism Working Group at
software components to search through the web and under- Digital Enterprise Research Institute, both also use the con-
stand its content. Experiences such the ones described in [13], cepts of UNWTO. HiTouch represents additionally tourism
[46], [47] reveal the benefits and advantages on using linked products and customers’ tourism expectations, while OnTour
open data in e-tourism. The idea is to build cultural heritage adds descriptions of leisure activities and geographic data.
knowledge from collaboration between open data published
by several institutions (e.g., governments, people interested,
tourists), enriched with data from other sources like DBPedia 2 WordNet is a lexical database for the English language -

and social media. The study presented in [48], surveys the https://www.w3.org/2006/03/wn/wn20/
3 SUMO: The Suggested Upper Merged Ontology, created for search,
most popular methods and tools used by touristic providers interoperation, and communication on the Semantic Web [54]
of information, products, and services to develop and apply 4 World Tourism Organization (UNWTO) - https://www.e-unwto.org/doi/
machine-processable (semantic) annotations of service, data, abs/10.18111/9789284404551

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DataTourisme5 ontology was created in 2017 by the com- widespread application in the library domain. RDA has a
pany PERFECT MEMORY in a french project. The aim of subscription cost.
this ontology is to centralize and publish as Linked Open The Committee for Documentation of the International
Data (LOD) travel information produced by different tourist Council of Museums (CIDOC) proposes the Conceptual Ref-
information systems in France. Additionally, this ontology erence Model (CIDOC CRM) [65], which since December
is connected to different existing ontologies as FOAF,6 2006, is recognized as an official ISO 21127: 2014 stan-
Schema,7 GoodRelations,8 Dublin core9 to do not duplicate dard. CIDOC CRM provides definitions, structures, basic
areas that are described already and in this way, to facilitate classes, and relationships for describing cultural heritage
links with these open databases. documentation for the querying and exploration of such data.
Local tourism ontologies for Australia [57], Thailand [58], It has extensions that allow adapting it to particular uses,
[59], Iran [60], and others, have also been proposed to mainly e.g., CRMdig [67], an ontology about steps and methods
develop applications such as recommendation systems [56], in the production of digital material and 2D and 3D digital
[60] and tourism planning [61]. representations.
Finto [66] is defined as a Finnish service for publishing
C. MUSEUM ONTOLOGIES and using vocabularies, ontologies, and classifications. Finto
Museums stand out as a knowledge source of cultural heritage is sponsored by various Finland government entities and is
and are the main POI within urban tourist centers. However, the successor of FinnOnto [68], an ambitious project that
the exiting proposals to represent knowledge related to muse- is the basis of metadata, ontologies, and LOD throughout
ums, vary according to characteristics of their research and Finland. The FinnOnto’s vision is to create a conceptual
particular interests. For example, they focus on aiming to deal semantic infrastructure to interconnect public and private
with the current data and resource heterogeneity [22], allow- organisms for intelligent exchange of content. Finto brings
ing collaboration among a group of museums [25], guiding a together ontologies from different domains, including Arts
visit according to a profile of interests [28], or providing the and Culture, that considers ontologies for Museum Domain
foundation for a virtual museum implementation [62]. This and Applied Arts (MAO10 /TAO,11 terminology of Folklore,
diversity of purposes means that ontology design, although Cultural Anthropology and Ethnology (KULO),12 Music
it can start from some common or standard basis, must be (MUSO),13 Musical Performance (SEKO),14 and Photog-
adapted to optimally capture particular characteristics, which raphy (VALO).15 These ontologies are based on YSO,16 a
can be influenced by social aspects, by project’s technolog- general concept ontology. YSO provides an extensive number
ical requirements (e.g., distributed and distant data sources), of concepts mainly arranged in a hierarchical structure, thus
by end users’ adaptability requirements (e.g., web page visi- it has the capability to encompass a wide range of environ-
tors or robot guide systems), among many other variables. ments. However its massive thesaurus nature and parent-child
The variety and heterogeneity of museum knowledge led structure could be overwhelming.
to the establishment of various standards with the purpose Usually, from these vocabularies and representation pro-
of normalizing and creating bases for ontology develop- posals, several authors have proposed ontologies in accor-
ment for particular purposes. Some popular standards in dance with their research objectives, extending or integrating
cultural heritage domain are, for instance, the thesaurus them.
ICONCLASS [63], the paid service Resource Description MUSEUM FINLAND project [69], is a proposal for
and Access (RDA) [64], the ISO Standard CIDOC CRM [65], semantic integration of museums in Finland, based on seven
and the massive thesaurus and ontology service Finto [66]. domain ontologies: Artifacts, Materials, Actors, Situations,
ICONCLASS [63] is a classification system of object Locations, Times, and Collections. MUSEUM FINLAND
definitions, people, events, and abstract ideas, arranged uses the Finnish cultural content thesaurus (Museoalan asi-
hierarchically, developed by the Netherlands Institute for asanasto - MASA)17 to create MAO ontology (now part of
Art History, that can be used for indexing, cataloging, and Finto service, as viewed before).
description of pictorial artworks, such as paintings, reproduc- Europeana Data Model (EDM) [70], has the aim to stan-
tions, photographs. dardize the representation of cultural heritage objects from
RDA [64] is a set of elements, guidelines, and instructions different domains such as libraries, museums, and audiovi-
for creation of metadata about library resources and cul- sual archives. It is not built on a particular standard, but adopts
tural heritage, according to international models focused on
linked data applications. RDA was created as a replacement
10 http://www.seco.tkk.fi/ontologies/mao/
for the Anglo American Cataloging Rules, and has a most
11 http://www.seco.tkk.fi/ontologies/tao/
12 http://www.seco.tkk.fi/ontologies/kulo/
5 https://info.datatourisme.gouv.fr/ 13 http://www.seco.tkk.fi/ontologies/muso/
6 http://www.foaf-project.org/ 14 https://www.kiwi.fi/display/Asiasanastotjaontologiat/
7 https://schema.org/ 15 http://www.seco.tkk.fi/ontologies/valo/
8 http://www.heppnetz.de/projects/goodrelations/primer/ 16 http://finto.fi/yso/en/
9 https://dublincore.org/ 17 http://id.loc.gov/vocabulary/subjectSchemes/masa.html

