=Paper=
{{Paper
|id=Vol-3286/07_paper
|storemode=property
|title=Visual Exploration of Digital Cultural Artifacts (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3286/07_paper.pdf
|volume=Vol-3286
|authors=Eleonora Bernasconi,Miguel Ceriani,Massimo Mecella
|dblpUrl=https://dblp.org/rec/conf/aiia/BernasconiCM22
}}
==Visual Exploration of Digital Cultural Artifacts (short paper)==
https://ceur-ws.org/Vol-3286/07_paper.pdf
Visual exploration of digital cultural artifacts (short
paper)
Extended Abstracts of doctoral thesis
Eleonora Bernasconi1 , Miguel Ceriani2 and Massimo Mecella1
1
Sapienza Università di Roma, Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti, via
Ariosto, 25, 00185 Rome, Italy
2
Università degli Studi di Bari Aldo Moro, Dipartimento di Informatica, via Edoardo Orabona, 4, 70125 Bari, Italy
Abstract
This work presents the extended abstract of the doctoral thesis about the Visual Exploration of digital
cultural artifacts. We exploited the synergy between semantic technologies and knowledge exploration
tools for digital libraries, proposing a system which includes: semantic enrichment of textual content; a
user interface for search and exploration of a digital library through visual navigation of a knowledge
graph of topics; the extensions of the central system to leverage the extracted knowledge. For example,
interface components hide complex queries to find interesting topics for users; an integrated system to
semi-automatically manage the knowledge contained in the images, generating video trailers of books;
and a collaborative system to improve extracted data quality.
Keywords
Semantic Web, Knowledge Graph, Visual Search Interface, Digital Library, Digital Humanities,
1. Research Context
Searching and exploring a vast text corpus has often arisen as a human need. Traditionally, the
search process is based on manually curated metadata classifying documents by arguments,
authors, metadata, etc. Albeit the metadata that used to be stored in physical cabinets is now
stored in databases, the process often remains similar.
Although being a decisive paradigm, the maintenance of metadata is costly and becomes
progressively more expensive and less reliable with the increase of required detail. The transition
to electronic documents (either created natively as such or digitized) enables the direct text-
based search of the content. Text-based search for the full content of documents is a powerful
tool. However, it comes with its limitations due to the inherent ambiguity of natural languages
and the need for the user to anticipate the actual words used in the content, as the machine
cannot capture what the user and the corpus mean. This is called the semantic gap. Statistical
methods can be successfully used for query expansion, mitigating the issue, but the user has
1st Italian Workshop on Artificial Intelligence for Cultural Heritage (AI4CH22), co-located with the 21st International
Conference of the Italian Association for Artificial Intelligence (AIxIA 2022). 28 November 2022, Udine, Italy.
Envelope-Open bernasconi@diag.uniroma1.it (E. Bernasconi); miguel.ceriani@uniba.it (M. Ceriani); mecella@diag.uniroma1.it
(M. Mecella)
Orcid 0000-0003-3142-3084 (E. Bernasconi); 0000-0002-5074-2112 (M. Ceriani); 0000-0002-9730-8882 (M. Mecella)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
no control of the process. Semantic enrichment methods, as named-entity recognition and
linking (NERL) [39, 40], aim to bridging the semantic gap between raw text and concepts, by
associating words in the documents with entities in a knowledge base, often a knowledge graph
(KG). NERL successfully enabled users to search and analyze text corpora [41] more effectively.
Nevertheless, the navigation of semantic relationships (with their meaning, rather than just as
generic connections) between extracted entities has seldom been adopted as a method for the
exploration of a corpus; even if it is known that the cognitive processes in library searching
are generally more complicated than a single topic-based search [42]. Also, while knowledge
extraction methods as NERL are now broadly used by big players in the industry as well as in
academic projects, their usage by small to medium size organizations (which often have text
corpora, either private or public, that they struggle to manage in a structured way consistently)
is still minimal, in part due to the lack of an established standard workflow. The idea proposed
in this thesis to address the problems identified concerns a system that, through the use of:
• artificial intelligence techniques, which extract information from unstructured sources
(such as text and images);
• semantic technologies that give an unambiguous meaning to the extracted concepts and
connect the information with other knowledge bases;
• an interface for explore and query the knowledge graph;
• an interaction paradigm which supports the serendipity effect to discover unexpected
things.
allows anyone possessing a digital library to extrapolate, reuse and promote its contents.
Building and maintaining institutional systems of organization of knowledge and related
datasets in libraries required years of work for a highly skilled and trained workforce. Knowledge
graphs in the domain of libraries and digital humanities demonstrate how the application of
automatic knowledge extraction and semantic enrichment to large-scale corpora opens up a
spectrum of possible new research questions that, until now, were difficult to answer with
existing methods.
Exploiting the opportunities in the digital humanities research field poses many methodolog-
ical and technical challenges.
