=Paper= {{Paper |id=Vol-2365/08-TwinTalks-DHN2019_paper_8 |storemode=property |title=Twinning Classics and A.I.: Building the new generation of ontology-based lexicographical tools and resources for Humanists on the Semantic Web |pdfUrl=https://ceur-ws.org/Vol-2365/08-TwinTalks-DHN2019_paper_8.pdf |volume=Vol-2365 |authors=Maria Papadopoulou,Christophe Roche |dblpUrl=https://dblp.org/rec/conf/dhn/PapadopoulouR19 }} ==Twinning Classics and A.I.: Building the new generation of ontology-based lexicographical tools and resources for Humanists on the Semantic Web== https://ceur-ws.org/Vol-2365/08-TwinTalks-DHN2019_paper_8.pdf
Twinning Classics and A.I.: Building the new generation
of ontology-based lexicographical tools and resources for
            Humanists on the Semantic Web

                     Maria Papadopoulou1,2 and Christophe Roche1,2
                           1 University Savoie Mont-Blanc, France
                              2 Liaocheng University, China

                         firstname.lastname@univ-smb.fr



       Abstract. This Twin Talk is about the ongoing collaboration between an expert
       in Classics and an expert in Artificial Intelligence (A.I.). Our approach set out to
       answer two interlinked issues, ubiquitous in the study of material culture: first,
       pairing things to their names (designations) and, second, having access to multi-
       lingual digital resources that provide information on things and their designa-
       tions. Our chosen domain of application was ancient Greek dress, an iconic fea-
       ture of ancient Greek culture offering a privileged window into the Greek belief
       systems and societal values. Our goal was to place the Humanist/domain expert
       at the centre of the endeavour enabling her to build the formal domain ontology,
       without requiring the assistance of an ontology engineer. The role of A.I. was to
       provide automations that lower the cognitive load for users unfamiliar with
       knowledge modelling. Building the model consisted in distinguishing between
       concept level (i.e. the stable domain knowledge) and term level (i.e. the terms
       that name the concepts in different natural languages), putting these into relation
       (i.e. linking the terms in different languages to their denoted concepts), and
       providing complete and consistent definitions for concepts (in formal language)
       and terms (in natural language).

       Keywords: ancient Greek dress, ontology, terminology.


1      Introduction

   The proposed Twin Talk is about the story of an interdisciplinary collaboration be-
tween an expert in the Humanities (Classics) and an expert in digital technology (Arti-
ficial Intelligence) working together to answer two interlinked issues, ubiquitous in the
study of material culture, broadly defined as “the investigation of the relationship be-
tween people and things irrespective of time and space” [1]: first, pairing things to their
names (designations) and, second, having access to multilingual digital resources that
provide information on things and their designations. Our chosen domain was ancient
Greek dress, an iconic feature of ancient Greek culture which offers a privileged win-
dow into the Greek belief systems and societal values. The challenge was triple:
68


a/ deal with the complex history of terms designating ancient Greek dress, some inher-
ited from ancient times, others coined by scholarship dating since the Renaissance [2-
3].
b/ define concepts formally, yet in a way that would be intuitive to the Humanist-Clas-
sical scholar, enabling her to do the ontological modelling on her own.
c/ model domain concepts and terms providing definitions for both (i.e., formal defini-
tions for concepts; natural language definitions for terms, based on the concept desig-
nated by each term).

   The paper is organized in five sections: Section 1 is the Introduction. Section 2 pre-
sents the chronicle of this ongoing collaboration. Section 3 introduces the problem ad-
dressed and explicates the solution given and the reasons for choosing it. Section 4
provides record of the fruit of the collaboration. Section 5 relates the particulars of the
collaboration experience. Finally, section 6 reports on the lessons learnt and suggests a
number of good practices.