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a wide range of these, such as CIDOC LIDO18 for museums, Provided CHO (Cultural Heritage Object). From CRM,
EAD19 for archives, and METS20 for digital libraries; with it takes Actor, that includes Group and Person; Phy-
the intention of being a Semantic Web framework between sical Thing, which considers Physical Man-Made
different domains. Thing, Biological object, and Collection;
ArCo (Arquitecture of Knowledge) [71] is an Italian Conceptual Object, that includes Appelation
project with the purpose of building a network of aligned and Information Object, which in turn includes
ontologies to represent cultural heritage data and publish the Procedure, Linguistic Object, Document, and
General Catalogue proposed by the Italian Ministry of Cul- Visual Item. The ontology is completed with own con-
ture. ArCo ontology version 0.5 consists of seven modules: cepts, such as Role and Digital Information.
(i) arco, is the root of the network, it imports the other six OntoMP [62], [73], is the foundation ontology of Museu
modules and models top level cultural heritage concepts; da Pessoa (MP), a virtual museum which has the purpose of
(ii) core, represents orthogonal concepts imported by the exhibiting stories about ordinary people. OntoMP is primarily
other modules; (iii) catalogue, models catalogue records; based on CIDOC CRM, in addition to FOAF and DBPe-
(iv) location, represents spatial and geometry information; dia. OntoMP concepts are directly related to person nature
(v) denotative description, covers measurable characteristics (People, Ancestry, Offspring, etc.), life episodes
and properties; (vi) context description, models the context (Childhood, Leisure, Marriage, Birth, etc.),
covering information related to agents, activities, or situa- abstract concepts (Dreams, Religion, Costumes,
tions; and (vii) cultural events, represents cultural events and etc.); relationships (Receives, Visits, Performs,
exhibitions. etc.). Some concepts are directly referred to CIDOC CRM,
SCULPTEUR [21] project, under the support of the however some properties related to the person cannot be
European Union, aims to develop a system for browsing described naturally, thus FOAF concepts such as Gender,
and searching museum collections using textual metadata, Person Names, and Person-images relations are
in addition to content analysis and an ontological classifica- included. From DBPedia, properties such as Religion,
tion. The proposed architecture contains a semantic layer that Profession, Education, Party, and Spouse are
consists of an ontology and information instances. SCULP- included.
TEUR is based on CIDOC CRM, and extends it to include Marchenkov et al. [23] propose an ontology aimed at
concepts such as objects digital representations and their developing a digital environment oriented to visitors and
respective feature vectors, besides tools and algorithms used museum service staff. This environment offers personal
to produce and compare feature vectors, query construction, recommendations based on user context and exhibition char-
and digital media for displaying purposes. acteristics; in addition to the collaborative management of
CURATE [33] presents an approach for representing cura- information contained in different museums. Authors pro-
torial narratives, thus, an exhibition is enriched by stories, pose a layer-based model in which the semantic layer is
or even to conform an exhibition by themselves with support responsible for providing mainly three advanced services:
of physical media. The problem approached by authors of (i) Visiting Service, consists of creating personalized exhi-
CURATE is that narrative meaning cannot be expressed or bition of a set of museum objects, based on the available
derived solely from the metadata of cultural heritage objects. knowledge of the visitor; this service adapts itself dynami-
Authors of CURATE base their research on the hypothesis cally during the museum tour; (ii) Exhibition Service to dis-
that curatorial narrative has generic characteristics and prop- play descriptions and visual information on visitors’ personal
erties that can be found in other narratives, such as novels or screens and devices; thus, physical exhibition is enlarged by
films, hence, concepts like Story, Plot, and Narrative using digital media; and (iii) Enrichment Service to support
can be adopted. CURATE is based on CIDOC CRM and the evolution of a semantic network, allowing to receive
DOLCE + DnS Ultralite (DUL) [72] ontologies. notes from visitors and staff in order to improve the database
MOM [22], is a top level ontology that also deals with the information. This ontological model is based on CIDOC
heterogeneous nature of cultural heritage and is based mainly CRM, but it is extended to be able to host a recommendation
on CIDOC CRM and EDM, in addition to ORE,21 FOAF,22 system, through a sub-ontology called Rank, which contains
DC,23 and SKOS.24 MOM ontology takes from EDM, the Rank class that stores exhibits scores, in addition to
classes like Non-Information Resource, which Exhibit and Profile classes.
includes Event, Time Span, Place; Informati- TOMS (Thailand Open Museum System) [25], is a project
on Resource, which includes Web Resource and whose main objective is to enable collaboration and informa-
tion exchange among Thailand national museums. It is based
18 http://cidoc.mini.icom.museum/es/grupos/lido/what-is-lido/
on LOD and CIDOC CRM. The project proposes a three layer
19 https://www.loc.gov/ead/index.html
20 http://www.loc.gov/standards/mets/
architecture: Data Storage, Manipulation and Processing, and
21 https://www.openarchives.org/ore/1.0/datamodel a System Interface Layer. Authors of TOMS detail how exist-
22 http://www.foaf-project.org/ ing information is mapped to CIDOC CRM corresponding
23 https://www.dublincore.org/specifications/dublin-core/dces/ concepts. Finally, they make a qualitative evaluation based
24 https://www.w3.org/2004/02/skos/ on the user’s experience and their satisfaction level. Some

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evaluation points are the improvement in work efficiency and – Music and Songs: defines concepts linked to music
the perceived system utility. and its production as society cultural expression.
Lo Turco et al. [24], describe a research about relation- • Ranking: needed concepts to rank cultural heritage
ships among cultural heritage, digital technologies, and visual expressions (e.g., exhibitions, monuments, events, cul-
models. Authors use CIDOC CRM classes and relationships tural sites) according to different criteria (e.g., visitors,
for available data and the CRMdig extension for the mapping reviews, comments).
of the documentation of the evaluative, analytical, deductive, Although this cultural heritage knowledge categorization
interpretative, and creative decisions related to gathering data covers our initial requirements (see Section IV), it can
stage and then to computer-based visualization process. be further extended with other cultural heritage and urban
tourism topics, such as Language and Traditional Medicine,
D. PROPOSED CULTURAL HERITAGE KNOWLEDGE as proposed in [38].
CATEGORIZATION
In our previous work [34], we propose a categorization of the E. COMPARISON
cultural heritage knowledge, based on the UNESCO’s defini- Table 1 presents the comparison of the reviewed ontologies,
tion and on relevant ontologies that represent some aspects of in terms of the proposed knowledge categorization, extending
it. Our categorization considers the following aspects: the scope of our previous work [34]. The proposed clas-
• Temporal Item: sification does not attempt to compare the scope of each
– Event: events, occasions, or situations. proposal within each concept. Table 1 displays only reviewed
– Time-Span: historical period of time. studies which have available information about resources,
as concepts and properties that are part of them, leaving out
• Permanent Item:
works that do not present these details.
– Place: locations, physical areas. While standards are a good starting point, it could be they
– Actor: people, roles, groups. do not cover all the edges that can arise during the devel-
– Physical Object: defines artifacts that can be opment of a project or their complexity may lead to adopt
human-made or from natural origin. it partially. Main POI ontologies have been developed from
– Material: defines the materials from which the arti- European projects [29], [31], [55], during the first decade of
facts are made. the 2000s, and nowadays, some of the project webpages are
– Person Extended: refers to concepts linked to not available, as well as their ontologies. DataTourisme is one
humans, such as their identity (names, nicknames, of the most recent ontology and it is currently under support
gender, etc.), to abstract elements (dreams, customs, and constant updates. As shown in Table 1, for POI ontolo-
profession, etc.), and human relationships such as gies, concepts related to time (Temporal items) are partially
politics or religion. covered, e.g., they do not cover activities such Production or
• Exhibition Creation. Permanent Items are partially represented by such
– Digital representations: defines the creation process ontologies, due to they can model touristic places, but they
and products of digitizing an exhibition (e.g., do not have the interest on representing artworks that can be
images, video, documents). present is such places (i.e., physical objects, their materials).
– Digital Processing and Analysis: refers to the Actually, touristic places represented by these ontologies go
process, treatment, tools, and analysis of digital beyond cultural heritage interest, they include, for example,
representations. hotels, restaurants, shopping centers. Since most of these
– Collections: set of physical or abstract objects that ontologies support tourist recommendation systems, concepts
conforms a collection. related to ranking are mostly covered.
– Narrative: elements that allow generating a story. Concerning, museum ontologies, concepts related to Tem-
• Extended Cultural Heritage poral items, Permanent items, and digital representations are
– Performance: defines concepts linked to cultural the main targeted represented resources, while Curatorial
activity carried out by people, such as speeches or Narrative and Ranking concepts are neglected. For both POI
dance. and museum ontologies, modeling Extended Cultural Her-
– Site as Cultural Heritage: defines elements to itage is not considered.
extend the Place concept at Permanent Item to be CURIOCITY ontology seeks to cover all aspects of our
able to include outdoor places with cultural interest proposed cultural heritage categorization in a minimalist way,
as cities, landscapes, etc. identifying concepts and properties, which serve as a nexus
– Event as Cultural Heritage: defines elements to and points of integration and extension to other domains.
extend the Event concept at Temporal Item to be It presents a modular conception with the intention of being
able to include social activities as festivals, rites, flexible and adaptable according to the application character-
etc. istics. Since, it is based on standards, such as CIDOC CRM,
– Culinary Tradition: defines the process and prod- and ICONCLASS, and on widely used ontologies such as
ucts of food preparation with cultural interest. Finto or DBPedia, the interoperability is guaranteed. For the

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TABLE 1. Comparison of ontologies related to cultural heritage.

