=Paper=
{{Paper
|id=Vol-1772/paper2
|storemode=property
|title=Steps Towards Accessing Digital Libraries Using Narratives
|pdfUrl=https://ceur-ws.org/Vol-1772/paper2.pdf
|volume=Vol-1772
|authors=Carlo Meghini,Valentina Bartalesi,Daniele Metilli
|dblpUrl=https://dblp.org/rec/conf/aiia/MeghiniBM16
}}
==Steps Towards Accessing Digital Libraries Using Narratives==
https://ceur-ws.org/Vol-1772/paper2.pdf
Steps Towards Accessing Digital Libraries
Using Narratives
Carlo Meghini, Valentina Bartalesi, Daniele Metilli
ISTI “Alessandro Faedo” – CNR, Pisa, Italy
{carlo.meghini,valentina.bartalesi,daniele.metilli}@isti.cnr.it
Abstract. One of the main problems of the current Digital Libraries
(DLs) is the limitation of the informative services offered to the users.
Indeed, DLs provide simple search functionalities which return a list of
the information objects contained in them. No semantic relation among
the returned objects is usually reported which can help the user in ob-
taining a more complete knowledge on the subject of the search. The
introduction of the Semantic Web has the potential of improving the
search functionalities of DLs. Many cultural institutions have represented
their metadata into formal descriptions encoded by means of formal lan-
guages such as RDF and OWL. In this context, the aim of our research
is to introduce the narrative as a new search functionality which does
not only return a list of objects but presents a narrative, composed of
events that are linked to the objects of the library and endowed with a
set of semantic relations connecting these events into a meaningful se-
mantic network. The paper presents the first theoretical achievements
on a model for representing narratives.
Keywords: Narratology, Digital Libraries, Semantic Web, Ontologies
1 Introduction
Digital libraries (DLs) are information systems that offer services over large
sets of digital objects [23]. The traditional search functionalities of DLs, such as
Europeana1 , consider that users express their information need through a natural
language query, and the digital library returns a ranked list of digital objects.
This approach works well on the web, where the objects of the search are rich text
pages with images and links to other pages, but it performs poorly on most DLs.
The reason is that the digital objects contained in them (e.g. representations of
books, photographs) are not meant to be read and navigated on the fly like web
pages. As a result, the response to a web-like query on a digital library is typically
a ranked list of metadata descriptors. In our study, we aim at overcoming this
limitation of current DLs by introducing a new first-class search functionality:
the narrative. The vision is that a user searching for a cultural heritage item like
the Baptistery of Florence in Europeana would obtain in response not the ranked
1
http://www.europeana.eu/portal/
list of objects concerning the Baptistery but rather a narrative about it, made
up of a list of events that compose its history, linked to the objects of the digital
library that contextualize them. Our study aims to introduce this new search
functionality using Semantic Web technologies, and developing a formal ontology
for representing narratives. In order to reach this goal, we studied the narratology
and computational narratology literature to identify the basic structure of a
narrative. We also reviewed the Artificial Intelligence (AI) literature to identify
the logical components of the narrative structure (e.g. events, fluents, agents),
and give their logical definitions. Then, we developed a conceptualization of the
narrative structure, as derived from the above background. Finally, in order to
develop an ontology to represent narratives, we evaluated the CIDOC CRM2 as
reference ontology [11]. The evaluation was based on the mapping between the
logic components of narratives and the terms included in the CRM.
The paper is structured as follows: Section 2 presents the narratology and
computational narratology background and Section 3 an overview of the related
works. In Section 4 an analysis of the AI literature in order to identify the formal
components of narratives is reported. Section 5 presents a conceptualization of
the narrative structure. In Section 6, a mapping between the formal components
of narratives and the CRM is reported. Section 7 presents a brief discussion of
the results of the mapping. Finally, Section 8 reports our conclusions.