2      Teaming up: the Classical scholar and the Artificial
       Intelligence (A.I.) expert

   Our team of two is made up of researchers at different career stages and with differ-
ent academic backgrounds, coming from disciplines as disparate as Classics and A.I. It
was initially formed with the aim to combine our diverse expertise in lexicography,
classics and dress studies (Classicist), terminology, ontology and A.I. (digital expert)
to model knowledge and terminology used to express this knowledge in the domain of
ancient Greek dress. Our working hypotheses are as follows: a/ while ancient Greek
garments and their names are culture-specific, ancient Greek dress concepts can be de-
scribed in a context-free, formal manner that enables sharing them across different nat-
ural languages; b/ concepts are defined by a set of essential characteristic known to
domain experts, traceable in texts, and visible through the representations of dress in
sculptures, painted vases, coins, etc. (a characteristic is essential for an object iff, when
removed from the object, the object is no more what it is. For example, ‘without
sleeves’ is an essential characteristic for an exomis)

   We first met at the 2013 Terminology and Ontology: Theories and Application
(TOTh) workshop organized at the University of Copenhagen, where we both contrib-
uted papers [4]. This was a happy coincidence that kick-started a series of academic
exchanges: the Humanist expressed the grave problem she encountered when dealing
with the terminology of dress in her domain. The Digital expert promised that this was
feasible and started to explain why. Soon they realized that this problem was part of a
wider problem facing the whole community that worked with textile and dress termi-
nology, which seemed unsolvable for decades: textiles and dress scholars need to stand-
ardize the language used in order to communicate knowledge about the objects of the
domain, so that everybody understands the same thing.
                                                                                         69


   After the launching of the Humanist’s Marie Curie Fellowship at the University of
Copenhagen (2015-2017), the team started working on modelling the domain of ancient
Greek dress. This work was a deliverable of the Humanist’s two year project, as shown
in the final project report [5]. In January 2017 the Humanist joined team Condillac-
LISTIC lab, at the University of Savoie, France. Condillac was founded by the Digital
expert several years back [6]. It is an international and interdisciplinary team primarily
of computer scientists and linguists working on Knowledge Representation. In Novem-
ber 2017 a new research center was opened at the Computer Department of Liaocheng
University, China [7] and the Digital expert asked the Humanist to present their work
and give lectures on Digital Humanities in China, so that more students and researchers
in Computer Science as well as in the Arts and Humanities would become familiar with
the idea of embarking on digital humanities projects individually or in teams made up
of a humanist and a computer scientist. The two researchers’ collaboration is ongoing
in the context of both Condillac and KETRC.


3      Problem and solution

       3.1 The problem of ‘naming things’ in the experts’ own words: a
       terminology and knowledge modeling issue

   Scholars vividly express a need, omnipresent in the study of material culture termi-
nology, whether the research area is ancient Greek dress (cf. d, e, h, j), dress of medieval
Scandinavia (cf. c), Greek material culture (cf. a), ancient Egyptian art (cf. b, k), clay
pottery from different cultures (cf. f, g, i) to:
    i) Determine what term goes with what object combining textual, iconographic, ma-
terial sources:
   a. “Only studies that combine archaeological and iconographic data with knowledge
derived from texts give the opportunity to correlate a word with an object.” [8]
   b. “In our case, the content of the terminology pertains to two fields: objects and
pictures. When saying "objects", I mean the entire material culture ... the terms we are
looking for pertain, obviously not to the specimens existing in reality, but to every oc-
curring type of objects and buildings. We have to do here with a list of designations of
things.” [9]
   c. “Research into dress history, whether the approach is founded in history, art or
archaeology, incorporates terminology, one way or another.” [10]
   ii) Adopt a standard common vocabulary of terms and definitions to promote re-
search in their field:
   d. “Although the standard Greek and Latin terminology employed by scholars to
describe ancient clothing may not be that which was used in antiquity … it is a useful
vocabulary of dress and will be used here.” [13]
   e. “Studies of garment-terms in historical societies tend to be hampered by a lack of
understanding of the specific vocabulary of dress.” [11]
70


