=Paper=
{{Paper
|id=Vol-2810/paper9
|storemode=property
|title=Supporting Complexity and Conjectures in Cultural Heritage Descriptions
|pdfUrl=https://ceur-ws.org/Vol-2810/paper9.pdf
|volume=Vol-2810
|authors=Gioele Barabucci,Francesca Tomasi,Fabio Vitali
|dblpUrl=https://dblp.org/rec/conf/colco/BarabucciTV20
}}
==Supporting Complexity and Conjectures in Cultural Heritage Descriptions==
https://ceur-ws.org/Vol-2810/paper9.pdf
Supporting complexity and conjectures in
cultural heritage descriptions
Gioele Barabucci1 , Francesca Tomasi2 , and Fabio Vitali2
1
NTNU – Norwegian University of Science and Technology, Trondheim, Norway
gioele.barabucci@ntnu.no
2
Alma Mater Studiorum – University of Bologna, Bologna, Italy
{francesca.tomasi,fabio.vitali}@unibo.it
Abstract. Data and metadata once hidden in dusty card cabinets of
thousands of galleries, libraries, archives and museums worldwide are now
available online in digital formats. An incredible explosion of metadata
has been expanding in the quantity of digitized data, the richness and
sophistication of data models, the number of institutions and private
citizens that contribute and their interconnection.
A fundamental issue, however, limits this undeniable success: current
data models force the expression of a single point of view. For example,
the field author is either set to a value or to another one. Any disagree-
ment about the content of a field is resolved before the publication of
the data and forever lost.
Yet, we argue, the expression of different and contrasting points of views
is a keystone of scholarship, as well as one of the most engaging aspects
for everyone. Bowdlerized, sterile, conflict-free data records fails to cap-
ture the core of important scholarly debates and thus fails to attract
the interest of the general public. The root cause of this issue is techni-
cal rather than cultural: current standards for data models (e.g. RDF,
OWL) do not simply support the expression of contrasting statements.
In this paper we propose both a methodological approach to address this
problem, and a proof of concept of how this problem could be fully and
cleanly overcome with modest extensions to the existing standards. We
name this approach “contexts and conjectures”.
Keywords: multiple points of view, contrasting assertions, RDF serial-
ization, scholarly conjectures.
1 Introduction
Why do Galleries, Libraries, Archives and Museums digitize collections? We can
list a few reasons: preservation of knowledge, i.e., associating to physical or cul-
tural artefacts what is known about them, should they get destroyed or forgotten
about; interlinking, i.e., historically situating these artefact by showing the con-
nections between them; circulation of knowledge, i.e., allowing larger audiences
to gain access to information about cultural artefacts that otherwise (because of
fragility, physical distance, obscurity) would be hard or impossible to reach.
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
105 G. Barabucci et al.
Yet, we notice, we are failing on all counts. We fail at preserving knowledge
because an extremely limited quantity of information ends up being associated
with our physical and cultural artefacts; we fail at interlinking collections because
technological constraints force us to choose between consistent or connected
datasets; and, most importantly, we fail at circulating knowledge because we
cannot interest a substantially large audience to our digital collections.
We believe that all these failures stem from a single cause: a (doomed ) search
for objectivity, forced by digital models that mostly ignore the derivative and
stratified nature of cultural objects, and allow for only one point of view to be
expressed, leading to unbalanced and incomplete descriptions of our artefacts.
This eliminates debates and disagreements, exactly what has a chance to create
and maintain interest in scholars and general audiences alike.
We rather believe that we should explicitly aim at representing competing
points of view and opinions, and make sure that we fully document their ex-
istence, their strengths and the ideas behind them, so that our audiences can
finally perceive representations that are truer and more interesting than the
sterilized and boring renditions forced by so-called objectivity.
In many disciplines of the humanities, truths are hard to come by and facts
are rare. In most cases, we use words such as facts and truths just to mean
“statements for which there is an acceptable trail of supporting sources”, or
“statements that are more or less accepted by the majority of the relevant schol-
ars” or even “statements that so far have not been disproven”.
