=Paper=
{{Paper
|id=Vol-2949/paper6
|storemode=property
|title=Towards the Representation of Claims in Ontologies for the Digital Humanities
|pdfUrl=https://ceur-ws.org/Vol-2949/paper6.pdf
|volume=Vol-2949
|authors=Salvatore Cristofaro,Emilio M. Sanfilippo,Pietro Sichera,Daria Spampinato
|dblpUrl=https://dblp.org/rec/conf/swodch/CristofaroSSS21
}}
==Towards the Representation of Claims in Ontologies for the Digital Humanities==
https://ceur-ws.org/Vol-2949/paper6.pdf
Towards the Representation of Claims in
Ontologies for the Digital Humanities
Salvatore Cristofaro1 , Emilio M. Sanfilippo2
Pietro Sichera1 , and Daria Spampinato1
1
Institute of Cognitive Sciences and Technologies - CNR,
via Paolo Gaifami 18, 95126 Catania, Italy
{salvatore.cristofaro,pietro.sichera}@istc.cnr.it,
daria.spampinato@cnr.it
2
Institute of Cognitive Sciences and Technologies - CNR
Laboratory for Applied Ontology,
via alla Cascata 56/C, 38123 Trento, Italy
emilio.sanfilippo@cnr.it
Abstract. Knowledge and data in the human sciences are sometimes ex-
pressed in hypothetical or even incompatible terms. One wonders there-
fore how to make sense of them in ontological modeling frameworks.
Accordingly, we present in the paper some preliminary ideas to make
ontologies for the Digital Humanities able to deal with hypothetical and
incompatible scholarly statements, which we call claims. Our proposal
builds on existing works in the state of the art. The results are still
preliminary; the contribution is more on the definition of the problem
and identification of the challenges rather than on the modeling itself,
therefore future work to strengthen our proposal is necessary.
Keywords: Ontology · Uncertainty · Claims · Digital Humanities · Cul-
tural Heritage · Vincenzo Bellini
1 Introduction
Research in the humanities, especially when focused on the study of past epochs,
is often based on partial or uncertain data. Simple examples are persons’ bio-
graphical elements such as birth or death dates. For instance, we know that
Dante was born between May 21st and June 21st, 1265 although we do not
currently know the exact birth date. In some other cases, we even have less pre-
cise information knowing, e.g., only the century in which something happened.
There are plenty of similar examples including hypothetical knowledge about,
e.g., artworks’ authorship, cities’ names or their archaeological provenance, just
to mention some common examples [9]. In all these cases, data are the outcomes
of research enterprises which are carried out on the basis of the available em-
pirical evidence; still, the results remain hypothetical and they sometimes need
to be expressed in probabilistic terms [10]. In addition, scholars may make in-
compatible hypotheses about the same phenomena. For instance, on the basis
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2 S. Cristofaro et al.
of research, a scholar may infer that Dante was born on a certain day whereas
another scholar may bring evidence but for a different date. Hence, to make
sense of experts’ knowledge, ontologies for the Digital Humanities (DH) need to
find ways to accommodate the representation of hypothetical and incompatible
scholarly statements, which we will call claims throughout the paper.
We address in the following some preliminary and ongoing research work
related to these topics. In particular, by reviewing the state of the art, we rely on
the work presented by Carriero et al. [3] to explicitly address the representation
of hypothetical and contrasting data. We will also discuss some challenges which
require further work to reach a higher modeling robustness.
The paper is organized as follows. In Sect. 2 we report on some examples of
data collecting and organization, stressing the need for a framework addressing
the representation of hypothetical/incompatible data. Relevant related works
are reviewed in Sect. 3. We present in Sect. 4 an analysis of how to represent
scholarly claims, which is further elaborated in Sect. 5. Sect. 6 concludes the
paper.
2 Motivations and background
To exemplify the discussion about the modeling of hypothetical and incompatible
knowledge and data in the Digital Humanities, we report here on some examples
and data collected during a research project at the Belliniano Civic Museum of
Catania (Italy) called Museo Virtuale della musica BellinInRete (BellinInRete
hereafter) [6].
The Museum preserves different kinds of objects related to the composer
Vincenzo Bellini (1801-1835). One of the main goals of BellinInRete is to digital-
ize data about the cultural heritage preserved in the Museum in order to, first,
facilitate the management of the data; second, to make it available – through the
Web – to both scholars and the general public. The use of Semantic Web (SW)
technologies is a desiderata for these goals but also to make more explicit the
intended meaning of the data. In addition, we think that BellinInRete would ben-
efit from both the exploitation of the reasoning mechanisms of SW languages like
the Web Ontology Language (OWL) and their use to make the data compliant
with the FAIR principles [16].
