=Paper=
{{Paper
|id=Vol-1509/submission_6
|storemode=property
|title=Linked Open Data Framework for Serendipity in History of Art Research
|pdfUrl=https://ceur-ws.org/Vol-1509/ITALIA2015_paper_6.pdf
|volume=Vol-1509
|dblpUrl=https://dblp.org/rec/conf/aiia/Silvello15
}}
==Linked Open Data Framework for Serendipity in History of Art Research==
https://ceur-ws.org/Vol-1509/ITALIA2015_paper_6.pdf
Linked Open Data Framework for Serendipity in
History of Art Research
Gianmaria Silvello
Dept. of Information Engineering, University of Padua, Italy
silvello@dei.unipd.it
Abstract. In this paper we outline the main lines of research for defining
a framework based on Linked Open Data (LOD) for supporting knowl-
edge creation in the Cultural Heritage (CH) field with a particular focus
on History of Art research.
We delineate the main challenges we need to deal with and we explore the
state-of-the-art in LOD publishing systems, LOD citation and author-
ity management. Furthermore, we introduce the idea of computer-aided
serendipity in History of Art research with the purpose of contributing to
the advancement of the field and to the definition of new methodologies
for entity linking and retrieval.
1 Motivation
One of the most relevant socio-economical and scientific changes in recent years
has been the recognition of data as a valuable asset. The Economist magazine
recently wrote that “data is the new raw material of business”; and the European
Commission stated that data-related “technology and services are expected to
grow from EUR 2.4 billion in 2010 to EUR 12.7 billion in 2015” [9]. The princi-
pal driver of this evolution is the Web of Data, the size of which is estimated to
have exceeded 100 billion facts (i.e. semantically connected entities). The actual
paradigm realizing the Web of Data is LOD [14], which by exploiting Web tech-
nologies, such as the Resource Description Framework (RDF) [20], allows public
data in machine-readable formats to be opened up ready for consumption and
re-use. LOD is becoming the de-facto standard for data publishing, accessing and
sharing because it allows for flexible manipulation, enrichment and discovery of
data as well as for overcoming interoperability issues. The ground breaking po-
tential of this approach resides in the semantic connections among data enabling
new knowledge creation and discovery possibilities.
The CH domain and in particular the History of Art research field, provide a
fertile ground where the LOD potential can grow and bloom; indeed, in History
of Art the preponderant way to produce new knowledge is to reveal connections
between different items (illuminated manuscripts, pictures, frescos) that can cast
a new light on an artist, an artistic movement or an art-historical period.
This potential is widely recognized by public and private agencies, which
keep investing in publishing CH resources as LOD. In the CH domain, which in
the EU alone “accounts for 3.3% of GDP and employs 6.7 million people (3% of
total employment)” [9]; as a consequence, in the last couple of years within the
Europeana digital library1 , the European Commission started a major effort for
publishing as LOD millions of multilingual and multimodal CH resources (e.g.
archival documents, illuminated manuscripts, pictures) gathered from more than
2300 institutions distributed across 36 countries.
Nevertheless, publishing CH resources as LOD on the Web, is just the first
step to enable new knowledge creation. To realize the full potential of the Web
of Data, we need to devise innovative methods for seeking and creating links
between datasets and to design user-oriented services for exploiting them. To
this purpose, History of Art is a fruitful domain that, not only can benefit,
but also can assist the development of these new methods because it provides:
rich and heterogeneous information needs, ample structured and unstructured
resource datasets and a proactive research community accustomed to seek new
connections between entities.
The aim of this paper is to outline the basics to design a LOD framework
for supporting knowledge creation in the History of Art domain and to outline
some of the scientific challenges we have to face. Overall, the framework we
envision pursues two main objectives: (i) to provide LOD-ready functionalities
to create, access and cite new knowledge and to represent and retain authority
information; (ii) to assist domain experts in the creation of semantic connections
and to empower them with (semi) automatic serendipity capabilities.