design of CURIOCITY ontology, we gather the experience To cover these requirements, we evaluated popular ontolo-
of previous proposals and take into consideration aspects gies in this domain. We considered CIDOC CRM, FINTO,
already cataloged as necessary. It is possible to represent the and ArCo as the closest to our objectives, because their
concept of cultural heritage not only contained in museums degrees of knowledge coverage. Although we knew the com-
but from a broader view according to the UNESCO catego- plex nature of CIDOC CRM, as noted in [74], we decided
rization. This greater representation perspective allows other to adopt it as the base ontology. It was not an easy decision.
city elements to acquire a cultural, educational, economic, Even though FINTO and ArCo have a greater coverage of
and recreational value; thus, its tourist attraction is enriched. concepts, the adoption of CIDOC CRM was due to its status
CURIOCITY ontology is described in more detail in the as a standard.
following section. CURIOCITY ontology is mainly a subset of CIDOC
CRM [65], which is focused on events; thus, it imposes a
IV. CURIOCITY ONTOLOGY: OUR PROPOSAL particular perspective of knowledge representation, that must
Our proposal is developed in the context of the project be taken into account when integrating with other ontologies.
RUTAS (Robots for Urban Tourism Centers, Autonomous According to RUTAS project requirements, we define five
and Semantic based),25 aimed at developing tourist guide extensions to CIDOC CRM based on the UNESCO’s classi-
robots and services for the diffusion and preservation of fication for a wider representation of the concept of cultural
cultural heritage and urban tourism. One of the RUTAS’s goal heritage: (1) Site as Cultural Heritage; (2) Event as Cultural
is to create a knowledge base of museums (indoor places) Heritage; (3) Performing Arts; (4) Music; and (5) Culinary
in Arequipa city in Peru, as well as the characterization of Tradition.
cultural and touristic elements present in its historical center We considered CURATE [33], due to curatorial narra-
(classified as cultural heritage by UNESCO), such as land- tive has special interest to be applicable in the context of
scapes, monuments, buildings, which correspond to outdoor tourist guide robots, in order to provide them story narra-
environments. tive capabilities. Arts and Culture Category of Finto [66],
Urban tourism as cultural heritage must also be approached DBPedia [75], and ICONCLASS [63], are also useful for
in the context of RUTAS project. Thus, artistic urban expres- inclusion and extension to new concepts and relationships.
sions, culinary art, urban cultural events, dance, music, etc., We also included CRMDig extension [67] to model dig-
should be also represented. ital representations of cultural elements, as well as other
Concepts related to handicrafts are also urban tourism ontologies from domains of interest, such as music (e.g.,
expressions. However, they present similar characteristics MUSIC Ontology [76], DOREMUS [77], [78]) or food
to cultural heritage contained in museums, thus, it is not (FOODON [79]), in favor of identifying interconnection
required to model additional elements to represent crafts- points, towards which they will be proposed the required
manship. On the other hand, it is necessary concepts related extensions.
to urban collections and exhibitions, such as ranking and CURIOCITY ontology development was carried out using
curatorial activity. a top-down approach, which means identifying general
terms and then going to specific ones [80]. We followed
the simple but effective approach proposed by Methontol-
25 https://github.com/JADA1979 under construction ogy [81], which consists of seven phases: (i) Specification;

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FIGURE 2. CURIOCITY Upper ontology: general reasoning.

FIGURE 1. General architecture of CURIOCITY ontology. Ontology, DOREMUS, FOODON), and cit: for classes,
properties, and relations added in CURIOCITY ontology.
crm:Event takes a central position, in accordance
with CIDOC CRM proposal. crm:Event is a sub-
(ii) Knowledge Acquisition; (iii) Conceptualization; (iv) Inte- class of crm:Period and this in turn is a subclass of
gration; (v) Implementation; (vi) Evaluation; and (vii) Docu- crm:Temporal Item, which is defined by crm:Time
mentation. We partially show the results of the Specification Lapse. A crm:Event occurs in a crm:Place (indi-
and Knowledge Acquisition phases in Section III, that present rectly through crm:Period) and involves a crm:Per-
our proposed knowledge categorization and the comparison sistent Item, which is superclass of crm:Actor,
with related studies. The result of the rest of phases are crm:Conceptual Thing, and crm:Physical
described in this section and the following one. We will keep Thing.
iterating on these phases to reach a more extended CURIOC- At this level of specificity, we identify general concepts
ITY ontology version. from which our five required extensions must derive. We have
CURIOCITY ontology is defined in three levels of speci- extended crm:Event concept with subclasses that by them-
ficity: (i) Upper Ontology, that identify two main branches selves constitute a cultural heritage, such as festive events,
from which general concepts are derived (Persistent Item and traditions, rites, celebrations, and similar; thus, CURIOCITY
Temporal Item modules); (ii) Middle Ontology, with classes ontology has cit:Event CH class (Event as Cultural Her-
and properties needed to extend the concept of cultural her- itage), as a subclass of crm:Event to represent them.
itage (Extended Cultural Heritage module); and (iii) Low In the same way, crm:Site is extended to make it
Ontology, providing a higher level of detail for the represen- possible to characterize places that constitute a heritage
tation of artwork objects. These levels are not mutually exclu- by themselves. CURIOCITY integrates Site CH (Site as
sive, they are only intended to indicate an abstract division of Cultural Heritage), which may contain subclasses according
specificity for the purposes of reasoning and concepts ana- to UNESCO’s classification such as: Historic Cities,
lyzed. CURIOCITY ontology is also enriched with axioms Cultural Landscapes, Underwater Cultural
and inference rules, which are part of the Logic component. Heritage, and Natural Sacred Sites.
The whole proposed architecture is depicted in Figure 1. Culinary traditions is considered as a crm:Physi-
In the following we explain each level in detail. cal Thing subclass, which is a non direct subclass of
crm:Persistent Item. It is proposed cit:Food and
A. UPPER ONTOLOGY cit:Food CH classes. In the case of music, songs, and
The dichotomy of continuity and occurrence is taken as basis performing arts, their extension is considered as subclasses
for entities hierarchy. Persistent Item represents things that of crm:Conceptual Thing, CURIOCITY ontology
have a persistent identity, which survive events. These can be includes the concepts cit:Music and cit:Performing
people, objects, ideas, or concepts. While Temporary Entity, Arts.
represents temporal concepts or phenomena whose nature We adopt the CRMDig extension (dig:Digital
is related to happening rather than being. From these two Object) to characterize digital representations of ele-
general concepts, it is defined the first general reasoning of ments of digital exhibitions, such as virtual museum
CURIOCITY ontology, which conforms the Upper Ontology implementations.
and is represented in Figure 2. We use the following prefixes Also, cur:Curatorial Narrative concept, based
to identify classes and relationships taken from the corre- on CURATE ontology, is integrated to cit:Event CH to
sponding ontology: crm: for CIDOC CRM ontology, cur: be able to represent narrative as cultural heritage.
for CURATE ontology, fin: for Finto ontologies, dbp: Additionally, the crm:Person class, which is rather
for concepts from DBPedia, mus: and foo: for concepts limited on CIDOC CRM, is extended with properties from
of music and food ontologies, respectively (e.g., MUSIC FOAF, thus a better representation of human characteristics is

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available. Furthermore, to improve expressiveness in the tem- Code 1. Site as Cultural Heritage module related triples
poral domain, OWL-Time [82] and CRMgeo [83] concepts
have been incorporated to CURIOCITY; and inference rules
have been formulated from the temporal relations of Allen’s
interval algebra [84].
Having identified these primary higher level elements,
it can be specified the next level of reasoning that defines the
specialized modules of CURIOCITY Middle Ontology.

B. MIDDLE ONTOLOGY
This level presents classes and relationships that allow the
extension and integration of CURIOCITY Upper Ontol-
ogy with ontologies from other domains in order to enrich
the representation of heritage knowledge. In this version of
CURIOCITY we present five extensions according to our
project requirements.