This is not a surprise, as precise knowledge in many cases is impossible or
outside our reach. Yet, scholars can easily deal with incomplete or conjectural
knowledge, and even when they are personally convinced of the truthfulness of
some information, they are aware and able to accept that different viewpoints,
dissent and speculations may exist about them.
Unfortunately, the ability of contemplating and handling different or oppos-
ing interpretations over the same piece of information is not embodied in the
software and in the digital data structures that we use to represent them: single
points of view are what we expect in our digital collections. The inevitable con-
flicts and disagreements cannot be expressed explicitly, and need to be simplified,
resolved and eliminated before committing information to the digital realm.
Yet, unknowns, disputes and dissenting opinions are often what in the first
place attracted, fascinated and keeps fascinating scholars. Neutered, indisputable
and unequivocal digital data have the twin problems of a) being a poor repre-
sentation of what we know and think and b) being fundamentally boring and
unable to attract anyone, especially laypeople and younger students.
Our approach to making datasets able to express the richness of the non-
digital scholarly debates is composed of two parts: contexts (Section 3) to allow
the expression of conflicting opinions on an artefact, and conjectural graphs (Sec-
tion 4) to encode scholarly statements as qualified arguments, rather than as
absolute truths, and a mechanism, collapse to reality, to turn some conjectural
graphs into plain RDF statements that can be interlinked and get them to in-
teroperate with other RDF-based datasets.
Supporting complexity and conjectures in cultural heritage descriptions 106
2 Common issues in representing metadata
Consider the example in Fig. 1, taken from a record in Europeana, coming from
the Bildarchiv Foto Marburg [13].
Fig. 1. An image found in an Europeana record
The existing record3 is accompanied by metadata stating that the item be-
ing described is a 53,9 x 41,3 cm print by G.B. Piranesi, dated 1756 (and/or
1787), titled “Veduta dell’Anfiteatro Flavio detto il Colosseo”, with subject
James Caulfield and King Gustaf III of Sweden. Another (now deleted) record
described the (very same) item as the Colosseum, a 70-80 A.D. building by Ves-
pasianus, and, in a plain text description, represented via a 1960/70 photo by
Konrad Helbig of a print by G.B. Piranesi (see Fig. 2).
Fig. 2. Two records for the same image from Europeana in 2012
These records are both incomplete and wrong. They contain, at the same
time, objective and factual information (e.g., the size of 53,9 x 41,3 cm), some
3
https://www.europeana.eu/en/item/2064137/Museu ProvidedCHO Bildarchiv
Foto Marburg obj20089555 T 001 T 071 The image was present in 2012 in two
separate and dissimilar records in Europeana, and is now (2020) present in only one,
the second having been removed probably due to being a duplicate and because of
the number of issues in its description.
107 G. Barabucci et al.
reasonable interpretations (e.g., the author) and many factual errors (e.g., James
Caulfield and King Gustav III are clearly not the subject of the image, this item
is not a building and its creation date is not 70-80 A.D.).
So, the facts are few and actually constitute the most boring and uninspir-
ing part of the data items we are shown, and many details, although wrongly
characterized, paint a much more complex and interesting story, once we take it
upon ourselves to understand and explain them.
We have a better story: this is a black/white photograph from a retrospective
of the works by Konrad Helbig (1917-86), a famous German art photographer,
known for his groundbreaking photographs of Italian classical works of art, and
of tanned, underage, naked Sicilian boys. The photograph depicts a print of
the Colosseum by Giovan Battista Piranesi (1720-78) taken from “Antichità
romane”, a four-volume book of high quality etchings of various sceneries of
Rome and its countryside, a bestseller of the XVIII Century, often brought back
home by the young nobles and high society bourgeois upon their return from
their “Grand Tour” in Italy.