    f. “…it would help if we could work out a list of standard vessel shapes, clearly
defined and illustrated, and a set of terms for them.” [12]
    g. “An intelligent discussion of pottery shapes is rendered more difficult by lack of
definitive nomenclature.” [13]
    h. “… Arabic terms for specific veil types (words like shaal, maghmuq, and lithma)
… will be used to identify certain ancient Greek veil-styles. This might not be the most
satisfactory answer, but at least it is expedient: we need to adopt a common workable
veil-vocabulary so that our investigation of the Greek veil can proceed without further
complication or impediment.” [11]
    iii) Have access to diachronic multilingual resources providing information on things
and their names:
    i. “…that we seek standardized terms for ceramic vessels expresses what I feel to be
a real need…develop multilingual vocabularies of technical terms” [14]
    j. “Creating a diachronic and global costume term base…is of considerable value for
textile terminology.” [15]
    k. The chief aim of a terminology is efficient communication among specialists when
discussing matters orally or in written form, efficient organisation of data banks, and -
a point of particular importance - successful communication among electronic data
banks. [9]
    Dress scholars have attempted to unravel the complexity of dress terminology [17-
18] and produce a classification of clothing in order to meet the need for a transcultural
denomination system for clothing parts, but the domain of ancient Greek dress has
never been described using a stable vocabulary [19-22]. Contemporary needs for ma-
chine tractable data on the Web impel the use of software artefacts, i.e., controlled vo-
cabularies, thesauri, ontologies, to structure domain knowledge. Yet, existing thesauri,
i.e., the Getty Art and Architecture Thesaurus (AAT) [23], ontologies of the domain of
dress [24], the CIDOC CRM, which provides definitions and a formal structure for
describing concepts and relationships in cultural heritage documentation [25], do not
cover the needs of scholars interested in ancient Greek dress, as they do not include any
Greek terms, apart from chlamys(es), chiton(s), peplos(es) and himation(s) in AAT.
                                                                                           71




                        Fig. 1: Art & Architecture Thesaurus s.v. chitons


       3.2 The solution: empowering the Classicist

Our aim was first, to match the names (terms) to the objects (concepts) of the domain
by defining them with consistency, then, structure and publish these terminological data
as shareable and reusable Linked Open Data with the help of a software platform that
would build the concept system based on defining concepts as sets of essential charac-
teristics, in compliance to ISO 108) [16]. We wished to achieve the above tasks using
workflows and tools that empower classicists and humanists by matching their way of
thinking and working, not the way of thinking and working of Semantic Web experts
and developers. The domain of application was ancient Greek dress and its culture-
specific terminology. Its importance as a social marker or as representative of the ma-
terials, techniques and technological know-how of a given era is unquestionable. Un-
raveling the intricacies of Greek dress terms, building the concept system of this do-
main, and publishing both terms and concepts as Linked Open Data, is to be the first
step towards making this knowledge easily discoverable and reusable.
    Our ontological modelling is informed by a theory of concept inspired by the inter-
national standards on terminology [16, 26]. According to ISO 1087 [16] concept is a
“unit of knowledge created by a unique combination of characteristics”; characteristic
is an “abstraction of a property of an object or of a set of objects; essential characteristic
is a characteristic “indispensable to understanding a concept”. Identify and define what
72


the objects and what the terms that designate them are, are constant ontological con-
cerns in both textual and object-based research of Greek antiquity. The inclination and
capability to clas-
sify,     sub-classify
and use the right
terms is the dividing
line between an ex-
pert and a lay per-
son. As we have al-
ready shown [27-
28], experts in an-
cient Greek dress
and other domains
of cultural heritage
regret the “termino-
logical vagueness”
that prevails in their
respective      fields,      Fig. 2: Naming Things in the domain of ancient Greek dress
blame it for hinder-
ing their work, and emphasize the need for consistent and consensual use of terms. As
archaeological finds come down to us like a picture book without names, and as texts
furnish names for objects, without providing illustrations, deciding “what was what’ is
not easy. Names, what the discipline of Terminology calls “terms”, provide shortcuts
to communicating what things are. Terms are words that belong in a domain-specific
language, not general language [29]. Terms mediate between concepts and language. A
term cannot exist without a concept, while a concept may have a verbal expression in
Language A, but not in Language B. Concepts are abstracted from individual objects.
They are the layer that mediates between ‘reality’, where objects live, and language,
where terms communicate meaning about concepts. Concepts are bits of knowledge
about the world, while terms are bits of language to verbally express this knowledge.
   The move from linguistic-textual discursive to extra-linguistic meaning is important,
when defining extra-linguistic entities, i.e., concepts and objects [30-32]. Our proposed
solution for the domain of Greek dress, but also for experts seeking to “name things”
in their respective domain, is to define the terms of the domain in relation to the con-
cepts designated by these terms and build their term list in connection to the ontology
made up by a concept isa hierarchy, whereby each concept (and term labelling this
concept in a natural language) is made up of a list of characteristics, following the ISO
1087 standard for terminology work.