1) SITE MIDDLE ONTOLOGY MODULE
Having identified place-related concepts, it is necessary to
extend the knowledge, in such a way a site can also be a
cultural heritage in its own right. This idea is reinforced
by the special status UNESCO grants to certain cities to
promote its conservation and protection. Cities, in turn, are
home to places and points with cultural and touristic interest,
therefore cit:Site CH (Site as Cultural Heritage) is a con-
cept proposed in CURIOCITY ontology. Other place-related
concepts have been identified as cit:Site CH sub-
classes, such as cit:Park, cit:Protected Area,
and cit:Natural Landscape, which are adapted from
DBPedia, in addition, the corresponding class description
(rdfs:comment) includes comments to distinguish them FIGURE 3. Reasoning about extended site as cultural heritage: Site
as classes to represent items with cultural interest. Other Middle Ontology module.
related subclasses can be extended according to the needs of
case of study (see Figure 3). To better describe a place with
cultural interest, it is necessary to use some concepts, such as involves crm:Physical Things, has a classification or
area, altitude, population, or time zone. These concepts are crm:Type (e.g., religious, sport, cultural), and is held in a
instances of the crm:Dimension class, which are quan- crm:Place and in a crm:Time-Lapse. This reasoning
tified with a crm:Measurement Unit, such as square is illustrated in Figure 4 and Code 2.
kilometers, meters above sea level, inhabitants, or GMT time.
It is also necessary to describe non-exact characteristics as a 3) MUSIC MIDDLE ONTOLOGY MODULE
cit:Quality of the place, such as cit:Climate. The Music and Songs are others cultural expressions consid-
reasoning for cit:SiteCH can be seen in Figure 3 and ered by UNESCO. The concept of cit:Music is added
Code 1. to CURIOCITY ontology as a crm:Conceptual Thing
subclass, which is already identified by CIDOC CRM. Music
2) TEMPORAL ENTITY MIDDLE ONTOLOGY MODULE as cultural heritage requires other concepts which allow
crm:Event is defined, according to CIDOC CRM, as the to extend beyond a music score contained in a museum
coherent processes and delimited interactions of material and to be understood as a representative cultural expression
nature in physical, social, or cultural systems. In this way, of the people (e.g., peruvian Huayno). One effort for this
cit:Event CH (Event as Cultural Heritage) proposes an integration is DOREMUS [77], an extensive project that
extension to characterize festive events and traditions as among other contributions presents an ontology based in
social activities, besides of rites and customs, that qual- FRBRoo26 and CIDOC CRM. DOREMUS aims to char-
ify as cultural heritage (e.g., the Inti Raymi Festival in acterize music scores and recording data. Another pro-
Cuzco-Perú or the Rio de Janeiro Carnival in Brazil). posal for the integration of music and cultural heritage
A Cultural Event such as a cit:Tradition is a sub- is presented by Thalmann et al. [78], a model for physical
class of cit:Event CH, which in turn has subclasses
such as cit:Rite, in which an crm:Actor participates, 26 http://www.cidoc-crm.org/frbroo/

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Code 2. Event as Cultural Heritage module related triples Code 3. Music module related triples

FIGURE 4. Reasoning about temporal information and event: Temporal
Entity Middle Ontology module.

and digital representation of music-related artifacts, as well
as the paraphernalia of live music events, harmonizing
CIDOC CRM, FRBR27 and the Music Ontology. We include
FIGURE 5. Reasoning about music: Music Middle Ontology module.
some minimal elements that permit the integration with
more elaborated ontologies, such as the mentioned above. Code 4. Performing Arts module related triples
Music related activities like its crm:Creation by an
crm:Actor, its cit:Performance by playing (per-
forming) a mus:Musical Instrument; and its classi-
fication by a mus:Music Genre, which is a subclass of
cit:Genre. Figure 5 and Code 3 illustrate this reasoning.

4) PERFORMING ARTS MIDDLE ONTOLOGY MODULE
In a similar way to cit:Music, cit:Performing
Art class represents cultural activities that character- 5) FOOD MIDDLE ONTOLOGY MODULE
ize people, including dances, theatrical performances, The culinary tradition identifies and characterizes one soci-
or similar. cit:Performing Art is a subclass of ety from another in a particular way. cit:Food CH as
crm:Conceptual Thing and has also cit:Genre to cultural heritage is proposed in a minimalist way that
classify these activities. Figure 6 and Code 4 illustrate this allows integration with ontologies of food domain, such
reasoning. as FoodOn [79]. It is also considered a cit:Food
Product Type to classify ingredients, as a subclass of
crm:Type; and cit:Preparation Process as an
27 https://www.oclc.org/research/activities/frbr.html activity to describe the food preparation process, as a subclass

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(i.e., artworks in museums, historical churches, exhibitions,
etc.) and outdoor environments (i.e., monuments and art-
works in the city, in parks, etc.). This knowledge has been
analyzed by standards, such as CIDOC CRM, which is
continuously reviewed and improved by use and research
experiences. Figure 8 depicts some of the concepts about
artworks and monuments reasoning, which are considered
in CURIOCITY ontology, e.g., an crm:Activity (sub-
FIGURE 6. Reasoning about performing arts: Performing Arts Middle
Ontology module. class of crm:Event), such as the production of an uten-
sil (crm:Persistent Item) like a basket case (Cesto),
Code 5. Culinary Tradition module related triples
was carried out by a prehispanic culture such as the Nazca
(crm:Group). Nowadays, this artifact is under the custody
of a Peruvian Museum (crm:Group), exhibited in its Arche-
ology and Ethnology department (crm:Place); located in
Arequipa city, declared World Cultural Site (cit:Site
CH) by UNESCO.
CURIOCITY ontology respects event-based CIDOC CRM
approach, however it adds richness to the concepts that
were defined in previous sections, i.e., an crm:Event is
not only a link between crm:Actor, crm:Thing, and
crm:Place, but crm:Event and crm:Place can repre-
sent by themselves an entity of cultural heritage. In this way,
we have a reasoning not only towards events, but also from
events. In the same way, a crm:Place not only delimit a
space, but they are also cultural heritage that includes other
cultural heritage elements.

D. IMPLEMENTATION
CURIOCITY ontology28 is implemented using Protégé [85]
as development environment and OWL 2 RL as the ontology
language. CURIOCITY is based on CIDOC CRM 6.2.2,
available in ERLANGEN CRM29 170309. Rules are imple-
mented with SWRL.30
The current version 0.3 of CURIOCITY ontology counts
with 108 classes, 322 object properties, 36 data properties,
and 14 inference rules. This version includes inferences rules
based on temporal relations of Allen’s interval algebra [84],
FIGURE 7. Reasoning about culinary tradition: Food Middle Ontology in order to generate and identify relations between instances
module. in temporal domain. For this purpose, we introduce con-
cepts from OWL-Time31 and CRMgeo.32 We also plan to
include rules in geospatial domain, as a final step to study
of crm:Production. cit:Preparation Process spatio-temporal relationships between entities.
is based on foo:Food Transformation Process, The proposed inference rules are expressed with propo-
a more complex concept to represent different types of sitions, such as ProperInterval (T), hasBeggining, hasEnd,
food processing. In a minimalist way, we can represent lessThan, intervalStarts, intervalOverlappedBy, interval-
a typical dish such as Peruvian Ceviche as an instance MetBy, contains, etc. For instance, the relation intervalOver-
of Food CH, product of the Process of Ceviche prepara- laps defined by:
tion (cit:Preparation Process), classified as mar-
inated as cooking method (crm:Type), from ingredients ProperInterval(T 1) ∧ ProperInterval(T 2)
such as green lemon, sea fish, red onion, etc. (instances of ∧ . . . . . . ∧ hasBeggining(T 1, T 1begin)
cit:Food). This reasoning is illustrated in Figure 7 and ∧ hasEnd(T 1, T 1end)
Code 5.
28 https://giulianodelagala.github.io/CURIOCITY/
29 http://erlangen-crm.org/
C. LOW ONTOLOGY: ARTWORKS AND MONUMENTS 30 https://www.w3.org/Submission/SWRL/
The reasoning about cultural heritage in its conventional 31 https://www.w3.org/TR/owl-time/
form refers to elements contained in indoor environments 32 https://cidoc-crm.org/crmgeo/home-5

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FIGURE 8. Reasoning about middle and low ontology elements CURIOCITY - CIDOC CRM.