A first attempt to model the heritage items preserved in the Belliniano Civic
Museum via SW ontologies has been undertaken within BellinInRete by relying
on the standard RDA cataloging framework, see [4]. More recently, an initial
ontology inspired by CIDOC-CRM [1], namely OntoBelliniLetters [5], has been
proposed (and is currently under refinement) concerning the organization of a
corpus of letters of Vincenzo Bellini (and held by the Museum as well).3
3
Within the project BellinInRete, a digital edition of Bellini’s letters is carefully re-
alized using the XML-based Text Encoding Initiative (TEI) for text markup and
digitization (http://licodemo.ilc.cnr.it/bellini-in-rete/). This effort repre-
sents a relevant part of the project.
Towards the representation of claims in ontologies for the Digital Humanities 3
Some of the data provided by the domain experts involved in the project are
expressed by including different confidence degrees. In particular, this is the case
of information about the dating of paper documents such as letters or musical
scores but also paintings and posters, among other objects. As mentioned in the
introduction, dates attributed to these items (e.g., when a letter was written
by Bellini) are sometimes just approximated and hypothetical. For instance, to
mention a simple case, one may not know the exact date when a poster advertis-
ing a performance was printed. However, if the poster reports the performance’s
date, one can at least infer that the poster was printed at some time before that
date.4 To mention another example, although a letter may not specify the date
when (or the place where) it was written, the postmark may provide information
about when it was sent, a piece of data from which scholars can make hypotheses
about when the letter was approximately written.5
From a knowledge representation perspective, what is required in our under-
standing is a modeling framework allowing domain experts to specify explicitly
their claims and the degrees of confidence of such claims about the investigated
phenomena. In our view, therefore, the framework should satisfy – at least – the
following requirements:
R1: It needs to allow for the explicit representation of both certain and uncertain
data, as well as the representation of conflicting data;
R2: It needs to allow for the representation of meta-data about claims, e.g.,
who made the claim, when, and which confidence degree it bears (probable,
improbable, etc.), among others.
In addition, as technological requirement, considering the wide use of the
SW in nowadays applications, the framework should be manageable through SW
languages and technologies (R3).
We explore in the next sections the development of such a framework by
commenting on the state of the art and identifying some challenges for future
work.
3 State of the art analysis
We have seen above that knowledge and data in the human sciences are often
expressed in uncertain terms. It is not therefore surprising that existing works
in the DH have already explored various manners to express uncertainty.
In application contexts dealing with the digital representation of text doc-
uments, the XML-based Text Encoding Initiative (TEI) allows modeling uncer-
tainty by means of (combinations of) dedicated markup tags as reported in the
4
This is the case of the poster with inventory number CT0031 S 19 advertising a
commemorative performance celebrating the centenary of Bellini’s death.
5
For instance, the date reported on the letter with inventory number CT0031 LL1.9
(a letter written in Naples by Bellini and addressed to Giovanni Battista Perucchini)
does not include any month information. However, from the postmark, we know that
it was sent in January.
4 S. Cristofaro et al.
TEI guidelines.6 This approach is useful to annotate digital texts although it is
less relevant for our purposes.
In scenarios explicitly addressing the definition of knowledge or data models,
it is worth mentioning the extension of CIDOC-CRM [1] called CRMinf [15]. The
purpose of this latter ontology is to represent meta-data of argumentation pro-
cesses such as inference making resulting in beliefs with belief values. Examples of
belief values are true and false, although the space of values can be enlarged. For
instance, mimicking the value of a fuzzy logic, one may express any real number
within the interval [0, 1] (see [13]). The contents of beliefs, i.e., what beliefs say,
called proposition set, as well as the premises which one assumes to infer a belief
are all intended as CIDOC-CRM’s information objects, i.e., descriptions which
exist independently from specific supports. For example, one may represent a
particular inference making process reaching the belief with (i) proposition set
‘Dante was born between May 21st, 1265 and June 21st, 1265’ and (ii) belief
value probable. In addition, the ontology covers the class Inference Logic for “the
rules used as inputs to [...] Inference Making” [15, p.10]. It should be clear that
CRMinf is meant to document reasoning procedures for logical derivations but,
since it is only informally specified, it cannot formally express them.