   The role of Artificial Intelligence (A.I.) in this endeavour is to reduce the complexity
of representing concept characteristics, concepts and terms and provide helpful auto-
mations to bridge the gap between classicists/humanists and the Semantic Web, while
promoting two good practices for developing methodologies and tools in Digital Hu-
manities: intuitiveness (of the theoretical and methodological framework to be devel-
                                                                                          73


oped), ease-of-use (of the software platform to be developed). To illustrate the com-
plexity of modelling: i.e. 10 differences leading to forming disjoint categories (e.g. with
or without sleeves), suffice to end up with a Porphyry tree of 1024 (2 10) terminal con-
cepts. Selecting a terminal concept out of this complexity requires the aid of the ma-
chine.
    Ontologies (in Computer Science) have been around for the last forty years or so
[33-34] and OWL ontologies have been around since 2004. They are the best tool to
describe domain knowledge, publish metadata compliant with Semantic Web and
Linked Data standards, annotate resources, and query knowledge bases; they are the
backbone of the Semantic Web [35]. But the standard language for ontologies in the
Semantic Web is OWL (Web Ontology Language) [36]. Modelling in OWL using Pro-
tégé [37-38] (or another platform for editing [39]) requires reasoning in Description
Logics.
    The reasons we opted out of building an OWL ontology in Protégé are both episte-
mological & practical: first, reasoning in OWL using Protégé means reasoning in role
restrictions, classes and individuals, data properties, object properties, A-box and T-
box, which is hardly intuitive to those with no background in Description Logics and
does not match the way classicists/humanists work. To do the modelling in this way,
domain experts either need an ontology engineer, or have to think like one. Second,
research has shown that human users do not fare well with highly formal systems, un-
less they have background in Computer science [40-42]. The Semantic Web is based
on logical reason-
ing (first order
logic), which re-
quires a highly de-
gree of formaliza-
tion. The use of a
formal language
with clearly speci-
fied syntax and se-
mantics, such as
Description Logics
at the heart of Se-
mantic Web ontol-
ogies guarantees
the consistency of
definitions and the
possibility to rea-
son on these mod-
els, but is not con-
sensus-oriented. It
is much more intu-         Fig. 3: Traceable knowledge primitives in the LSJ definition of chiton
itive to humanists
to define the objects of the domain in terms of knowledge primitives that can be traced
74


in dictionary definitions, primary texts & archaeological objects. Fig. 3-4 illustrate the




            Fig. 4: Traceable knowledge primitives in the LSJ definition of exomis

knowledge primitives of chiton and exomis respectively traceable in its definition in
LSJ [43-44, the standard dictionary used by classical scholars. These are: worn next to
the skin, (initially) by men, (later also) by women. Additional characteristics subdivid-
ing this type of Greek garment into subtypes are included in the LSJ definition.
Knowledge primitives can be a firm basis for consensus-reaching discussions among
domain experts. Domain experts should be given common ground for agreeing (or dis-
agreeing) on the definitions of terms and concepts. Description Logics does not guar-
antee a common basis upon which a dialogue among experts can exist. In contrast,
semantic primitives of concepts can form a stable basis for scholarly discussions on the
meaning of concepts and terms.
   In order to build our domain ontology, we used Tedi [45], a software developed by
the digital expert, which empowers domain experts. Tedi software supports both term
standardization and customization. Standardization of terminologies relies upon expert
agreement on domain knowledge, which is necessary for collaboration and rapid shar-
ing of information. Customization accommodates and preserves the diversity of terms
across languages. Tedi’s complex architecture deploys two interconnected levels:
   - the formal domain ontology level, which consists of an editor for concepts and an
editor for objects. The editors of attributes, relations, and axes of analysis are accessible
by means of the concept editor.
   - the terminology level, which consists of an editor for terms and an editor of proper
names.
   For the user’s convenience the interfaces are color coded: green for the conceptual
dimension, blue for the linguistic dimension. Tedi allows ontoterminologies to be ex-
ported in different formats human readable, as well as machine tractable and Semantic
Web compliant: HTML (static and dynamic), RDF/OWL, SKOS, JSON, and CSV.