Code 6. SWRL inference rule example categorization of cultural heritage. The following section
presents the evaluation of our proposal.

V. CURIOCITY ONTOLOGY EVALUATION
An evaluation of our proposal has been carried out in order to
answer three main questions: (i) What percentage of elements
do we keep in common with CIDOC CRM standard; (ii) How
do these changes affect various aspects such as the complex-
ity, ease of use or maintenance of our proposal compared to
others?; and (iii) Do the elements that constitute our proposal
contribute to a better representation of the cultural heritage?.
To evaluate ontologies, it is appropriate to follow a
methodological process that provides metrics for qualita-
∧ . . . . . . ∧ hasBeggining(T 2, T 2begin) tive and quantitative assessments. In this work, we follow
∧ hasEnd(T 2, T 2end) the systematic approach proposed in [35], which in turn is
based on well-known ontology evaluation strategies such
∧ . . . . . . ∧ lessThan(T 1begin, T 2begin)
as: golden standard [86], OQuaRE [87], OntoMetrics [88],
∧ . . . . . . ∧ lessThan(T 2begin, T 1end) and OOPS! [89]. This methodology offers a comprehensive
∧ . . . . . . ∧ lessThan(T 1end, T 2end) evaluation and even a comparative evaluation with similar
→ intervalOverlaps(T 1, T 2) available ontologies. It proposes a guideline to comparatively
evaluate ontologies, considering Correctness and Quality per-
is expressed in SWRL language and included in the ontology spectives, based on three levels of comparison:
(Code 6). • Lexical: it includes linguistic, vocabulary, and syntactic
Each of the components of CURIOCITY ontology has aspects.
been developed taking into account the knowledge repre- • Structural: it considers aspects related to taxonomy, hier-
sentation objectives of the RUTAS project and UNESCO’s archy, relationships, architecture, and design.

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where VSO∗ are the term weighting vectors of the ontologies.
To compute DocSim, we use the class TFIDFVectorizer of the
scikit-sklearn library of Python.33

TermWeighting = TF × IDF (1)
t
TF = (2)
T
1 D
IDF = × (1 + log 2 ) (3)
2 d
VSOi · VSOt j
DocSim(Oi , Oj ) = (4)
FIGURE 9. Perspectives, levels, and methods for a comparative study of kVSOi kkVSOj k
ontologies [35].
According to DocSim(Oi , Oj ) metric, CURIOCITY ontol-
ogy has a 71.8% similar terms to ERLANGEN CRM;
• Domain Knowledge: it considers how effectively the whereas the left percentage (28.2%) corresponds to the inclu-
knowledge has been covered and how the results of the sion of other concepts and properties which conform the
application are aided by the use of the ontology. proposed extensions.
Figure 9 illustrates the components of this evaluation
framework. This comparative evaluation process assumes the B. STRUCTURAL LEVEL
existence of a reference, called golden standard, which can be The structural level is mostly evaluated according to the
represented by a knowledge categorization elaborated with relationships among entities, as ontologies are graphs. We use
the support of experts in the area, a base ontology, or a OQuaRE methodology [87] to conduct a structural evalua-
set of documents describing the domain knowledge. In our tion. OQuaRE metrics are calculated according to (5) to (16).
case, the golden standard is defined by the knowledge cat- All OQuaRE’s characteristics (i.e., Structural, Functional
egorization based on UNESCO’s cultural heritage definition Adequacy, Compatibility, Reliability, Transferability, Oper-
presented in Section III-D and the requirements from RUTAS ability, and Maintainability) are scored according to the
project. We also conduct a structural comparative evaluation OQuaRE scale system (i.e., 1 means not acceptable, 3 is mini-
using OQuaRE methodology among CURIOCITY, CIDOC mally acceptable, and 5 represents exceeds the requirements).
CRM Standard (available as ERLANGEN-CRM), and ArCo We implemented an application34 to perform automated
Ontology [71]. metrics calculation, the assignment of the score according to
OQuaRE charts, the results presentation, as well as graphics
A. LEXICAL LEVEL that allow a better comparative analysis. The application was
At this level, the evaluation is based on similarity metrics developed in Python 3.8, using libraries such as Rdflib for the
that allow analyzing the proximity of concepts and related management of the ontology graph, as well as the generation
vocabulary within the domain, from the ontologies evaluated. of necessary queries; Numpy for numerical processing, Pan-
To calculate these similarity metrics, we have developed das for the generation of results tables, and Matplotlib for the
a parser that allows the extraction of the ontology entities creation of graphs.
(i.e., classes, relationships, properties) from their RDF/XML
6PathLength(CThing , LeafCi )
language implementations. Thus, we have the lists of entities LCOMOnto = (5)
names of both ontologies. 6PathLeaf Cj
To determine the percentage of reuse of ERLANGEN 6PathLength(CThing , LeafCi )
WMCOnto = (6)
CRM elements adopted in CURIOCITY ontology, we uti- 6LeafCi
lize the Document Similarity using the Vector Space DITOnto = max(PathLength(CThing , LeafCi )) (7)
Model (VSM) to evaluate linguistic similarity between two NACOnto = 6Ci 6AncCj /6LeafCj (8)
ontologies [90]. In this sense, each ontology is represented
6Ci 6SubCj / 6Ci − 6LeafCk

NOCOnto = (9)
as a document that consists of a bag of terms (conformed by
6Ci 6AncCj / 6Ci − 6CTk

the N terms that appear in any of the documents) extracted CBOnto = (10)
6Ci 6ProCj + 6Ci 6AncCk /6Ci

from the lists of entity’s names, labels, and comments in the RFCOnto = (11)
ontologies. The term weighting function to calculate each NOMOnto = 6Ci 6ProCj /6Ci (12)
component in the N-dimensional vector for each ontology 6Ci 6SubCj
is presented in (1) to (3), where t is the number of times a RROnto = (13)
6Ci 6SubCj + 6Ci 6ProCk

term occurs in a document, T is the total terms in document,
D is the total of documents to compare; and d denotes the 33 https://scikit-learn.org/stable/modules/generated/sklearn.feature_
number of documents where the term occurs at least once. extraction.text.TfidfVectorizer.html
Then, the Document Similarity between the two ontologies 34 https://github.com/giulianodelagala/CURIOCITY/tree/master/
is calculated by taking the cosine dot product, as (4) shows, Evaluation/OquaRE

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TABLE 2. Structural comparison: CURIOCITY, ERLANGEN CRM, ArCo.