2 Background
2.1 Narratology
In literary theory, narratology is a discipline that studies the narrative structure
and the logic, principles, and practices of its representation [24]. The earliest
antecedent to modern narratology can be found in the Poetics of Aristotle, who
defines a narrative as the imitation of real actions that forms an argument whose
fundamental units, or events, can be arranged in a plot [4]. However, the the-
oretical principles of narratology derive from linguistic-centered approaches to
literature defined by Russian formalists in the early 20th century. Russian for-
malism defines narratology as based on the idea of a universal pattern of codes
that operates within the content of a work. A narrative can thus be conveyed
through several different means of communication, e.g. speech, writing, gestures,
music. In particular, Vladimir Propp proposed a model to represent folktales as
combinations of basic building blocks, including thirty-one narrative functions
and seven roles of the characters [27]. Russian formalism distinguishes between a
fabula, defined as a series of events in chronological order, and a syuzhet, which is
the particular way the story is narrated by its author [27]. The theory of narratol-
ogy was further developed by mid-20th Century European structuralism. Claude
Lévi-Strauss outlined a grammar of mythology [17]; Tzvetan Todorov was the
first to coin the term narratologie [29]. In the Cognitive Narratology [14] per-
spective, narratology is considered a psychological phenomenon, and narrative
2
Conceptual Reference Model
11
aspects have to be studied from a cognitive perspective. Currently, there is no
universally accepted definition of narrative structure. For instance, Crawford [9]
claims that a narrative is a high-level structure based on causality, but not on
spatio-temporal relations. In addition to the fabula and syuzhet, Bal [5] defines
a third level that constitutes the concrete representation of the content that is
conveyed to the audience.
2.2 Computational Narratology
Computational narratology studies narratives from a computational perspective,
focusing on “the algorithmic processes involved in creating and interpreting nar-
ratives, modelling narrative structure in terms of formal computable representa-
tions” [19]. Computational narratology is based on engineering disciplines aiming
at developing AI systems for reproducing human-like narrative behaviour and
intelligent interfaces for interacting with narratives [20]. In AI this term refers
to storytelling systems, i.e., software generating stories described in natural lan-
guage, implementing linguistic formalisms [7]. Some of the early storytelling sys-
tems are TALE-SPIN [22] abd UNIVERSE [16]. These are hybrid systems that
implement a computer model of creativity in writing. Recently, ontologies were
used to generate narratives. For example, it happened in the MAKEBELIEVE
[18] and ProtoPropp [13] projects. In our research context, these systems are in-
teresting because they use formal models that provide a symbolic representation
of a narrative.
3 Related Works
Narratives have been recently proposed to enhance the information contents
and functionalities of DLs, with special emphasis on information discovery and
exploration. For example, Bletchley Park Text [26] is a semantic application
that allows users to explore collections of museums. The semantic description
of the resources is used to organize a collection into a personalized web site
based on the chosen topics. In the PATHS project [12] a system that acts as
an interactive personalized tour guide through existing digital library collections
was created. In this system the events are linked by inherence relations. Within
the CULTURA project [1] a tool to enrich the cultural heritage collections with
guided paths in the form of short lessons called narratives was developed.
4 Components of Narratives in Artificial Intelligence
We conducted a study of the AI literature in order to identify the formal com-
ponents of narratives. In this Section we report the logic definitions of the com-
ponents of narratives as defined in the Event Calculus (EC) theory [15], with a
brief mention also to the Situation Calculus (SC) [21] as related background. The
SC is a logic language for representing and reasoning about dynamical domains.
In dynamical domains the scenarios change because of the actions performed by
the agents. The basic elements of the calculus are:
12
– Situations represent a sequence of actions. The situation is a state resulting
from these actions. Sequences of actions are represented using the function
symbol do, so that do(a, s) represents the new situation after that the action
a was performed in situation s.
– Fluents are functions and predicates that vary over situations (e.g. location
of the agent). Fluents are situation-dependent components used to describe
the effects of actions.
– Actions are changes performed by agents from a situation to another in a
dynamic world.
SC works well when there is a single agent performing instantaneous, discrete
actions. When actions have duration and can overlap with each other the alter-
native formalism is the EC, which is used for reasoning on actions and changes
and it is based on points rather than on situations. EC allows reasoning over in-
tervals of time and fluents are time-dependent rather than situation-dependent.