The first edition of the book (1756) was dedicated to James Caulfeild, Lord
Charlemont (1728–1799). In 1756 Gustav III (1746–1792) was 10 years old, not
a king yet, and likely uninterested in the arts. He was King, on the other hand,
after Piranesi’s death, when his son Francesco (1758-1810) made Gustav III the
new patron of the publishing and dedicated the new edition (1787) to him. This
explains both the two dates and the double dedication of the volume.
This may be a better story, but can we now expect the Europeana record
to be fixed accordingly? Uhm, well. First, ours is just one of many possible ex-
planations: it should not be promoted to truth only because it is richer or more
plausible than the current one. Second, and most importantly, this explanation
deals with many different levels of abstraction of the artefact (the chain of re-
productions, the physicality of these reproductions, the people associated with
them, etc.), and the data model employed by Europeana [10] does not possess
the necessary concepts. Unfortunately, Europeana’s problems are not unique:
these limitations are common to most data models used in the humanities.
The example in the previous section has shown various typical problems in
representing metadata (digitally or otherwise) about our cultural heritage. We
like to classify these issues in a few broad categories:
reticence: information that was well known was not recorded, due to haste, lack
of skill, or, more probably, lack of policies and software support. The role of
Helbig, the relationship between the table and whole book are missing, etc.
flattening: the fact that an artefact depicts another artefact is never made
explicit, and here it actually occurs twice (a photograph of an etching of a
building). A single set of metadata describes the image, the physical support,
and the chain of entities depicted, as an inconsistent and meaningless record.
coercion: important information for which no appropriate field was found, was
forced into inappropriate fields (e.g., the dedication of the whole book to
James Caulfeild and Gustav III ends up as the subject of a single table),
leaving to the reader the job of making things straight, and forever baffling
any automatic tool tasked with indexing and searching collections by subject.
Supporting complexity and conjectures in cultural heritage descriptions 108
dumping: important information for which no appropriate field was found, was
forced as plain text inside a descriptive field, easy for humans to read but
forever lost to any automatic tool. For instance, the existence of Helbig, the
date of the photograph and the exact placement of the table within a four-
volume book are text-only in the Description field, and never formalized.
In addition to these, there are a few remaining problems we should also
consider: not all statements of our story have the same reliability; our statements
are clearly authorial (we are expressing our own personal interpretations); and
we ourselves have fairly diverse levels of confidence about the events described.
In the following section we propose an approach called contexts. Contexts
cohesively address these issues, and their adoption enables data models to express
not only fine details about the artefacts, as they do now, but also a wide variety
of opinions, points of views and conjectures that constitute the largest part of
our knowledge about cultural heritage.
3 Creating rigorous metadata by adding contexts
Plenty of data models exist in the digital humanities to represent metadata, facts
and information about our cultural heritage. They encode fine details about our
artefacts but cannot easily handle more complex situations like the one presented
above and cannot describe the circumstances within which these details find their
place, their role, their correctness, their plausibility. The approach we suggest
to overcome these limits is the use of contexts, presented in more details in [2]
and summarized in this section.
In general, we define contexts as sets of statements meant to characterize
the metadata about an artefact, rather than the artefact itself. For instance,
determining the provenance of the creation date of an ancient vase is not a
statement about the vase, but a statement about a statement about the vase.
Contexts provide boundaries to opinions and make them comparable to facts.
Identifying and expressing the context of all statements is fundamental for their
correct interpretation and use. Without them, we could draw false conclusions.
The following are a few contexts that we have identified:
Temporal relationships: facts and assertions are often constrained inside a
temporal interval. For instance, Gustav III was the King of Sweden only
between 1771 and 1792; he was alive in 1756, but he was not King then, and
hardly the addressee of a dedication from a Roman printer.
Spatial relationships (or, better, jurisdictional): geopolitical entities are
evolving concepts and statements referring to them must be qualified. For
example, was Caulfeild Irish? James Caulfeild, 1st Earl of Charlemont (1726-
1799), was a noble of the Kingdom of Ireland, then under the rule of the
Crown of England, with little or no direct connection with the current Re-
public of Ireland. His nationality is a handy simplification for a much more
nuanced characterization of his true origins.