Accomplishing the first goal will overcome current LOD publishing practice
where the data are produced and stored by local systems and then mapped,
synchronized and published as LOD; this procedure requires a multiplication
of investments and produces scarcely connected datasets given that the expert
knowledge is not tackled in the publishing activity. Our idea is to conflate data
creation, connection, and publishing in one phase and let computer scientists
and domain experts to work back-to-back joining knowledge and efforts. In this
context there is the need also to provide concrete solutions for fundamental but
overlooked issues as data citation and authority management.
Achieving the second goal will push History of Art research boundaries by
joining the experts’ knowledge and intuition with automatic tools able to retrieve
relevant entities in the Web of Data. To this purpose, from the computer science
point-of-view, we need to devise query models from the experts information
needs, envisage and define methods for mapping entities to queries, use them
for retrieving target entities and then provide entity rankings. Current solutions
are biased toward Web search where user needs are expressed using very few
terms; as a consequence, they employ flat models of user inputs where contextual
information, text, images, links, categories and feedbacks, when available, are
considered at the same level and equivalent one to the other.
The History of Art domain provides us with rich and heterogeneous user
inputs that poses additional challenges, but that also provides the means for
advancing entity retrieval state-of-the-art. The framework we envision could also
1
http://europeana.eu/
push ahead the creation and distribution of CH resources offering new ways for
addressing consumers’ demand for data access and for greater participation in
the creativity process.
The rest of the paper is organized as follows: Section 2 presents the state-of-
the-art in LOD publishing in the CH context, serendipity oriented algorithm and
data citation methodologies. In Section 3 we present the main design lines, the
challenges we have to face for realising the envisioned framework and introduce
the art research use case on which the proposed framework will rely. In Section
4 we propose an architecture that could implement the envisioned framework.
Finally, in Section 5 we draw some final remarks.
2 State-of-the-art and some open questions
The efforts for disclosing the LOD potential within the CH field must go to-
wards the design of new methodologies for creating meaningful and possibly
unexpected semantic links between data and for managing the knowledge cre-
ated through these connections. This endeavor is needed to make the Web of
Data fully operational and so that its role can be compared to that played by
search engines over the last two decades in disclosing the full potential of the
Web.
The CH domain and particularly the History of Art not only can be beneficial
of the full exploitation of the LOD potential, but they can also provide fertile
ground where new methods and technologies can be designed, developed and ap-
plied. Indeed, in History of Art the preponderant way to produce new knowledge
is to reveal connections between different items (illuminations, pictures, frescos)
that can cast new light on an artist, an artistic movement or an art-historical
period. The most valuable connections are the unexpected ones linking elements
that may seem to have very few in common; indeed, in this domain, important
discoveries have often been done thanks to associations and connections of items
emerged also by chance in the research path identified by domain experts. This
process of discovery can be defined as serendipity and it is especially encouraged
by LOD where meaningful links between entities allow us to move across diverse
and apparently unrelated knowledge domains. History of Art is a well suited
domain where algorithms fostering serendipity within LOD can be designed,
developed and evaluated because it provides: rich and heterogeneous informa-
tion needs, ample structured and unstructured resource datasets and a proactive
community accustomed to seek new semantic connections between entities.
Given the scientific and economic impact of LOD and the leading role of CH
for triggering its potential, a lot of research is being carried out in important fields
such as database systems, information retrieval and digital libraries focusing
especially on efficient ways for storing and querying datasets [2,3], extending Web
search to entity retrieval [5] and devising sustainable methods for publishing [6]
and linking LOD [11, 13].
However, several crucial questions still remain unanswered or overlooked.
What merits to be linked? How can linking towards potentially unknown data
sources be eased? How can the serendipity process be enhanced and guided by
semantic methodologies? How can domain experts’ knowledge be exploited for
creating meaningful semantic links? How the authoritative information about
entities, activities and people involved in data creation be established and re-
tained? How new created knowledge represented by a data subset can be cited?