In the work of Carriero et al. [3] relative to the development of a SW ontology
for cultural heritage, the authors rely on the ontology called Descriptions and
Situations (D&S) [8] for representing scholarly attributions; e.g., the attribution
of authorship to a document. A situation is a portion of reality whose constituting
entities stand in some relations and satisfy a description (i.e., an information
object, see [14]). Some situations, called interpretations, bear an epistemological
nature because they do not represent domain entities as they are but as they are
conceived and characterized by domain experts. For instance, the situation in
which a person is attributed to a book in the role of author is an interpretation,
an “epistemological fact”, based on some (possibly partial) scholarly evidence.
A different approach is proposed by Martin-Rodilla et al. [10] based on fuzzy
logic. The authors have also proposed a manner to represent fuzzy values in
conceptual models [9]. By relying on fuzzy logic, this proposal allows to express
the degree of truth of data in a precise manner (which can be also approximated
in qualitative terms, as the authors show). Differently from both CRMinf and
Carriero et al. [3], however, the authors do not characterize the attribution of
uncertainty values, e.g., in terms of who made it, when, etc.
We will see in the next sections how the approaches of CRMinf and Carriero
et al.[3] could be exploited to represent uncertain but also conflicting claims.
Also, although the use of non-classic logics seems better suited to deal with
partial or probable knowledge, differently from Martin-Rodilla et al. [10], we
stick on classical logic to facilitate the use of SW technologies in future works.
6
https://tei-c.org/release/doc/tei-p5-doc/en/html/CE.html, last accessed in
June 2021.
Towards the representation of claims in ontologies for the Digital Humanities 5
4 A modeling proposal based on claims
We can consider an ontology as a representation about which things exist in an
application domain, how they are related and characterized. Alternatively, an
ontology stands for a representation of a domain as this is understood by some
agents (domain experts, typically) within a certain context. Let us call realism
and descriptivism these two approaches, respectively (see [11]). As common prac-
tice in ontology engineering, the assumptions behind the two approaches do not
necessarily find an explicit place in ontologies.
To make an example, consider a relation like bornAt holding between a person
and the time when she was born. From an ontology modeling perspective, it can
be represented in different ways. An approach, inspired by ontologies like DOLCE
[11] or CIDOC-CRM [1], could be provided by formulas like (1-2) (we do not mean
to provide an exhaustive formal representation but to introduce some examples
to exemplify the discussion). Formula (1) introduces the primitive predicate
broughtIntoLif e(e, x, t) saying that the event e brought person x into life at
time t.
broughtIntoLif e(e, x, t) → Event(e) ∧ P erson(x) ∧ T ime(t) (1)
This predicate is used in (2) by which bornAt(x, t) means that there was an
event, namely, a birth event, bringing x into life at t.7
bornAt(x, t) ↔ ∃e(broughtIntoLif e(e, x, t)) (2)
Further restrictions can be used to characterize the event’s time, e.g., to
specify that it cannot span across days, months or years.8 From a realist per-
spective, the formulas are meant to describe how reality is. In the descriptivism
view, they tell how experts conceive reality. In both cases, however, reference to
either plain reality or experts’ conceptualization remains at the meta-modeling
level, and these assumptions are not reflected in the formulas.
Assume now that we do not know the exact date when someone was born. To
mention an example from BellinInRete, it is not certain that Francesco Ferlito,
Vincenzo Bellini’s uncle, was born in 1770. In this case, we are not only inter-
ested in saying that there was an event which brought Ferlito into life (either in
the realist or descriptivism sense). We need to make explicit reference to what
domain experts think and, in particular, we need to represent the association
between Ferlito and his birth time as an hypothetical assessment, i.e., a (sound)
belief about when he was born. As said in the previous sections, the representa-
tion of hypotheses plays a relevant role in the human sciences (and not only of
course), e.g., because scholars have only partial data about domain entities.
A way to handle similar scenarios may be via the explicit introduction of
claims. The idea is to represent some of the properties characterizing domain
7
Recall that ontologies like DOLCE [11] or CIDOC-CRM [1] allow representing data
values, including dates, according to value spaces (called quality spaces in DOLCE).
8
A degree of approximation is commonly employed when representing dates. As a
matter of fact, one seldom represents the exact moment when someone was born.