Part-whole relationships: the item being described in Helbig’s photograph is
only one page in a four-volume book, itself published in at least two editions.
109 G. Barabucci et al.
We should be able to distinguish facts regarding the individual page (the
heading of the image, the subject, etc.) from those regarding the volume as
a whole (the author, the publication date(s), the dedications, etc.)
Object-subject relationships: Object-subject chains can be particularly
deep, intricate and fascinating. In our example what we are describing is
in fact a JPEG, derived from a high-resolution TIFF scan, of a photograph
by Konrad Helbig dated 1956, of an etching dated 1756 by G.B. Piranesi,
of a 70-80 a.D. building in Rome. Each of these items deserves descriptive
metadata that must be associated with the correct entity.
Provenance: Statements of the metadata come from sources that should be
identified: an individual, a text, the direct observation of the item, etc. Prove-
nance not only gives backing and traceability to the stated facts, but most
importantly it allows multiple different and competing statements to coexist,
even if in contrast with each other.
Confidence: the sources themselves expose varying degrees of confidence in ex-
pressing this or that fact. Recording a confidence level allows for conjectural,
hypothetical and even wacky statements to correctly coexist with established
and settled information.
The use of contexts provides the objectivity and truthfulness that software
needs and computer scientists crave for, without giving up on the richness of
information favored by domain scholars. E.g., the statement “the Marburg Foto
Archiv organized a retrospective about Konrad Helbig” is clearly speculative,
non-objective and conjectural. Adding a little context, the statement “Barabucci
et al. (2020) speculate that the Marburg Foto Archiv organized a retrospective
about Konrad Helbig” becomes objective and easily verifiable. The addition of
provenance created a statement that is undeniable and usable in a scientific
discourse.
Contexts allow expressing more than just settled and boring data: their abil-
ity to enunciate conflicting views allows the creation of a much more interesting
and nuanced representation of our cultural past.
3.1 Expressing contexts in RDF
The natural habitat for contexts as presented in the previous sections is in
datasets expressed with Semantic Web standards such as RDF4 and OWL5 .
Expressing metadata using RDF is easy, and most of the existing models used
in cultural heritage already have an RDF representation.
RDF is used to express statements about entities. In the RDF model each
statement is expressed using a triple composed of a subject, a predicate and an
object, the subject being the entity being described. For example: “Antichità
Romane” (subject) has author (predicate) “G.B. Piranesi” (object).
Modelling contexts means expressing statements whose subject is not the
artefact being described, but another statement in RDF that expresses some
quality about the artefact. The simplest approach is called reification, which
4
Resource Description Format: http://www.w3.org/TR/rdf11-concepts/
5
OWL 2 Web Ontology Language: http://www.w3.org/TR/owl2-overview/
Supporting complexity and conjectures in cultural heritage descriptions 110
requires treating the statement itself as an entity, which becomes the subject of
the contextual statement. For example, in Fig. 3, :S is a statement that asserts
that the book “Antichità Romane” was authored by G.B. Piranesi (1). The
statement at the end (2) affirms that statement :S was attributed to GBarabucci,
FTomasi and FVitali, thus assigning a clear and unambiguous provenance to it.
: S rdf : type rdf : Statement ; ¬
rdf : subject :AntichitàRomane ;
rdf : predicate dc : author ;
rdf : object : GBPiranesi .
: S prov : wasAttributedTo [ : GBarabucci ,
: FTomasi , : FVitali ] .
Fig. 3. A contextualized statement
We cannot, however, simply add these RDF statements to existing RDF
collections. These statements would be understood by RDF processors and rea-
soners as expressing at the same time two related but independent sentences: 1)
that “G. B. Piranesi is the author of the book ‘Antichità Romane’”, and 2) that
“Barabucci, Tomasi and Vitali assert this”. The truth of the first statement is
not under discussion: it is flatly asserted. The provenance of this statement is
provided in addition to it and is at the same level. Contexts expressed in this
way do not restrains the statement of authorship, as we were hoping to obtain.