We can point out four main research areas concerning these questions that
must be taken into account:
– LOD-based systems: The vast majority of systems in the CH domain
are not natively LOD-based and adopt a publishing paradigm involving re-
dundancy and multiplication of costs [15]. Moreover, in most cases (e.g.
Europeana), the links to external sources are established without involving
domain experts and are rather syntactic than semantic. The framework we
envision is aimed as developing user-oriented services for native LOD cre-
ation and exploration. Moreover, it aims to provide services for connecting
data while experts are working, thus exploiting their knowledge for link cre-
ation.
– Serendipity capabilities: No system in the CH domain provides end-users
with this function. Related methodologies are proposed in the context of
entity linking and retrieval targeting Web search: they deal with sparse user
inputs [1] and general datasets [16]. These solutions employ flat models of
user inputs mainly focusing on query expansion techniques [4].
– LOD citation and authority management: Recently two EU projects
(i.e. PRELIDA2 and DIACHRON3 ) marginally considered these aspects
from the permanent preservation point-of-view, but there are as yet no
concrete solutions for LOD data. Other citation systems focused on rela-
tional data [17] or hierarchical data such as eXtensible Markup Language
(XML) [7], but they are not applicable to the LOD case. [18] proposed
an initial methodology based on named graphs and RDF quad semantics
which enables persistent, dereferenceable, variable granularity and human-
and machine-readable citations of LOD subsets, but no ready-to-use solution
has been implemented yet. Several challenges are still open in data citation
of LOD such as the citation of evolving datasets which involves temporal
aspects of RDF, the definition of equality between two citations and the
concept of citation closure [8] (something like closure of a paper references).
3 Serendipity in art research: a proposal
For what it is concerned with the serendipity algorithms the framework we envi-
sion will employ (semi) automatic methods for suggesting entities to researchers;
the idea is that this methodology could help them in their daily work by trig-
gering the exploration of new research paths leading to new knowledge creation
in History of Art. Now, we present the concrete steps we are going to follow to
2
http://prelida.eu/
3
http://www.diachron-fp7.eu/
realize the framework that we call Linked Open Data Enhanced Framework for
Serendipity in Art Research (LODESTAR).
We will rely on a concrete use case rooted in history of art research which
aim is to identify, analyze and assess the influence of the most ancient Divine
Comedy illuminated manuscript illustrations on the many representations of the
Hereafter and biblical and mythological characters that can be found in late-
XIV century and Renaissance art. Indeed, many inventions that can be found
in the first Divine Comedy illuminated manuscripts were destined to have great
success and also to be used in later manuscripts, not only with Dante’s poem,
but also with other texts and other works of art. In order to achieve large-scale
results, beside illuminations, we will also take into account products of the major
arts such as paintings, frescos and sculptures, thus bringing new knowledge on
different fields of the History of Art. The object of the research is highly original
and constitutes a field of investigation that has never been explored before.
Given its ample scope, the large diffusion of the Divine Comedy and its influence
through the centuries on many fields, this use-case offers substantial chances of
success especially for applying semi-automatic serendipity methodologies; it is
a valuable starting stage to design, apply and test methods that will be then
re-used in other use-cases and domains.
Rich and
Citable heterogeneous inputs
reference
related images
Entity extraction Source
and enhancement
Set-based
multimodal related documents entities
Input streams hierarchical
extracted from entity modeling
the environment item connections
Data
citation
domain experts descriptions
LODESTAR
work environment Adaptive
queries
modeling
Devised
queries
Entity
suggestions
Target entities
for links Rank
Authority creation model
management
LOD Cloud
Fig. 1. The workflow envisioned by the LODESTAR framework.