EC axioms define a fluent true at a point in time if “the fluent was initiated by
an event at some time in the past and was not terminated by an intervening
event” [28]. Davidson [10] defines actions as a subclass of events. In Davidson’s
opinion, the distinct sign between general events and actions is the intentionality
of actions. Like SC, Event Calculus has actions. However, Davidson’s distinction
between events and actions is not present. In the EC actions are events. In the
following list we report the logical definitions of the components of narratives.
– Generalized events. In the context in which actions and objects are aspects
of a physical universe with a spatial and temporal dimension, a generalized
event is a space-time chunk. This abstraction allows thinking to generalize
event concepts like actions, locations, times, fluents and physical objects.
– Mental events and mental objects. The relations between an agent and “men-
tal objects” like believes and knows, are called propositional attitudes, be-
cause they identify attitudes that agents can have towards a proposition [28].
Using the reification method it is possible to turn a proposition into an ob-
ject that could become an argument of a sentence (because only terms and
not sentences can be arguments of predicates).
– Narrative. A narrative is a possibly incomplete specification of a set of actual
event occurrences [25]. The EC is narrative-based, unlike the standard SC
in which an exact sequence of hypothetical actions is represented.
5 A Conceptualization of the Narrative Structure
Following the narratology theory and the components of narratives as defined in
the AI literature, we envisage a narrative as consisting of three main elements:
1. the fabula, representing the fabula as defined by the Russian formalism, i.e.
the sequence of the events that composes the story in chronological order;
2. the narrations, one or more texts that narrate the fabula, and that corre-
spond to Bal’s definition of presentation;
3. a reference function that connects the narrations to the fabula and allows
deriving the syuzhet (or plot) as defined by the Russian formalism.
13
Fabula. The fabula is built on top of events, an event being an action or occur-
rence taking place at a certain time at a specific location. In a fabula, events are
connected to each other by three kinds of relations:
– a mereological relation, relating events to other events that include them as
parts, e.g. the birth of Dante Alighieri, the major Italian poet of the late
Middle Ages, is part of the life of Dante;
– a temporal occurrence relation, associating each event with a time interval
during which the event occurs. An event occurs before (or during, or after)
another if and only if the period of occurrence of the former event is before
(or during, or after) the period of occurrence of the latter. We formalize
these relations between events using Allen’s temporal logic [3];
– a causal dependency relation, relating events that in normal discourse are
predicated to have a cause-effect relation in the narrator’s opinion, e.g. the
eruption of the Vesuvius caused the destruction of Pompeii. We are not in-
terested in modeling the mechanical causal relationships that connect events
in a physical process. We are rather interested in a more generic notion of
causality, whereby the connected events may be years apart in time and the
causal connection may be indirect.
Narrations. Each narration of a fabula consists of one or more narrators and a
text, which is authored by the narrator(s).
Reference. The reference function connects each portion of text that narrates
an event to the narrated event. In order to model reference we need to identify
textual units, which we call narrative fragments (or simply fragments), each of
which narrates one or more events. Notice that the reference function allows
deriving the plot of the narrative. Indeed, by visiting the text of the narration in
its natural order, it is possible to access the narrative fragments and, via these,
the events in the fabula, in the order established by the narrator.
We provided a specification of the above conceptualization in mathematical
terms. This allows us to concentrate on the proper capturing of the notions
highlighted above, postponing any language consideration to a later stage, once
the mathematical specification will have brought forward the required machinery.
A detailed description of the mathematical specification is reported in [6].
6 CRM Mapping of Narratives Components
In order to develop a semantic model to represent narratives, on top of which
to develop the new search functionality for DLs, we used the CIDOC CRM
(CRM for short) as reference ontology [11]. This choice was determined by the
fact that the CRM is an ISO standard ontology and promotes a shared under-
standing of cultural heritage information through a semantic framework that
any cultural heritage organization can use to map its cultural objects. Further-
more, the CRM terms allow mapping the most important logic components of
narratives as reported below. The following definitions are extracted from the
CRM official documentation3 .
3
http://www.cidoc-crm.org/docs/cidoc\_crm\_version\_6.2.pdf
14
– Event. In the CRM, the class E5 Event corresponds to the definition of event
in the EC theory. This class “comprises changes of states in cultural, social
or physical systems, regardless of scale, brought about by a series or group
of coherent physical, cultural, technological or legal phenomena”.