Extending current data models with the ability to assert statements as true
only within and depending on the truth of a given context is problematic due to
shortcomings of the underlying RDF meta-model. The same issues arise not only
in the Semantic Web, where RDF and OWL are used, but also in traditional
databases, where the ER model [6] is used.
We could, of course, overcome this limit by introducing ad-hoc interpretation
rules in our own metadata processors, e.g., by having them ignore assertions
outside of explicitly activated contexts. This approach would work in practice,
but would be project-specific and would prevent the sharing of our data with
the rest of the world, e.g. as Linked Open Data: we cannot expect all LoD
participants to use our modified rules instead of standard RDF reasoners to
process this and other RDF datasets.
Or, we could adopt a RDF extension called nested named graphs [11], that
introduces the concept of “local truth” and changes what RDF reasoners are
allowed to infer from a dataset. Nonetheless, as of 2020, nested named graphs
are not yet fully standardized, nor supported by reasoners.
Adopting contexts would make it possible to express different opinions while
providing the consistency and the truthfulness needed to support the coexistence
of heterogeneous point of views.
4 Conjectures: machine-readable scholarly assertions
grounded on contexts
Contexts provide a clear and systematic way to record possibly contrasting state-
ments about the same object from multiple points of views, but they do not
111 G. Barabucci et al.
address how contrasting statements may be processed, combined and presented
by a piece of software.
Our approach to make contextual metadata coexist and work with other
RDF-based datasets goes under the name of conjectures. Conjectures are schol-
arly statements that are formally expressed without being asserted, i.e., without
an associated truth value. Statements expressed in conjectures are as in a limbo:
their truth state is unknown, and they can, if desired, be subjected to conditions,
called guards. Now and then, for example when evaluating a what-if scenario,
conjectures can be programmatically turned into usual assertions through a spe-
cific operation called collapse to reality (or collapse to truth).
Multiple statements can be collected in conjectural graphs, a set of statements
that may or may not be asserted collectively and that must be either all asserted
or none asserted at the same time. When a conjecture is collapsed to reality, the
corresponding conjectural graph becomes equivalent to a plain RDF graph.
In the following sections we will illustrate through a running example how
conjectures, conjectural sets and contexts fit together, discussing basic seman-
tics, notation and compatibility with RDF. The basis of our example are two
incompatible conjectures: “Francesca Tomasi states that Giovanni Battista Pi-
ranesi is the author of Antichità Romane” and “Fabio Vitali states that Elvis
Presley is the author of Antichità romane”.
4.1 Semantics and notation
The core semantics of a conjecture, in contrast to classic RDF statements, is that,
by entering a conjecture, we do not assert the corresponding statements, but only
assert that 1) these conjectures exist, 2) they are available for examination, and
3) they can be collapsed to reality.
We use the a simple extension to the TriG [5] syntax to denote a conjecture
graph “Cj” that expresses that the statements “S1 P1 O1” and “S2 P2 O2” are
possibly but unknowingly true. A guarding statement can be added, for instance
an attribution to a specific agent A that has provided the conjecture:
CONJECTURE Cj {
S1 P1 O1 .
S2 P2 O2 .
}
Cj prov : wasAttributedTo A .
Conjectural graphs cannot be linearized as RDF graphs without losing their
semantic expressivity. Nonetheless, there exists a simple and straightforward
representation of conjectural graphs in RDF as named graphs, and it can be
generated algorithmically with a very limited procedural effort.
By construction, the conjecture “Cj” is defined as equivalent to a new graph
“CTXj”, called the weak context graph of Cj, in which the predicates in the
statements have been replaced by weakened predicates that are only conjec-
turally equivalent to their original counterparts:
CTXj {
S1 PG1 O1 .