In Figure 1 we show the workflow realizing the objectives tackled by the
LODESTAR framework. In the center there is the work environment where do-
main experts will carry out their daily research work. This environment provides
rich and heterogeneous input streams such as free and semi-structured text, im-
ages, links, user profile and contextual information; LODESTAR is required to
automatically catch these streams that are implicit expressions of user needs and
to extract entities from them.
These entities will be sources for the entity retrieval algorithms realizing the
LODESTAR serendipity methodology shown on the right hand side of Figure
1. This would be a major shift from state-of-the art entity retrieval solutions,
which are typically employed in syntactic linking processes where input sources
are constituted by few keywords. In LODESTAR users are not asked to explicitly
issue queries, but the traces they leave behind while using the system will be
automatically gathered and exploited for devising queries. To tackle this goal
we have to employ new methods to model the extracted entities by associating
different groups of entities to different levels of relevance decided on the basis of
the user needs within the context under consideration; in other words we have
to go beyond the current “flat models” that consider all the user inputs at the
same level.
This model will allow us to devise several queries corresponding to different
entities selections and compositions that will be used to retrieve “target entities”
from the LOD cloud. The purpose of issuing multiple queries is to cover a wide
spectrum of potential user needs as well as to ease the discovery of unknown en-
tities that may trigger the creation of unexpected connections. The rank module
gathers the target entities and orders them accordingly to the user need they
tackle.
Lastly, the ranked entities are suggested to the users that will consider them
for establishing semantic connections fostering new knowledge creation.
The left-hand side of Figure 1 reports data citation and authority manage-
ment components needed to give credit to data creators (e.g. produce a di-
achronic reference to a data subset), retain context (e.g. who created a link,
when a link has been created) and control data conflicts (e.g. choose the most
authoritative statement among contradicting ones). LODESTAR has to provide
general methodologies for tackling these issues for which, in literature, there are
no concrete solutions yet. We will employ the solution proposed in [18] by ex-
tending it in order to consider also the temporal aspects of LOD datasets; indeed,
a citation should be resolved by referring to the precise version of the data that
was cited. To this end we plan to employ some RDF versioning methodology as
described in [12, 19].
4 A possible implementing architecture
Serendipity can be realized by following the workflow shown on the right of Fig-
ure 1. Firstly, we need to trace the user information within the LODESTAR work
environment such as the item being studied, annotations, items being compared
to it, free text and semi-structured text (e.g. form fields), clickstream and user
profiles. Then, we apply mining algorithms, extending existing entity extraction
solutions, for extracting phrases, entities, categories from all these input streams;
afterwards, the enhancement module, exploiting suitable ontologies, will connect
the extracted entities one with the other via automatically established semantic
links. At this stage we obtain an RDF graph of connected entities representing
a very rich combination of user needs.
There exists no ready-to-use solution for handling this “user needs graph” and
devise queries from it; LODESTAR represents the very first effort tackling this
problem. The methodology we envision is to hierarchically model the user needs
graph by associating different groups of entities to different levels of relevance
decided on the basis of contextual information.
Machine learning methods have been proposed in literature for the task of
learn classes in ontologies and constructing knowledge from user-provided ex-
amples; a possibility is to develop a similar approach to assign relevance weights
or probabilities (i.e. the estimated relatedness to the user needs) to the entities
in the graph. These weights will be exploited for modeling the user needs graph
with a innovative set-based model, i.e. the NESTOR model [10], organizing the
entities in sets with different levels relevance as shown in Figure 2.
User needs graph modeled via NESTOR Adaptive Query Modeling Queries
S + (B) = {X 2 I | X ⇢ B} = {C, D} D
I C qS + (B)
A
E E
B
B BE = B [ E D qBE
C
C D
B = B \ S + (B) B
qB
Fig. 2. A methodology for handling rich and heterogeneous user inputs and devising
queries via adaptive query modeling.
NESTOR comes with a whole bunch of defined set-operations that will be
exploited to design an algorithm for selecting alternative entity combinations
within the graph; from these selections a bunch of queries covering a wide spec-
trum of potential user needs will be devised.