6 S. Cristofaro et al.
entities from an explicit descriptivist and epistemological perspective. For in-
stance, recalling the example above, this view may lead to a formula like (3),
where bornAt(x, t, c) is now a ternary predicate read as ‘person x was born at
time t according to claim c’.
bornAt(x, t, c) ↔ Claim(c) ∧ ∃e(broughtIntoLif e(e, x) ∧ assigns(c, t, e)) (3)
Intuitively, bornAt(x, t) in formula (2) expresses a direct link between a per-
son and her birth event’s time, whereas bornAt(x, t, c) in (3) bears an “episte-
mological flavor” saying that the relation bornAt between x and t holds only
because of claim c. In a sense, the predicate bornAt is “relativized” to the claim,
hence it expresses an hypothesis about the phenomena. Note that the formula
employs the (primitive) relation assigns(c, t, e) saying that ‘claim c assigns time
t to the event e.’ Also, broughtIntoLif e, differently from formula (1), is now a
binary relation between an event and a person (for simplicity, we do not formally
introduce this new predicate), because the link between the event and the time
when the event is supposed to have occurred is asserted only via the claim.
In our understanding, a modeling approach on these lines could turn useful to
make explicit the dependency of some data on intentional attributions, recalling
the requirements mentioned in Sect. 2. In the next section, we dig into the notion
of claim addressing some modeling challenges for its representation in ontologies,
including the relation between, say, “claimed-” and “regular-” predicates (e.g.,
bornAt in (3), and bornAt in (2), respectively).
5 Discussion on claims
In order to put forward the introduction of claims, we shall say more about how
they could be conceived, also with respect to existing ontological frameworks.
Since our research is still preliminary, we address some proposals and challenges
which need to be further investigated to reach a higher robustness.
Claims wrt the state of the art. We understand claims as entities that (i)
result from scholarly investigations, (ii) bear epistemological values (see below),
(iii) are accessible in an inter-subjective manner, (iv) can be collaboratively for-
mulated by multiple scholars, and (v) depend on their creators. Thus, different
scholars can express – independently from each other – similar but not identical
claims. In addition, (vi) there can be conflicting claims about the same phe-
nomena; e.g., the impresario Alessandro Lanari was born in 1790 according to
Seminara (see [2, p. 201]), and in 1787 according to the data reported in VIAF.9
Finally, it seems reasonable to consider claims as (vii) static entities which can
persist through time but cannot change. For example, if scholar s formulates
claim c at time t about document d attributing it to author a, then, if at a later
time t0 s attributes the authorship of d to a different author a0 , s creates a new
claim c0 which entertains various relations with c (e.g., being about the same
document d, being produced by the same scholar s, being a revision of c, etc.).
9
http://viaf.org/viaf/30590930, last accessed in June 2021.
Towards the representation of claims in ontologies for the Digital Humanities 7
There are some analogies but also relevant differences between our ideas and
what done by CRMinf [15]. First, CRMinf’s beliefs are temporal entities. To quote
from [15, p.11], “[t]his can be understood as the period of time that an individual
[agent] or group [of agents] holds a particular set of propositions to be true, false
or somewhere in between”. Claims stand on a more abstract level, since we do
not mean to represent a temporal entity in which an agent holds an hypothesis,
e.g., the state in which agent a thinks that Alessandro Lanari was born in 1790;
rather, we focus on the hypothesis itself. In this sense, claims are more similar to
CRMinf’s proposition sets. Clearly, one can introduce the time in which a claim
is created, as well as its creation event or the state in which someone holds the
claim. Second, we agree with CRMinf in that claims depend on their creators,
which can be single agents (actors in CIDOC-CRM’s terminology) or groups.
Carriero et al. [3] express scholarly attributions via the representation of
interpretations (i.e., facts with an epistemological status). As a working hypoth-
esis, claims could be understood as specific types of descriptions (in the sense
of [3]) satisfying the conditions mentioned at the beginning of this section. In
particular, when a claim assigns a property to an entity, e.g., a birth date to
a person or authorship to a document, there is a corresponding interpretation
representing the epistemological fact in which the entity satisfies that property
(see below for discussion and examples).
The conceptual model. Following Carriero et al.’s [3], the diagram in Fig.
1 illustrates (in a preliminary manner) how our approach may work (classes in
yellow are taken from [3]). More precisely, the conceptual model is based on the
ontology called ArCo context description and its modeling pattern for representing
situations.10
Fig. 1. Preliminary modeling of claims based on Carriero et al. [3].
10
The ontology is available at https://w3id.org/arco/ontology/
context-description/1.2; the situation-pattern can be found at http:
//www.ontologydesignpatterns.org/cp/owl/situation.owl (both resources
last accessed in June 2021).