6Ci 6ProCj TABLE 3. OQuaRE evaluation summary.
PROnto = (14)
6Ci 6SubCk + 6Ci 6ProCj

INROnto = 6Ci 6SubCj /6Ci (15)
TMOnto2 = 6Ci 6AncCj /6Ci (16)
where,
• Ci : Ontology classes.
• RCi : Relations of class Ci .
• ProCi : Properties of class Ci .
• AncCi : Direct ancestor of class Ci .
• SubCi : Direct subconcept of class Ci .
• CThing : Ontology root.
Table 2 details the obtained metrics and their corre-
Figure 10(c) shows the comparison of the three ontolo-
sponding OQuaRE score to evaluate the structural level,35
gies according to the sub-characteristics corresponding to
for CURIOCITY, ERLANGEN-CRM, and ArCo. Figure 10
Compatibility (Replaceability), Reliability, Transferability
depicts the comparison of the three ontologies according to
(Adaptability), and Operability (Learnability) characteristics.
each OQuaRE’s characteristic.
ArCo scores the best of the three ontologies. CURIOCITY
Figure 10(a) depicts the OQuaRE’s Structural charac-
scores higher than CIDOC CRM for each of these charac-
teristic, which evaluates ontology quality factors, such as
teristics, which indicate that better performance is expected.
Consistency, Formalization, and Entanglement. In this case,
This improvement in the overall scores is mainly due to a
ontologies score similar for each sub-characteristic. The
higher value of the WMCOnto metric; denoting that ArCo and
weakness of the ontologies is in Cohesion, whose LCOMOnto
CURIOCITY are less complex.
metric shows that there is a strong dependency between
Figure 10(d) shows the Maintainability comparison. ArCo
components, mainly due to the complexity of the relation-
gets the highest score in all the sub-characteristics, followed
ships between concepts. The other sub-characteristic with the
by CURIOCITY. In the case of CURIOCITY, the weakest
lowest score is Formal Relationships, linked to the RROnto
scores are in Analysability and Testability sub-characteristics,
metric, which indicates that the ontologies present a lower
which can be understood as a degree of difficulty in diagnos-
number of sub-concepts versus the number of properties; it is
ing deficiencies and validation.
not exactly a symptom of weakness of the ontologies, but an
Table 3 and Figure 11 show the summary of the OQuaRE
indicator of how they are structured.
evaluation for the CURIOCITY, ERLANGEN CRM, and
Figure 10(b) represents the comparison of the two ontolo-
ArCo ontologies. ArCo scores higher overall mainly due to
gies according to Functional Adequacy scores. CURIOC-
metrics such as WMCOnto (score 5), NOMOnto (score 4), and
ITY and ERLANGEN CRM get similar scores for each
DITOnto (score 2), which show that Arco has a less complex
sub-characteristic. The weakness of both ontologies is in
structure. CURIOCITY scores better than CIDOC CRM in
Clustering and Similarity sub-characteristics, because a wide
Transferability, Reliability, Compatibility, Maintainability,
range of properties of each concept makes clustering process
and Operability characteristics, which implies being easier to
difficult, whereas ArCo presents a better behavior. The other
adapt and maintain without losing interoperability with the
sub-characteristic with a low score is Results Representation,
standard ontology.
which indicates that all three ontologies are complex; there-
fore, they have a degree of analysis difficulty in the results
C. DOMAIN KNOWLEDGE LEVEL
they provide.
At this level, the defined golden standard is used to asses cov-
35 http://miuras.inf.um.es/evaluation/oquare/Metrics.html erage and correctness of the knowledge of the domain. Our

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FIGURE 10. OQuaRe characteristics comparison.

golden standard is represented by the categorization based the area of museums and involved in art, cultural heritage,
on UNESCO’s definition of the cultural heritage knowledge and tourism, were obtained. Demographic data are shown in
described in Section III-D and the requirements of RUTAS Table 4.
project. We subjected CURIOCITY ontology to experts eval- A first question presented to the experts was: When you
uation, to confirm that golden standard is appropriate in this think of Cultural Heritage, which concepts comes to your
domain. mind?; the expert is asked to give a level of relevance from
‘‘Unrelevant’’ to ‘‘Very relevant’’ for concepts related to:
1) EXPERTS’ OPINIONS REGARDING THE GOLDEN ‘‘Event, situations of interest’’, ‘‘Artwork, handicraft’’, ‘‘Per-
STANDARD forming art, theater, traditional dance’’, ‘‘Music, Traditional
The need of representing the concepts of cultural heritage songs’’, ‘‘Festivities, traditions, customs’’, ‘‘Typical food,
that include not only artworks circumscribed in museums, culinary traditions’’, ‘‘Monuments, buildings, squares’’,
but even those elements of the city that make up the attention ‘‘Landscapes, countryside, nature reserves’’, ‘‘Sports, sport-
of urban tourism, both of concrete and abstract nature, is the ing events, children’s games’’, and ‘‘Language, dialects,
focus of this research. For this reason, experts in the area were phrases’’. We also included an open response alternative,
consulted about the elements that are considered as heritage, to learn about other relevant concepts proposed by the inter-
and then contrasted with the golden standard, which is the viewees, however we only received more specific concepts
base to identify those gaps that have been overlooked and that can be related to the more general concepts above
should be part of CURIOCITY ontology. (e.g., photography could be related to Artwork).
To do so, two questionnaires were preliminarily developed As shown in Figure 12, concepts with the greatest rele-
through online forms. Opinions of 10 participants, experts in vance to the idea of cultural heritage are those related to works

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FIGURE 12. Summary of answers to the question about concepts related
to Cultural Heritage.

FIGURE 11. OQuaRE evaluation summary.

TABLE 4. Participants demographic characteristics of the domain
location, spatial info’’, ‘‘Person or Group: author, creator,
knowledge evaluation questionnaire. culture’’, ‘‘Exhibition, gallery, display’’, ‘‘Curator, descrip-
tion, additional info’’, and ‘‘Material, color, shape’’; with
the purpose of identifying the basic concepts on which a
representation of cultural heritage elements should be based.
Again, we also included an open response alternative, to learn
about other descriptor elements proposed by the interviewees,
however we only received more specific concepts related to
the physical nature of an artifact (e.g., dimensions), these
concepts are included in Material category.
Figure 13 summarizes the results of the survey to this
question, from which it can be appreciated that the essen-
tial elements of the description are given by the space-time
pair; in second place the information needed corresponds to
the elements of the person, the exhibition and the material;
finally, additional supporting data to the curator is required
for representation of a cultural heritage element.
The questionnaire answers validate the necessity of an
extension for cultural heritage representation from a urban
tourism point of view, and validates our knowledge catego-
rization as the golden standard, which in turn is defined from
of art, monuments, and landscape, two of these three elements UNESCO’s cultural heritage classification (see Section II)
have a context related to outdoor environments (monuments and on described knowledge from evaluated ontologies (see
and landscape), clearly identified as points of interest in an Section III).
urban tourism context. The concepts related to performing As shown in Table 1, CURIOCITY addresses some of the
art, music, festivities, typical food, and language (dialects) gaps in cultural heritage representation in the context of a
are rated as very relevant. Out of these, only language is city and urban tourism, according to this established golden
not included in CURIOCITY. Finally, concepts related to standard and RUTAS project requirements.
events and sports are considered of medium-high relevance.
Sports related concepts can be adapted from performing art D. DISCUSSION
concepts. CURIOCITY ontology has been evaluated at three lev-
The second proposed question was: What information do els: Lexical, Structural, and Domain Knowledge. Lexical
you think is necessary to describe an element of Cultural level evaluation results show that CURIOCITY ontology has
Heritage?; the expert is asked to give a level of informa- around of 70% of similar concepts with CIDOC CRM base
tion necessity about: ‘‘Time: dates, periods, events’’, ‘‘Place: ontology (ERLANGEN CRM), indicating that a core has

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FIGURE 14. CURIOCITY framework architecture.