The queries will be issued against the LOD cloud for retrieving groups of
target entities; each group will be ordered by a ranking model built on solutions
for structured search, e.g. statistical language models, and then aggregated by
exploiting meta-search engine techniques to be presented as link suggestions to
the users.
This envisioned methodology can be embedded in a more comprehensive
architecture as shown in Figure 3 composed by 4-staked layers concerning im-
portant aspects of a system architecture: scalability, robustness, reactivity and
suitability.
The first layer is designed by considering that LODESTAR will deal with
big datasets and large user and machine requests. Therefore, in order to provide
linear scalability and fault tolerance, several technologies should be taken into
L DESt AR
framework
Suitability
Domain Expert Community
Analysis Search Visualization Linking Citation
Service Service Service Service Service
CREATE FIND EXPLORE INNOVATE ATTRIBUTE
Reactivity
Entity
SPARQL User's Inputs Adaptive
Extraction and
end-point Modeling Querying
Enhancement
SERENDIPITY CAPABILITIES
Robustness
Data Authority
Ontology Data Citation
Integration Management
Map-Reduce Hadoop
Mapping and
Programming Distributed File
Synchronization
Scalability
Model System
Distributed Multinode Cluster
Fig. 3. A possible architecture implementing the LODESTAR framework.
account, in particular: Apache Cassandra4 as distributed column store solution
and Apache Hadoop5 in conjunction with the Map-Reduce functionalities to
provide reliable and distributed computing.
Scalability is also concerned with the selection of datasets that will serve
the use-case we have in mind; in a real-world scenario we cannot assume that
all relevant data will be available as LOD because even if many relevant CH
institutions and systems are LOD-compliant – e.g. Europeana6 , the Library of
Congress, the British Library, the French National Library, and Cultura Italia7
– others still are not – e.g. Artstor, Dante On-line8 , and the Web gallery of Art.
To deal with all these sources, it is necessary to employ a mapping and
synchronizing module that will harvest, map to LOD and store these data. An
apposite data integration module will blend different resource kinds (as part of
the robustness layer). All the resources will be represented accordingly to ontolo-
gies defined within the domain of interest; computer scientist and art historians
will jointly carry out this activity by defining an RDF Schema and reusing ex-
isting vocabularies to establish bridges with third-party ontologies – e.g SKOS,
Europeana Data Model, CIDOC-CRM – that will also be exploited for serendip-
ity.
In the reactivity layer, we could provide a SPARQL end-point, which will
work as a data provider allowing third-party services to discover, re-use and
connect to the LODESTAR data. This layer also accommodates the modules
implementing the serendipity algorithms.
All the presented services and solutions must be made available to end-users
via pluggable user interface software components (i.e. Web portlets); the main
end-user interfaces need to deal with an environment for describing items, anno-
tating and visually comparing them, seeking and connecting entities, browsing
the data and making use of the data citation and serendipity modules.
5 Final Remarks
In this paper we discussed the role of LOD in the CH field and we outlined some
of the cultural and computational challenges we have to face in order to define a
framework for supporting knowledge creation in CH. In particular, we selected a
stimulating use case based on History of Art research which provides us with rich
input data and a research community oriented to seek new connections between
different items to create new knowledge.
The main challenges we outlined regard the necessity to define new method-
ologies for entity linking and retrieval which take into account complex user
inputs, a work environment which on the one hand exploits existing LOD for
knowledge creation and on the other hand stimulates the production of new and
4
http://cassandra.apache.org/
5
https://hadoop.apache.org/
6
http://www.europeana.eu/
7
http://www.culturaitalia.it/
8
http://www.danteonline.it/
unexpected connections between CH resources and a data citation methodol-
ogy allowing us to cite evolving subset of data with variable granularity and to
produce both machine- and human-readable references.
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