• Novel user interface and interaction paradigm are needed to support users in viewing,
annotating, and systematically analyzing relevant parts of possibly large digitized cor-
pora. Users could express relevance by selecting corresponding concept definitions in
knowledge graphs.
• Scalable text-mining and machine-learning techniques are needed to systematically and
efficiently analyze and compare the characteristics, contents, and relationships of concepts
expressed in knowledge graphs within and across corpora.
• Algorithms are needed that support users in detecting, contextualizing, and analyzing
various forms of expressions and associated narrative techniques in corpora spanning an
extended period, in which the syntax and semantics may have been subject to constant
change.
For these reasons, the need arises to propose:
• the development of tools and scalable techniques for aligning large-scale, multi-media
corpora with concepts expressed in knowledge graph;
• support in knowledge exploration through a novel interaction paradigm based on the
principle of serendipity that enables users to discover unexpected things.
• validation mechanisms ensure trust in data quality when humans curate data with different
levels of expertise and result from automatic processes.
2. Research Objectives and contributions
The various research phases related to the study of the state of art, the identification of open
challenges and the design development and evaluation process of the proposed solution will be
discussed in detail below.
2.1. Studying the state-of-the-art
A digital library’s knowledge extraction and management are receiving increasing attention in
industrial and academic research fields. Thousands of publications use artificial intelligence
and semantic technologies tools to reach the research goals.
Forty-four tools were selected and classified by: interaction paradigm used, such as Node-
Link visualization [24, 27, 28, 23], tabular visualization or visual query composition [34, 16, 20];
type of information viewed (data [31], model visualization [18, 17], data to model visualization
[32, 19, 33]; complexity reduction strategies [29, 26, 30, 22] to optimize the search results
viewed; background characteristics, such as semantic enrichment and knowledge extraction
[53, 51, 48, 55, 52]; the use of semantic annotation (automatic [47], semi-automatic [50, 54] and
manual [49]). Furthermore, many tools face the challenge of exploring the contents of a digital
library, but two, in particular, go in the same direction as this work. Yewno Discover [45] is
an integrated system that offers classification and visual exploration of academic materials to
help scholars in their research. However, it is not adaptable and flexible to different contexts of
use, except with ad hoc adjustments. Furthermore, concerning the proposed system, it makes
limited use of the KG structure for exploration, which is at the core of the research questions
posed here.
Sampo-UI [46] is a framework that provides a set of reusable and extensible components,
application state management, and a read-only API for SPARQL queries, which can be used to
create a user interface for a semantic portal. Unlike Sampo UI, the system proposed in this work
also offers a knowledge extractor service from unstructured data and a semantic enrichment
service.
From literature emerges like the knowledge graphs are a prominent answer to disseminating
cultural heritage challenge. The nature of KG is integrability[38]. This feature allows connecting
different cultural domains on the web in the form of linked open data, thus promoting the
dissemination of cultural heritage. Furthermore, KG can be explored and interrogated with
complex queries favouring the discovery of new knowledge (serendipity).
Compared to the tools analyzed so far, we propose a tool with the incremental exploration
of knowledge graph information (supporting the principle of serendipity) with the novelty of
application to the exploration context of a digital library. Our system aims to facilitate the
search, viewing and exploration of books or documents that deal with the information sought.
Among the tools analyzed, no system focuses on exploring a catalogue of books that exploits
the interaction paradigm proposed in this work and detailed in section 2.2.
2.2. Designing and developing an original system
Once that concrete open challenges have been individuated, the pursued task is solving them.
The good methodological practices learned in the research path bring ideas and instruments to
approach such problems. However, the main contribution of this research was to go beyond.
From the literature and a study conducted with five researchers belonging to humanities,
common behaviours were identified for searching a digital library for content[11, 37, 12].
The main general supported search behaviors are the following:
• find documents relevant to a specific topic;
• expand or specialize searches by moving through related topics;
• have visibility of available related resources, which could potentially be of interest;
• visually organize the resources found by considering their relationships and properties;
• find topics and documents at the crossing of multiple topics, possibly of different kinds
(places, people, time periods, etc.).
For the sake of the analytic approach, the experimentation effort was framed through a set of
research goals[10].
• Would users exploring a corpus of text profit from the semantic navigation of the associ-
ated KG of topics?
• What kind of user interface would effectively support such a navigation?
• What kind of users, scenarios, and tasks would benefit from this interaction paradigm?
• Does building and maintaining a semantic enrichment and KG creation pipeline necessar-
ily involve high upfront costs and highly skilled developers?
To answer the above questions, the following hypotheses have been formulated.
• Users will be able to effectively explore a text corpus through a KG-based user interface,
which offers the following main functions:
– a. finding concepts through text search (among the ones pertinent to the specific
domain),
– b. visually navigating the concepts and their relationships, and
– c. showing documents relevant to the selected concept.
• The method, given a corpus of texts in a specific domain, will benefit both users with little
knowledge of the domain (by supporting semantically-relevant discovery) and domain
experts (by enabling a topic-oriented visual organization of the documents).