S2 PG2 O2 .
Supporting complexity and conjectures in cultural heritage descriptions 112
PG1 i s C o n j e c t u r a l l y E q u i v a l e n t T o P1 .
PG2 i s C o n j e c t u r a l l y E q u i v a l e n t T o P2 .
}
CTXj prov : wasAttributedTo A .
The graph is a weak form of the conjecture because instead of using the
expected predicates P1 and P2, we use ad hoc predicates PG1 and PG2, which are
different and independent from P1 and P2, but are actually asserted, and connect
the same subjects to the same objects. Weak predicates are associated to their
respective strong predicates through the new predicate isConjecturallyEqui-
valent which provides the semantic closeness and structural weakness that we
need to establish the connection between them.
In this way we can assert multiple clashing statements at the same time:
conjectures can be expressed side by side and their assertion of truth is absent
or postponed.
Our running example can now be expressed as follows:
CONJECTURE : T { :AntichitàRomane dc : creator : GBPiranesi . }
: T prov : wasAttributedTo : FTomasi .
CONJECTURE : V { :AntichitàRomane dc : creator : EPresley . }
: V prov : wasAttributedTo : FVitali .
As said, these conjectural graphs are equivalent to the following weak con-
textual graphs in plain RDF:
:T {
:AntichitàRomane T : creator : GBPiranesi .
T : creator conj : i s C o n j e c t u r a l l y E q u i v a le n t dc : creator .
}
: T prov : wasAttributedTo : FTomasi .
:V {
:AntichitàRomane V : creator : EPresley .
V : creator conj : i s C o n j e c t u r a l l y E q u i v a le n t dc : creator .
}
: V prov : wasAttributedTo : FVitali .
where the syntax T:creator and V:creator denotes two weak predicates
that live in separate namespaces, differ from each other and differ also from the
original Dublin Core dc:creator, but are explicitly represented as conjecturally
equivalent to it.
4.2 Collapse to reality
The process one can use to actually assert the content of conjectural graphs is
that of collapse to reality, in which the statements belonging to a conjecture are
reasserted in their full and strong form, while leaving other conjectures in their
weak, non-asserted form. The collapse does not modify the nature of the conjec-
ture, but re-empowers it. The term refers to the reduction of the superposition
of multiple independent (and, possibly, incompatible) conjectures to a single one
which is then asserted as true, and it is inspired by a similar concept in quantum
mechanics, where the wave function collapse is the effect of an observation from
113 G. Barabucci et al.
the external world that reduces multiple states of a quantum system to a single
one.
In practical terms, collapsing a graph means programmatically adding ex-
tra statements that short-circuit the guarded predicates. They re-represent the
guarded predicates as actual facts. This is done either via the introduction of
predicates such as owl:equivalentProperty to connect weak and strong predi-
cates, or by re-asserting in their full form the statements expressed in weak form.
Since the two approaches are redundant but not reciprocally incompatible, we
can do both so as not to depend on OWL tools to obtain the desired result.
Collapses to reality are, themselves, graphs that can be contextualized, for
instance with provenance. For example, if we want to express the fact that Gioele
Barabucci believes in the attribution by Francesca Tomasi, we would augment
“T” with a collapsing predicate and attribute it to him, as follows:
: collapseOfT prov : wasAttributedTo : GBarabucci .
: collapseOfT conj : collapses : T .
: collapseOfT {
: GBPiranesi dc : creator :AntichitàRomane .
T : creator owl : eq uivalentPr op ert y dc : creator .
}
The system sees and processes the new statement as any other statement,
without needing specific support for conjectural graphs. The collapse to reality is
well contextualized and can be made temporary, since we can cleanly undo it by
removing the collapsed graph as a switch in what-if scenarios. At the same time,
all conflicting conjectures are still in place and available for further processing
by conjecture-aware tools.