context of this project, data from indoor spaces (e.g., muse-
ums, historic churches, and libraries) and outdoor spaces
(e.g., historical sites, squares, monuments) are being col-
lected for art specialists in a repository called D-RUTAS.36
Towards the accomplishment of RUTAS project goals,
we have developed CURIOCITY framework [36], which is
FIGURE 13. Summary of answers to the question about information roughly composed by three layers (see Figure 14): (i) Seman-
needed for the description of a Cultural Heritage item.
tic Repository layer aimed at managing the semantic data,
such as the CURIOCITY repository, with information about
cultural heritage in urban tourism; other semantic repositories
been preserved allowing interoperability with the standard, can also be included, such as an ontology to represent tourists
and a glossary of common terms, which facilitates the under- (i.e., basic information, preferences, interests), an ontology
standing and usefulness of the proposal. to manage robots’ tasks (e.g., navigation, mapping, object
Structural Level has OQuaRE metrics as guideline, as sug- detection); (ii) Data Processing layer in charge of process-
gested by the evaluation methodology followed in this work. ing sources of information (e.g., D-RUTAS repository, web
CURIOCITY, ERLANGEN CRM, and ArCo characteristics pages, databases of museums, databases of government cul-
were compared. tural institutions) to automatically populate the semantic
CURIOCITY ontology shows similar conditions in com- repositories and generate a rich knowledge network; it also
parison with CIDOC CRM in the Structural and Functional processes and executes queries from the Application layer;
Adequacy characteristics, however CURIOCITY presents and (iii) Application layer, which provides interfaces for
better conditions for the rest of characteristics, which could maintenance, updates, and navigation through the semantic
be understood that CURIOCITY has a better performance in repositories; services such as online artwork catalogs, vir-
maintenance and learning issues than the CIDOC standard. tual museums, web forms to collect data, are offered at this
ArCo shows a less complex structure, whereas CURIOCITY layer. CURIOCITY framework code and documentation are
and ERLANGEN CRM ontologies have complexity as a available online.37
point to take into consideration for their use. On the other Next sections describe the components developed of the
hand, all three ontologies present an optimal domain repre- first prototype of CURIOCITY framework and explain how
sentation richness that translates into query consistency, good CURIOCITY ontology supports its functionality.
modularity, and adaptability.
Domain Knowledge level needs the intervention of domain
A. SEMANTIC REPOSITORY LAYER
experts; in this way, questionnaires were used to know their
Currently, the Semantic Repository layer has a CURIOC-
perception of the ontology’s adequacy. Results at this level,
ITY ontology base version, mainly consisting of the Low
show that concepts needed for an adequate representation of
Ontology, representing artworks and museums. This semantic
cultural heritage and urban tourism domain have been consid-
repository is automatically instanciated from the Data Pro-
ered. The perception of CURIOCITY ontology utility from a
cessing layer. In the current version, it has the information
preliminary test also returns favorable responses. However,
from four museums of Arequipa, Perú: Municipal Museum
further tests on the quality of results and proposed inferences
‘‘Guillermo Zegarra Meneses’’, Convent Museum ‘‘La Reco-
remain to be carried out.
leta’’, ‘‘Santa Catalina’’ Museum, and Convent Museum
‘‘Santa Teresa’’, transformed into triples from D-RUTAS
VI. CURIOCITY FRAMEWORK: AN APPLICATION CASE (see Section VI-B).
RUTAS project aims to develop a system of robots as tour
guides in urban centers, solving the problem of connectivity
between robots and providing access to tourist information 36 https://github.com/dulcineo
through a semantic repository available on the cloud. In the 37 https://github.com/giulianodelagala/CURIOCITY

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TABLE 5. Number of records processed from D-RUTAS and instantiated triples in CURIOCITY ontology.

TABLE 6. Summary of mapping concepts and properties from D-RUTAS museum data to CURIOCITY ontology.

At this level, Apache Jena Fuseki is used as the SPARQL mapping from museums description in D-RUTAS to classes
server, with TDB as the storage sublayer and Openllet38 as and properties of CURIOCITY ontology. As instance of the
the reasoner. Table 5 shows the number of records processed extended cultural heritage concept, the class cit:Site CH
from D-RUTAS and the number of triples (before and after is included to allow categorizing the museum and the city.
using the reasoner) that were generated. The largest number Also, cit:Description is included in order to improve
of instances obtained after applying the Pellet reasoner to organization of additional notes for narrative purposes as well
the knowledge base supported by the CURIOCITY ontol- as virtual catalogs implementation.
ogy (i.e., concepts, properties, inference rules), generates Table 7 summarizes the mapping from object data con-
new knowledge from the initial data. The knowledge base tained in museums (i.e., artworks description in D-RUTAS)
obtained from the different steps of the instantiation are avail- to CURIOCITY ontology. It takes as an example the artifact
able at CURIOCITY’s repository.39 ’Cesto de la Cultura Nazca’.

B. DATA PROCESSING LAYER 2) INSTANTIATION: CSV PARSER
This layer provides tools to automatically instantiate the The implementation process of the instantiation begins
Semantic Repository layer and generate SPARQL queries to with the spreadsheets containing the D-RUTAS museum
support the Application layer. Nevertheless, for the current data, which are exported to CSV format for manipula-
version of the framework, before using these tools, it is nec- tion. Parsing and generation of the RDF triplets is done
essary to perform a manual mapping process of D-RUTAS through a parser developed with Python 3.8, Pandas 1.1.2,
concepts to CURIOCITY ontology classes and relations. and RDFlib 5.0.0 libraries. D-RUTAS data are pro-
This mapping configuration can be saved in a JSON file for cessed line by line, starting with general concepts with
later use. In the future, this mapping process will be also multiple references (e.g., crm:E55 Type, crm:E44
automated, by using, for example, string similarity, string Material, crm:E58 Measurement Unit). Then,
matching, or natural language processing. specific concepts are matched to the artifact (e.g., crm:E22
Man-Made Object, crm:E54 Dimension). Lastly,
1) MAPPING D-RUTAS TO CURIOCITY ONTOLOGY
the triplets corresponding to properties that relate the previous
The mapping process consists of matching the D-RUTAS data concepts are generated.
contained in MS Excel spreadsheets to corresponding CURI- During the instantiation, some drawbacks were detected
OCITY ontology entities. Table 6 shows a summary of the and overcome: (i) incomplete data: some fields of D-RUTAS
38 https://github.com/Galigator/openllet tables are identified as Unknown or Missing Data; since these
39 https://github.com/giulianodelagala/CURIOCITY/tree/master/ data are not represented in the knowledge base, it is necessary
Instances to represent this empty attributes in the query response with

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TABLE 7. Summary of mapping concepts and properties from D-RUTAS artwork data to CURIOCITY ontology.

some text such as ’unknown’ to identify the missing infor- in museums. The second application (Figure 16 (b)) offers
mation; (ii) fields that can refer to different classes (e.g., the a 3D tour of virtual museums for end users, as well as a
author of a work can be a crm:Person or crm:Group); tour creator utility for developers to design and implement
they demand user intervention to specify the corresponding virtual visits to museums from the data and information kept
class through a dialog box; and (iii) homonymy problems: a in the semantic repositories. The third service (Figure 16 (c))
posteriori review by specialists is necessary to correct these is a desktop application oriented to the administration of the
errors in the semantic repository. semantic repository. The functionalities of this application
Relationships between different concepts by means of allow administrators to configure the mapping of CSV tables
the properties, both previously identified, are illustrated in (entities of the ontology and the generation of instances in
Figure 15, from the example shown in Table 7. an automated way from the data), to make queries to the
semantic repository under combined criteria, to make partic-
3) SPARQL QUERIES ular queries in SPARQL format, and to update the semantic
The Data Processing layer receives queries from the Appli- repository manually.
cation layer that are transformed into SPARQL queries and These applications allow final users to get information
processed at the Semantic Repository layer. Query results are about museums of Arequipa, Perú, through graphical inter-
returned in JSON format back to the Application layer, which faces that automatically transform their requirements into
shows them at the user interface. The reasoner integrated queries and respective results, supported by the SPARQL
at the Semantic Repository layer gives the benefit that the QUERY Engine at the Data Processing layer, that in
queries can obtain inferred information. Some of the imple- turn accesses the proper repository at the Semantic Repos-
mented queries are available at CURIOCITY repository.40 itory layer. Similarly, the applications for developers
(e.g., tour creator and desktop admin) are supported by graph-
C. APPLICATION LAYER ical interfaces at the Application layer, that can access APIs,
The current version of CURIOCITY framework offers three engines, scripts, etc. provided by the Data Processing layer,
services for end users. The first one (Figure 16 (a)), is a in order to query or manage the Semantic Repository.
general user oriented web page that allows users to browse The application experience of CURIOCITY ontology
the semantic repository, perform queries for searching under shows our progress in developing use cases and represents
combined criteria, and obtain details of artifacts contained an indicator towards the successful realization of our require-
ments. We have shown that final users, as well as developers,
40 https://github.com/giulianodelagala/CURIOCITY/tree/master/Querys can transparently access the Semantic Repository, through the

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FIGURE 15. Mapping museum data to CURIOCITY ontology.