• It is feasible to build a ready-to-use complete system, including both semantic enrichment
pipeline and web-based front end, which is able, with only some configuration, to be
applied to any specific corpus to enable the KG-based exploration.
While the first two research questions and related hypotheses are relevant for investigating
the benefits of the proposed approach for the end users, the last research question and hypothesis
investigate the usefulness and portability of such a system to different contexts of use.
2.2.1. Evaluation of the proposed system
We evaluated the proposed solution, based on a corpus search and exploration paradigm with the
transparent use of knowledge graphs to improve and measure the level of acceptance (strengths
and weaknesses) by the users of the system. The system has been tested in the context of a
specific use case: exploration of the book catalog of medium-size publishing house, specialized
in classical antiquity. The anticipated final users of the tool can be roughly classified in two
categories:
• domain experts who may adopt a new approach to search and discover resources in the
context of their research;
• curious people who want to explore new topics.
The evaluation process[9] lasted two years and was characterised by three phases:
• an evaluation of the extracted data, from the point of view of quality and usefulness, with
the help of domain experts;
• a small-scale qualitative user-based evaluation of the tool with a some researchers of the
field;
• a larger and richer user-based evaluation of the tool, both on its own and in comparison
with other existing solution, which involved both students and researchers of the field.
2.3. Extensions of the proposed system
After the proposed system was positively evaluated, it was decided to proceed by developing
some applications of potential interest for the domain of Digital Humanities and Digital Libraries
as extensions of the primary system. In particular, the same interaction paradigm was applied
to two different domains concerning the humanities (ancient symbols and ancient places). New
functionality has been added downstream of the system that processes the images present in
the digital library, recognizing the objects represented, which are inserted into the explorable
knowledge domain. Also presented is the extension that allows domain experts to validate the
quality of automatically extracted reports.
2.3.1. Application of the interaction paradigm to different domain
The availability of a tool such as the one proposed in this work would foster collaboration
among the researchers in the area, and could attract curious [36], casual, users by easing the
diffusion of niche topics like those regarding ancient documentary texts[7]. Offering a pipeline
to build a custom KG, can (i) introduce a common vocabulary for researchers in the area, (ii)
share a common understanding of how concepts are related, (iii) enable the reuse of domain
knowledge, and (iv) make domain assumptions explicit. In addition the graphical user interface
can be exploited to allow researchers (i) to explore the KG, (ii) to search and explore relations
and connections between resources, (iii) to make historical-geographical implications, and (iv)
to discover new facts about the research field.
Another extension arises from the researchers’ need to explore knowledge bases in a carto-
graphic context[8]. This is done by searching for links between the different topics related to a
place or a key term, in such a way as to reveal unexpected connections during the exploration
of contents and, thus, generating new ideas.
2.3.2. Knowledge extraction from images
Another extension of the primary system involved the addition of the automatic extraction
of the contents of the images present in a digital library[4, 5, 6]. This extension has allowed
the reuse of this information to generate new applications, for example, the semi-automatic
creation of book trailers to support storytelling for digital libraries.
Multimedia storytelling is an effective and engaging method to convey information in multiple
domains. Specifically, book trailers –video advertisements for books– positively influence the
desire to learn and the motivation to read. The video trailer generator system supports an
expert by gathering relevant crowd-sourced multimedia content, which, arranged as stories,
can be used to showcase a book in the form of video clips. Crucially, the expert controls how
the content is finally combined and edited rather than offering a fully automated process.
2.3.3. Validation of the automatic knowledge extraction
After a critical analysis of the entire information extraction and modeling process, the most
significant difficulties arise from problems with the quality of automatically extracted data. The
limitations found, such as OCR errors and disambiguation errors of the extracted concepts, are
part of those problems that limit the system potential, which was attested by the users during
the evaluation.
Consequently to the findings, a strategy for improvement was evaluated founded on the
following:
• improving improve as much as possible the OCR, NER, and NEL algorithms [15, 21, 25, 35]
• inserting a human control (human in the loop) to validate the automatic extractions.
At least at the moment, no algorithm can extrapolate information with optimal quality, so
the human expert needs to have the last word.
For this reason, a proposed solution[3] inserts a layer of human control so that domain
experts can validate the automatically extracted results.
Allowing domain experts to collaboratively validate information previously automatically
extracted from a Digital Library (DL) is an approach to support the incremental data quality
improvement that can be done specifically through the validation of entity linking. Furthermore,
rather than seeing just the results of the extraction process, it can be helpful for the domain
experts to trace the origin of where the AI recognized a specific entity (i.e. a “snippet” of text or
an image).
Acknowledgments
SCIBA (Regione Lazio and MIUR - Determinazione n. G07413 del 16/06/2021) project partly
supports this work. Miguel Ceriani acknowledges funding from the European program PON
Ricerca e Innovazione 2014-2020, within the project no. COD.AIM1852414, activity 2, line 2.1
and the project “Computational methods for the web economy”, DM 1062/2021.
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