To summarize, the collapsing process is powerful but simple to implement, re-
quiring only the use of well-defined and contained pre- and post-processing proce-
dures during the ingestion of conjecture-based datasets. This allows conjecture-
based datasets to be used together with more classic RDF- and OWL-based
datasets currently published as Linked Open Data.
5 Representing contexts in the Semantic Web
Much of the past and current data modelling efforts towards the description of
cultural objects are focused on being able to represent an increasing amount of
details. In [2] we provide an extensive survey of the depth and richness of many
data models for the description of cultural heritage and historical sources.
Although these models expose a complex, multidimensional and intercon-
nected landscape, none of them addresses the issues discussed here, because of
their implicit assumption that data description should be neutral and objective.
Challenges to the notion of neutrality of data models can be seen in HiCO6 ,
where interpretation acts are used to express the provenance of interpretations.
The Mauth tool [7] expresses the authoritativeness of existing statements with
explicit paternity, let users become an active of the description process. More re-
cent projects [9,8] demonstrate the possibility of a semantic enrichment through
a contexts-aware approach, especially in the Linked Open Data workflow.
6
Historical Context Ontology: http://purl.org/emmedi/hico
Supporting complexity and conjectures in cultural heritage descriptions 114
Similarly, ontologies such as PROV-O7 , for describing the provenance, those
in the SPAR family8 (FaBiO for publishing data, PRO for managing roles, CiTO
for representing citations) or GAF9 , for linking mentions, can all be used to add
pieces of context to data descriptions. Further, the CMV+P document model [1]
makes the very fact that most cultural artefacts reference or embed other arte-
facts explicit and integrable into other data models. The use of graphs to contex-
tualize specific statements is employed also by nano-publications [12], although
not to block the assertion of the statement, but just to enrich them.
Furthermore, the recent RiC-O10 model uses the similarly named notion of
“contexts” to indicate a plurality of paratextual information that translate the
classical siloed approach into a graph of connections between vocabularies and
ontologies. Additionally, contexts and conjectures can be seen as a generalization
of the Factoid model [4] used in prosopography, where it is common practice to
treat information found in old records not as objective truths, but as utterances
of partially trusted sources. This mistrust for sources, and the consequent need
for contextualization, is reflected in the design of modern APIs for querying
historical datasets [14,3].
6 Conclusions
The inability of current technologies such as RDF to express multiple contrasting
points of views over the same cultural artefact is causing an impoverishment of
the datasets published by GLAMs. In addition, the fact that each collection can
state only a single point of view is making it harder (and often impossible) to
interlink datasets that use, voluntarily or involuntarily, different points of view.
To address this issue, we propose a combination of two approaches. First,
contexts: a method to allow the expression of conflicting opinions on all aspects
of an artefact. Second, conjectural graphs: a way to encode scholarly statements
as qualified arguments, and not as absolute universal truths, as required by RDF.
In addition, we present a mechanism, named collapse to reality, that makes
it possible, in a limited and controlled way, to turn specific conjectural graphs
into plain RDF statements that can be linked to and made interoperable with
other RDF-based datasets.
In contrast to other techniques, our approach does not require the publish-
ers of datasets to settle on a single point of view upfront. Instead, institutions
can publish rich datasets containing multiple interpretations of the same data,
leaving to the users or to the scholars the ability to accept certain conjectures
while refuting others.
The ability to express conflicting opinions, different hypotheses and, in gen-
eral, all that is part of a lively and meaningful scholarly debate, should repre-
sent a key requirement for all data formats used to describe cultural artefacts.
7
W3C PROVenance Interchange Ontology: https://www.w3.org/TR/prov-o
8
Semantic Publishing and Referencing Ontologies: http://purl.org/spar
9
Grounded Annotation Framework: http://groundedannotationframework.org
10
ICA Records in Contexts Ontology: https://www.ica.org/standards/RiC/ontology
115 G. Barabucci et al.
This is not currently the case. We hope that the proposed contexts and con-
jectural graphs will provide a productive start point for a theoretical, practical
and methodological reflection on how to beyond the current state of the art and
address what is probably the biggest challenge that cultural institutions face
when it comes to reaching and engaging scholars and the citizens alike.