– Action. Actions identified by Davidson correspond to the class E7 Activity in
the CRM. “This class comprises actions intentionally carried out by an actor
that result in changes of state in the cultural, social, or physical systems
documented”.
In order to refine our mapping, we analysed the single types of generalized events
that are useful to represent the components of events and we mapped them with
the classes of the CRM.
– Agent. The CRM uses the class E39 Actor to represent people, either indi-
vidually or in groups, who have the potential to perform intentional actions
of kinds for which someone may be held responsible.
– Location. This concept is represented in the CRM through the class E53
Place. “This class comprises extents in space, in particular on the surface of
the earth [...] independent from temporal phenomena and matter”.
– Time. CRM uses the class E52 Time-Span to represent this concept. “This
class comprises abstract temporal extents, in the sense of Galilean physics,
having a beginning, an end and a duration”.
– Physical Objects. In the CRM the class E18 Physical Thing describes “all
persistent physical items with a relatively stable form, man-made or natural”.
– Mental Objects. In the CRM the class E28 Conceptual Object comprises
“non-material products of our minds and other human produced data that
have become objects of a discourse about their identity, circumstances of
creation or historical implication”.
The relations defined on the events (and actions) of the fabula, are expressed
by the following CRM properties:
– The mereological relation is represented using the property P9 consists of,
which associates an instance of E4 Period with another instance of E4 that
is defined by a subset of the phenomena that define the former. E5 Event is
a subclass of E4, therefore P9 can be used also as an event mereology.
– The event occurrence relation is represented by the CRM property P4 has
time-span, which describes the temporal confinement of an instance of an
E2 Temporal Entity and therefore of an event. Because the period of occur-
rence of an event may not be known, the CRM allows to directly relate events
based on their occurrence time. To this end, it introduces seven properties
(P114 -P120 ) mirroring Allen’s temporal logic [2].
– The causality relation is represented by introducing a new property of causal
dependency. This is the only proposed extension to the CRM, whose only
causal property P17 was motivated by cannot be used for narratives since
it relates activities but not events. Indeed, CRMsci4 , an extension of CRM
for science, defines a direct causality relation, via the property O13 triggers,
which “associates an instance of E5 Event that triggers another instance of
4
http://www.ics.forth.gr/isl/CRMext/CRMsci/docs/CRMsci1.2.2.pdf
15
E5 Event with the latter [...]; in that sense it is interpreted as the cause”.
However, this property is inadequate to the needs of narratives, whose events
may be separated by possibly long periods of time.
7 Discussion
As result of the mapping, the logic components of narratives we identified can be
defined using classes and properties of the CRM. Furthermore, the CRM provides
several subclasses of E5 Event which identify types of event (e.g. E63 Beginning
of Existence, E65 Creation). These subclasses are useful to establish a first cat-
egorization of events. Furthermore, another advantage of the use of the CRM is
the existence of CRMinf5 , an extension of the CRM, which we are considering
to describe the inference processes of the narrator. Indeed, in addition to the
components of narratives, we considered to represent the inferential process of a
narrator who reconstructs the events that compose a narrative starting from the
study of the primary sources. Our model aims at describing the knowledge prove-
nance, i.e. the process of tracing the origins of knowledge [8]. Reconstructing the
inference process is important to evaluate the trustworthiness of the knowledge.
A user can determine the quality of the knowledge based on its derivations. CR-
Minf aims at representing data attribution, scientific concepts of observation,
inferences and beliefs. For these reasons, we have adopted the CRM as reference
vocabulary to construct an ontology for representing narratives.
8 Conclusions and Future Work
In this paper we have presented the first theoretical achievements on a model for
representing narratives. In particular, we have presented a review of the works
on narratives and described our project relating it to developed models both
in the cultural heritage and in the digital library fields. The final aim of our
research is introducing narratives as as a new search functionality for DLs. As
output of a query, this new search functionality should not return just a list of
objects, as the current DLs report as output, but it should present a semantic
network we called narrative, composed of the events of the narrated story. The
events and their contextualizing components, e.g. digital objects, are connected
by semantic relations meaningful to the user.
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