8 S. Cristofaro et al.
Considering the diagram in the figure, the relation hasDescription, here used
between Situation and Description,11 tells that a situation is the state of affairs
corresponding to (satisfying) what the description describes. For instance, for
the description d telling that Lanari was born in 1790, the situation satisfying
d is constituted by Lanari exemplifying the property of being born in 1790. The
relation is situation of (and its inverse has situation; not shown in the diagram)
is used to link a situation to its constituting elements. Recall that situations in
[3] are the ontological counterparts for reified relations in languages like OWL
which do not support the representation of predicates with arity higher than
two (see [3, p.20]). In the context of [3], therefore, the relation is situation of
can be seen as the link between a reified relation and its arguments. As said,
the Interpretation class models situations with an epistemological grounding,
hence, they exist because of interpretation criteria (represented in [3] by the class
Interpretation Criterion) identifying them. These criteria share some similarity
with our notion of claim. The authors do not however specify, e.g., whether
the identity of interpretation criteria is bound to their creators or whether they
can change in time while maintaining their identity. This is the reason why we
subsume Claim under Description rather than extending Interpretation Criterion
(which indeed does not appear in Fig. 1). We use the relation dependsOn to
stress that interpretations require claims to exist. Its cardinality is one-to-many
on the side of Claim; this is because a situation may correspond to multiple
claims which state the same fact but differ with respect to their creators. Claims
attributing different or even incompatible properties to the same entities pose
some challenges which we will address later in this section.
Concerning epistemological values, intuitively, one may need to say that a
claim bears a certain level of uncertainty. Recall that classical logic is bivalent,
i.e., (logical) propositions are either true or false (and nothing more). This could
pose some limits for representing claims, and an approach based on the use of
non-classical logics would be likely more suited, e.g., along the lines of what done
by Martin-Rodilla et al. [10]. On the other hand, as said, the use of classical logic
allows using SW technologies for knowledge representation and reasoning, which
is a desiderata in nowadays DH application scenarios and in our project(s) as
well (requirement R3, Sect. 2)
A way to handle epistemological values in a modeling framework based on
classical logic could be done on the lines of CRMinf (see Sect. 3). For instance,
one may introduce a class for (sorts of) epistemological characteristics, i.e., Epis-
temological Value in Fig. 1, expressing either qualitative values only (similarly to
[7,9]) or more precise quantitative metrics.12 The manner in which these values
are established is meta-information remaining out of the scope of the ontology;
that is, we assume that a scholar decides which epistemological value a claim
bears with respect to the adopted research methodology. We are aware that
11
Domain and range for hasDescription are not specified in ArCo context description.
At first glance, this relation generalizes the relation satisfies used in D&S [8].
12
The representation of epistemological values can be refined by adopting an approach
similar to DOLCE [11] to model values in terms of quality spaces.
Towards the representation of claims in ontologies for the Digital Humanities 9
this is only a simple way to “mimic” non-classical truth-values. In a sense, our
proposal is a trade-off between modeling needs and technological requirements.
Finally, as the terminology suggests, relations between Claim, Actor, and
Time are simple links to relate claims to the actor (single person or group) who
made the claim and the time at which the claim is expressed, respectively.
Figure 2 represents an exemplification of the diagram above to a specific
claim by which Alessandro Lanari was born in 1790 according to Seminara such
that the claim has epistemological value probable and was expressed at a certain
time. Since we represent the attribution of a birth date, both the claim and the
resulting interpretation are called – following [3] – dating claim and interpre-
tation. Also, the relation is dating of is used in [3] to explicitly link a dating
interpretation to the person to which it is about.13
Fig. 2. Seminara’s claim about Alessandro Lanari’s birth date
Challenges. Moving to some open challenges requiring further research,
first, the structure of claims and situations need to be better characterized and
linked. For instance, a formula like (4) could be adopted to tell that if claim
c assigns birth date t to person p, there exists an interpretation i standing
for the epistemological fact in which the dating of (the birth event of) p is t,
and i satisfies/depends on c (see Fig. 2). A similar approach could be used to
characterize other types of assignment relations and the corresponding claims.
assignsBirthDate(c, t, p) → ∃i(hasDescription(i, c) ∧
(4)
dependsOn(i, c) ∧ isDatingOf (i, p) ∧ date(i, t))
A second and important challenge concerns the relation between hypotheti-
cal and non-hypothetical (i.e., certain) knowledge, therefore, the use of claimed-
predicates (e.g., bornAt(x, t, c)) in tandem with regular predicates (bornAt(x, t)).