       3.3 A new scholarly workflow for building definitions for things

  The Tedi tool-based method for building multilingual ontoterminologies is com-
posed of 5 interrelated tasks, which do not necessarily have to be performed in a linear
                                                                                      75


fashion. The first step is to define the concepts of this complex domain in a formal
language by means of specific axes of analysis. The next step is to associate each term
with the concept made of the chunks of knowledge essential to defining it. Such mod-
elling can structure knowledge so as to eventually support two types of queries: by
means of keywords, and by means of concepts. Fig. 5 illustrates the linking of concepts
to terms by means of selecting essential characteristics.




                    Fig. 5: Selecting the essential characteristics for exomis

   The ontology has led to the building of an ontology-based online dictionary, whose
definitions of term were definitions of thing. Using the example of the exomis, we have
arrived at the following definitions, in English, French and Modern Greek:
“exomis” : Short and non-pleated garment for man, usually worn around the body di-
rectly on the skin, this sleeveless garment consists of two pieces of cloth sewn together
along the sides, attached on the left shoulder leaving the right shoulder and part of the
chest naked.
“Exomide” : Vêtement de corps pour homme, court, non-plissé et sans manches. Com-
posé de deux pièces cousues le long des côtés, attaché sur l’épaule gauche laissant
l’épaule droite et une partie de la poitrine nues, il est généralement porté directement
sur la peau.
“Eξωμίδα” : Κοντό, χωρίς πτυχώσεις και χωρίς μανίκια ανδρικό ένδυμα, το οποίο
συνήθως φοριόταν ως κυρίως ένδυμα. Αποτελούνταν από δύο κομμάτια υφάσματος
ραμμένα στα πλάγια και στερεωμένα στον αριστερό ώμο που άφηναν τον δεξί ώμο
καθώς και μέρος του στήθους ακάλυπτα.
The LSJ, the bilingual Greek-English dictionary commonly used in the field of classical
studies, defines exomis as a “tunic with one sleeve”. This definition is not only incom-
plete, but also problematic with regard to the notion of “sleeve”: “The adjective am-
phimaschalos attributed to the Greek chiton in no way implies the idea of sleeves, but
only, by its very etymology, that of the two armpits ... it is abusively, in my opinion,
76


that our translators or lexicographers speak of ‘sleeve’ tunics when it is a tunic with
two armholes.” [46, our translation].
   This approach led to building thing definitions, i.e. definitions of the concept denoted
by the term, and was not aimed at representing term meanings in discourse. The result
is precise and complete formalized knowledge allowing to verify logical properties for
multilingual semantic searches and semantic annotations. The objective of our ap-
proach is not to impose definitions, but to propose definitions (in natural language) that
are based on domain knowledge. This approach allows experts to discuss objectively
on the basis of the essential characteristics on which they generally agree. Fig. 6-7 show
the definition for exomis as exported in fully human readable and machine processible
exports (dynamic HTML and OWL respectively). The Tedi Onto-Dictionary of terms
and concepts will be deposited in Clarin.