References
1. Barabucci, G.: The CMV+P document model, linear version. In: Bleier, R.,
Winslow, S.M. (eds.) Versioning Cultural Objects: Digital Approaches. IDE (2019),
ISBN 978-3750427020
2. Barabucci, G., Tomasi, F., Vitali, F.: Modeling complexity in public history and
cultural heritage. In: Noiret, S., Tebeau, M., Zaagsma, G. (eds.) Handbook Digital
Public History. De Gruyter (2021), ISBN 978-3-11-043922-9
3. Barabucci, G., Zingoni, J.: PROSO: prosopographic records. In: Tomasi, F., Vitali,
F. (eds.) Proceedings of the 1st International ACM Workshop on Collaborative
Annotations in Shared Environment: Metadata, vocabularies and techniques in
the Digital Humanities, DH-CASE@DocEng 2013, Florence, Italy, September 10,
2013. pp. 3:1–3:7. ACM (2013). https://doi.org/10.1145/2517978.2517982
4. Bradley, J., Short, H.: Texts into databases: The evolving field of new-style proso-
pography. Literary and linguistic computing 20(Suppl), 3–24 (2005)
5. Carothers, G., Seaborne, A.: RDF 1.1 TriG. W3C recommendation, W3C (Feb
2014), https://www.w3.org/TR/trig/
6. Chen, P.P.: The entity-relationship model - toward a unified view of data. ACM
Trans. Database Syst. 1(1), 9–36 (1976). https://doi.org/10.1145/320434.320440
7. Daquino, M.: Mining Authoritativeness in Art Historical Photo Archives. Semantic
Web Applications for Connoisseurship. Ph.D. thesis, University of Bologna (2019)
8. Daquino, M., Giovannetti, F., Tomasi, F.: Linked data per le edizioni sci-
entifiche digitali. il workflow di pubblicazione dell’edizione semantica del
quaderno di appunti di Paolo Bufalini. Umanistica Digitale 3(7) (Dec 2019).
https://doi.org/10.6092/issn.2532-8816/9091
9. Daquino, M., Mambelli, F., Peroni, S., Tomasi, F., Vitali, F.: Enhancing semantic
expressivity in the cultural heritage domain: Exposing the zeri photo archive as
linked open data. ACM Journal on Computing and Cultural Heritage 10(4), 21:1–
21:21 (2017). https://doi.org/10.1145/3051487
10. Doerr, M., Gradmann, S., Hennicke, S., Isaac, A., Meghini, C., Van de Sompel, H.:
The europeana data model (EDM). In: World Library and Information Congress:
76th IFLA general conference and assembly. vol. 10, p. 15 (2010)
11. Gandon, F., Corby, O.: Name That Graph or the need to provide a model and
syntax extension to specify the provenance of RDF graphs. In: W3C Workshop -
RDF Next Steps. W3C, Palo Alto, United States (Jun 2010)
12. Groth, P., Gibson, A., Velterop, J.: The anatomy of a nanopublication. Inf. Serv.
Use 30(1-2), 51–56 (2010). https://doi.org/10.3233/ISU-2010-0613
13. Peroni, S., Tomasi, F., Vitali, F.: Reflecting on the europeana data model. In:
Agosti, M., Esposito, F., Ferilli, S., Ferro, N. (eds.) IRCDL 2012, Bari, Italy,
February 9-10, 2012, Revised Selected Papers. Communications in Computer and
Information Science, vol. 354. Springer (2012)
14. Vogeler, G., Vasold, G., Schlögl, M.: Von IIIF zu IPIF? ein Vorschlag für den
Datenaustausch über Personen. In: Sahle, P. (ed.) DHd 2019 Digital Humanities:
multimedial & multimodal. (2019)