FIGURE 16. Front-end applications.

utilities provided at the Processing Data layer (e.g., SPARQL our proposed categorization of the domain knowledge and
engine, scripts, API, parsers) from comfortable and easy-to- RUTAS project requirements. Moreover, we introduce and
use graphical interfaces at the Application layer. Thus, it is developed a very first version of CURIOCITY framework,
possible to represent the knowledge of cultural heritage and to show the suitability of the ontology in a case study of
urban tourism domains of a city; besides being a semantic four museums of Arequipa, Perú. The Data Processing layer
base for CURIOCITY framework services, in order to con- allows the transformation of data from excel to RDF triples
form a level of abstraction of knowledge and use of semantic of CURIOCITY ontology (Semantic Repository layer), gen-
web technologies for final users. erating a richer repository. The Semantic Repository layer is
the base of services and applications, such as on line catalog
VII. CONCLUSION AND FUTURE WORK and virtual museum. In this scenario, we demonstrate that,
In this paper, we present a new formal representation of by using CURIOCITY ontology, it is possible to represent the
cultural heritage knowledge in the context of urban tourism. knowledge of cultural heritage and urban tourism domains of
We describe the ontology CURIOCITY (Cultural Heritage a city, as the basis for developing interoperable services and
for Urban Tourism in Indoor/Outdoor environments of the applications.
CITY) to represent the cultural heritage knowledge, fol- Thus, this work represent novelty solutions for researchers
lowing the UNESCO classification and mainly based on and experts in this area from several perspectives: (i) CURI-
CIDOC CRM to keep the interoperability among applica- OCITY ontology offers new opportunities for the devel-
tions. Following a methodological process, we evaluate and opment of flexible, intelligent, and interoperable services
compare CURIOCITY ontology, showing that it is able of and applications in the tourism domain; this represents a
representing all aspects of cultural heritage, according to step towards more empowered semantic e-tourism services;

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[80] S. Schulz, M. Boeker, D. Raufie, and D. Schober, ‘‘GoodOD—Good YUDITH CARDINALE received the degree
ontology design,’’ Tech. Rep., 2012. Accessed: May 6, 2021. (Hons.) in computer engineering from the Uni-
[81] M. Fernández-López, A. Gómez-Pérez, and N. Juristo, ‘‘METHONTOL- versidad Centro-Occidental Lisandro Alvarado,
OGY: From ontological art towards ontological engineering,’’ in Proc. Venezuela, in 1990, and the M.Sc. and Ph.D.
Ontological Eng. AAAI Spring Symp. Ser. Menlo Park, CA, USA: Ameri- degrees in computer science from the USB,
can Asociation for Artificial Intelligence, Mar. 1997. [Online]. Available: Venezuela, in 1993 and 2004, respectively. She has
https://oa.upm.es/5484/ been a Full Professor with the Computer Science
[82] Time Ontology in OWL, W3C, Cambridge, MA, USA, 2020. Accessed:
Department, Universidad Simón Bolívar (USB),
May 6, 2021.
Venezuela, since 1996, and an Associate Professor
[83] CRMgeo Spatiotempotal Model, ICOM, Paris, France, 2020. Accessed:
May 6, 2021. and a Researcher at the Universidad Internacional
[84] G.-A. Nys, M. V. Ruymbeke, and R. Billen, ‘‘Spatio-temporal reasoning de Valencia, Spain, since 2019. She is currently an Associate Researcher
in CIDOC CRM: An hybrid ontology with GeoSPARQL and OWL-time,’’ at the Universidad Católica San Pablo, Arequipa, Perú. She has written
in Proc. CEUR Workshop, vol. 2230. Aachen, Germany: RWTH Aachen a range of scientific papers published in international journal, books, and
Univ., 2018, pp. 1–14. conferences, and has participated as a member of program committees of
[85] A. M. Musen, ‘‘The protégé project: A look back and a look forward,’’ AI several international conferences and journals. Her research interests include
Matters, vol. 1, no. 4, pp. 4–12, Jun. 2015. parallel processing, distributed object processing, operating systems, digital
[86] A. Ulanov, G. Shevlyakov, N. Lyubomishchenko, P. Mehra, and A. Polutin, ecosystems, high-performance on grid and cloud platforms, collaborative
‘‘Monte Carlo study of taxonomy evaluation,’’ in Proc. Workshops frameworks, and web services composition, including semantic web.
Database Expert Syst. Appl., 2010, pp. 164–168.
[87] A. Duque-Ramos, J. Fernandez-Breis, R. Stevens, and N. Aussenac-Gilles,
‘‘OQuaRE: A square-based approach for evaluating the quality of ontolo- IRVIN DONGO received the B.Sc. degree
gies,’’ J. Res. Pract. Inf. Technol., vol. 43, pp. 159–176, May 2011. in computer science from the Catholic San
[88] B. Lantow, ‘‘Ontometrics: Application of on-line ontology metric calcula- Pablo University, Peru, in 2012, and the M.Sc.
tion,’’ in Proc. BIR Workshops, vol. 1684, 2016, pp. 1–12. and Ph.D. degrees from the University of
[89] M. Poveda-Villalón, M. C. Suárez-Figueroa, and A. Gómez-Pérez, ‘‘Val- Pau, France, in 2014 and 2017, respectively.
idating ontologies with oops!’’ in Proc. Int. Conf. Knowl. Eng. Knowl. From 2018 to 2020, he was a Postdoctoral Fel-
Manage. Berlin, Germany: Springer, 2012, pp. 267–281. low at the École Supérieure des Technologies
[90] N. Jian, W. Hu, G. Cheng, and Y. Qu, ‘‘Falcon-AO: Aligning ontologies Industrielles Avancées (ESTIA), France. He is
with Falcon,’’ in Proc. Workshop Integrating Ontologies, 2005, pp. 87–93. currently an Associate Researcher in computer
[91] M. A. Cornejo-Lupa, R. P. Ticona-Herrera, Y. Cardinale, and science at the ESTIA and at Catholic San Pablo
D. Barrios-Aranibar, ‘‘A survey of ontologies for simultaneous localization
University. His research interests include normalization and anonymization
and mapping in mobile robots,’’ ACM Comput. Surv., vol. 53, no. 5,
of Web resources, knowledge-bases modeling (semantic web), policies and
pp. 1–26, Oct. 2020.
[92] H. J. M. Munoz and Y. Cardinale, ‘‘GENTE: An ontology to represent users management of credentials, security model and anonymization technique,
in the tourism context,’’ in Proc. 47th Latin Amer. Comput. Conf. (CLEI), and machine/deep learning techniques for an analysis and classification of
Oct. 2021, pp. 1–10. data to discover patterns, gesture recognition, and affective computing.

REGINA TICONA-HERRERA received the
degree (Hons.) in computer science from the
Universidad Católica San Pablo (UCSP), Peru,
the M.B.A. degree from the Universidad de
Mondragon, Spain, in 2006, and the Ph.D. degree
in computer science from the Université de Pau
ALEXANDER PINTO received the degree (Hons.) et des Pays de l’Adour, France, in 2016. She is
in computer science from the Universidad Católica currently an Associate Professor with the Com-
San Pablo, Perú, and the B.Sc. degree in civil puter Science Department, UCSP. She is also a
engineering from the Universidad Nacional de San Postgraduate Director of the UCSP and an Asso-
Agustín, Perú. He is currently pursuing the Ph.D. ciate Researcher and a Visiting Professor at the École Supérieure des
degree in computer science with the Engineering Technologies Industrielles Avancées (ESTIA), Bidart, France. Her research
Sciences Program, Pontificia Universidad Católica interests include semantic web, big data, information retrieval, databases, and
de Chile. His research interests include semantic tangible interfaces. She has participated as a member of program committees
web, graph theory, machine learning, and audio of international conferences.
signal processing.

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