For example, considering again the case represented in Fig. 2, the dating inter-
pretation tells that Alessandro Lanari was born in 1790 according to a dating
claim. Consequently, we cannot infer that Lanari was born in 1790 independently
13
The representation of claims could be done at different levels of granularity. For
instance, in the case of dates, one may explicitly tell that the claim is about both
the month and the day, in which case two different dating claims (about the same
person) would be needed.
10 S. Cristofaro et al.
from the claim. Thus, a formula like (5) cannot be assumed in general.
bornAt(x, t, c) → bornAt(x, t) (5)
In our understanding, an ontology for the DH needs to represent claims along
with an approach for domain entities which do not require the attribution of
hypothetical properties. There are at least two scenarios where the use of claims
seems appropriate: the first one, to express a level of uncertainty about the data;
the second one, to document the provenance of the data, e.g., as seen above, that
it is according to Seminara that Lanari died in 1790. This however requires a
deeper analysis of provenance knowledge and modeling requirements to better
understand how our approach can deal with provenance scenarios in applications.
Also, it could be argued that data increase in reliability and becomes more
certain if there is increasing evidence about them, or the scholarly community has
put in place verification strategies. The verification of data in the human sciences
can be challenging, especially when one has only partial sources about past
phenomena. From this perspective, one might dig into the link between certainty
and uncertainty via theories for judgement aggregation, exploring therefore the
manner in which multiple claims could be consistently aggregated (see [12]).
Aggregation mechanisms are also needed to combine data which are expressed
at different granularities; e.g., claims ascribing the birth date of a person with a
time interval vs. claims ascribing specific time points within the interval.
A third challenge, mentioned throughout the paper, concerns the representa-
tion and management of incompatible claims, which can lead into inconsistencies
with respect to the assumed background knowledge; e.g., with axioms establish-
ing that a person cannot be born on different dates. One may explore in this
case, too, the application of judgment aggregation methods to discard conflict-
ing data. If this is not possible, e.g., because there is no definitive reliability
measure in favor of a piece of data, another strategy could consist in “relaxing”
the knowledge constraints.
Just to mention an example, a formula like (6) could be (reasonably) used
to tell that when two different claims c and c0 attribute birth dates t and t0 ,
respectively, to the same person x, t and t0 need to be the same date.
bornAt(x, t, c) ∧ bornAt(x, t0 , c0 ) → t = t0 (6)
However, since there can be conflicting claims about the same phenomena,
an approach on the lines of (6) could be too restrictive. For this reason, (6) could
be discarded from the background knowledge to make it possible for two equally
reliable claims to attribute incompatible properties to the same entity, leading
to incompatible but co-existent situations.
Figure 3 shows VIAF’s claim about Alessandro Lanari’s birth date. This
claim and the corresponding situation coexist with what represented in Fig. 2
(at least, up to the point in which some grounded evidence emerges from one of
the two claims).14
14
For archival purposes, one may be interested in keeping claims that have been dis-
carded because they are not anymore accepted by the scholarly community. In this
Towards the representation of claims in ontologies for the Digital Humanities 11
Fig. 3. VIAF’s claim about Alessandro Lanari’s birth date
6 Conclusions and future work
We discussed in the paper the representation of hypothetical knowledge in the
field of the Digital Humanities from an ontological modeling perspective. We
proposed to tackle this topic in terms of properties claimed by scholars on re-
search evidence. Claims can be formulated collaboratively by multiple scholars,
are accessible in an inter-subjective manner, and bear epistemological values
describing their level of uncertainty/reliability.
The research work described in the paper is still in its infancy, and some
identified research challenges remain still open; examples include the represen-
tation of claims about incompatible properties and the relationships between
hypothetical and certain knowledge. We plan to address these challenges more
carefully as future work. In particular, this will be done by taking into account
theories for judgment aggregation and modeling approaches for the representa-
tion of beliefs. This analysis will hopefully contribute to reach a more mature
level of understanding of the raised issues and to put forward our ideas in a
robust formal setting exploitable in SW applications.
Acknowledgements: We are grateful to colleagues at the CNR Institute of
Cognitive Sciences and Technologies (ISTC), as well as to SWODCH’s reviewers
for their valuable comments on previous versions of the paper. No one but us is
responsible for any remaining mistake.
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