            Fig. 6: Tedi export of the entry for term “exomis” in dynamic HTML
                                                                                         77




                         Fig. 7: A fragment of the ontology in OWL


4      Academic output & other achievements

The model for collaboration between a classical scholar and an Artificial Intelligence
expert has numerous achievements to show for:
 The Onto-dictionary of ancient Greek dress;
 A new software for onto-terminologies standalone and in a proprietary language;
 A more recent attempt to model the domain of ancient Greek vases [47];
 Disseminating the idea of interdisciplinary collaboration between Humanists and
  Digital experts by means of Condillac-LISTIC (France) and KETRC (China).
 Testing their idea for ontological modelling of terminologies from Humanities’ dis-
  ciplines with colleagues from different communities (in international conferences
  and peer-reviewed journals) including a best paper award [51]: archaeologists (Insti-
  tut français d’archéologie orientale-Cairo 30-31 October 2016), digital classicists and
  digital humanists (European Association for Digital Humanities 2018) [48], termi-
  nologists (TOTh 2016) [27], information scientists-librarians-archivists (AIDAin-
  formazioni Journal) [28], translators-lexicographers-linguists (Lexicologie Termi-
  nologie Traduction-LTT) [49], Artificial Intelligence experts (Revue Intelligence Ar-
  tificielle special issue on DH and AI) [50], computer scientists (several papers given
  in China [7], Semapro 2018 [51]).


5        The collaboration experience

       5.1 The good stuff: a mutually empowering experience

    Cambridge English Dictionary defines collaboration as “the situation of two or more
people working together to create or achieve the same thing” [52]. Our collaboration
flourished thanks to our positive attitude and openness. We agreed on the research goal,
specific objectives, approaches, and methodology. Especially because this was a cross-
border collaboration, online meetings were scheduled at regular intervals, having spec-
ified the details of the agenda beforehand. In terms of accountability, both researchers
accepted full responsibility for the actions, as well as to disclose the results in a trans-
parent manner. Our collaboration is informed by the principles laid out in the European
Code of Conduct for Research Integrity [53].
78


       5.2 The challenging stuff

In 1993 Turner and Cochrane [54] suggested that there are four types of projects ac-
cording to two parameters: how well defined their goals are, and how well defined the
methods of achieving them are. In our collaborative project the goal was clearer to the
classical scholar and the method to the digital expert to start with. Each one had to
familiarize oneself with the part which was less clear: the humanist had to cultivate the
capacity to operate at a representational level involving types and instances. The digital
expert had to adjust to the particularities, uncertainties and gaps in knowledge and in-
formation that are common when dealing with past cultures.


6        Suggestions for good practice

The first lesson learnt was that team work is mutually enriching and empowering. The
second lesson was that the more one practices interdisciplinary collaborative research,
the better one becomes at it. The third lesson is that if a digital solution is offered to
Humanists, it should cater to the specific needs of the target community.
    Collaboration is common practice among digital humanists. According to a recent
study “digital humanities researchers engage regularly in collaborative research. One
out of three respondents indicate that they collaborate very often with others on a re-
search project. Altogether, seven out of ten say that they engage often or very often in
research collaboration” [55]. If indeed practice makes perfect, digital humanists are
well equipped towards setting up collaborations.
    Knowledge modelling is interdisciplinary by definition: “In recent years the devel-
opment of ontologies has been moving from the realm of Artificial-Intelligence labor-
atories to the desktops of domain experts” [56]. Making cultural heritage term-lists
computable in order to link them to other types of resources (e.g., museum objects) is
a problem-driven question (as is Ontology Engineering par excellence), not a curiosity-
driven one, as in much of the research done in Classics and the Humanities. Our ap-
proach aims to show that in order to build workflows and tools that are better suited to
the needs of the targeted community, similar interdisciplinary teams are a necessity.
We advocate capturing domain knowledge with the help of domain experts, when
building ontologies or terminologies whose conceptual system is a formal domain on-
tology.
    How can scholars and digital experts maximize benefits from such collaborations?
The answer is to provide training on how to change the way of thinking, i.e. training
Computer Scientists on how to think like a Humanist (i.e., a researcher who seeks to
understand and analyze how humanity manifests itself in different periods, cultures,
media etc.) and train Humanities’ scholars on how to think like a Computer scientist
(i.e., someone who develops digital tools and media for real-life problem solving). Get-
ting to understand each other’s way of thinking raises awareness and improves not only
the product, but also the process of the collaboration.
                                                                                               79


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