# FAST CAT: Collaborative Data Entry and Curation for Semantic
Interoperability in Digital Humanities

Pavlos Fafalios<EMAIL_ADDRESS>0000-0003-2788-526X ,  Kostas Petrakis
<EMAIL_ADDRESS>,  Georgios Samaritakis<EMAIL_ADDRESS>,
Korina Doerr<EMAIL_ADDRESS>,  Athina Kritsotaki<EMAIL_ADDRESS>,
Yannis Tzitzikas<EMAIL_ADDRESS>and  Martin Doerr<EMAIL_ADDRESS>Centre for Cultural Informatics and Information Systems Laboratory, FORTH-
ICSN. Plastira 100HeraklionGreeceGR-70013

(2021)

###### Abstract.

Descriptive and empirical sciences, such as History, are the sciences that
collect, observe and describe phenomena in order to explain them and draw
interpretative conclusions about influences, driving forces and impacts under
given circumstances. Spreadsheet software and relational database management
systems are still the dominant tools for quantitative analysis and overall
data management in these these sciences, allowing researchers to directly
analyse the gathered data and perform scholarly interpretation. However, this
current practice has a set of limitations, including the high dependency of
the collected data on the initial research hypothesis, usually useless for
other research, the lack of representation of the details from which the
registered relations are inferred, and the difficulty to revisit the original
data sources for verification, corrections or improvements. To cope with these
problems, in this paper we present FAST CAT, a collaborative system for
assistive data entry and curation in Digital Humanities and similar forms of
empirical research. We describe the related challenges, the overall
methodology we follow for supporting semantic interoperability, and discuss
the use of FAST CAT in the context of a European (ERC) project of Maritime
History, called SeaLiT, which examines economic, social and demographic
impacts of the introduction of steamboats in the Mediterranean area between
the 1850s and the 1920s.

Data Entry, Data Curation, Semantic Interoperability, Archival Research,
Digital Humanities

††copyright: acmcopyright††journalyear: 2021††journal: JOCCH††journalvolume:
-††journalnumber: -††ccs: Information systems Information integration††ccs:
Information systems Data cleaning††ccs: Information systems Mediators and data
integration††ccs: Information systems Extraction, transformation and loading

##  1\. Introduction

A vast area of research in descriptive and empirical sciences concerns the
quantitative analysis of empirical facts, their description and interpretation
of possible causes, influences and evolution trends. Research in this case
typically starts with the definition of the kind and formal structure of the
data that needs to be extracted and/or transcribed from one or more data
sources, for evaluation and further analysis of aggregated facts. This type of
research is noticed in a wide range of disciplines, including History (e.g.,
analysis of historical archival sources (Delis, 2020; Petrakis et al., 2021);
our use case in this paper), Archaeology/Anthropology (e.g., isotope or DNA
analysis of ancient bone remains found in a particular area (Haak et al.,
2008)), or Biodiversity (e.g., analysis of data from mesocosm experiments
(Pitta et al., 2017)).

However, current practice of quantitative analysis in these sciences nearly
exclusively uses spreadsheets or simple relational databases, in order to
organise the data as rows with multiple columns of related parameters. This
form offers itself for direct quantitative analysis under varying parameters,
which may consequently be used for the scholarly interpretation of causes and
impacts. However, it also has some typical problems including: i) the high
dependency of the digitised/transcribed data on the initial research
hypothesis, usually useless for other research, ii) the difficulty of
representing nested tabular structures, iii) the lack of representation of the
details from which the registered relations are inferred, iv) the difficulty
to revisit the original sources of transcribed facts for verification,
corrections or improvements, and v) the difficulty to combine and integrate
data from multiple and diverse data sources, and thus perform (semi-)automated
quantitative analysis of facts coming from different information sources
(Doerr and Iorizzo, 2008).

In this paper, we focus on research workflows that require some kind of
quantitative analysis of large amounts of data, such as transcripts of
archival sources, and study the aforementioned problems in the context of the
SeaLiT project, a European (ERC) project of Maritime History which examines
economic, social and demographic issues of Mediterranean History between the
1850s and the 1920s emerging from the introduction of steamboats. Our
objective is to support historians of this project in digitising and curating
their unique information sources collaboratively, for enabling long-term
preservation, semantic interoperability, and the use of the data as a primary
source for current and future historical research.

To this end, we first detail the data management requirements, the respective
challenges, as well as the overall methodology we follow for supporting
semantic interoperability. We then describe FAST CAT, a collaborative system
that we have developed for data entry and consolidation. We present its user
interface, the supported functionalities, its configurability, as well as its
use by a large number of historians within the SeaLiT project.

The rest of this paper is organised as follows: Section 2 discusses the
context of this work and the related requirements and challenges. Section 3
describes the overall methodology we follow for data entry, data consolidation
and data exploration, and discusses related works. Section 4 describes the
FAST CAT tool, its configurability, its data entry interface, as well as the
FAST CAT TEAM environment for data curation. Section 5 describes the use of
FAST CAT in the context of the SeaLiT project. Finally, Section 6 concludes
the paper and discusses future work.

##  2\. Context, Requirements and Challenges

###  2.1. Context: The SeaLiT Project

SeaLiT111SeaLiT - Seafaring Lives in Transition. Mediterranean Maritime Labour
and Shipping during Globalisation, 1850s-1920s. ERC Starting Grant - ID:
714437. http://www.sealitproject.eu/ is an ongoing research project of
Maritime History that explores the transition from sail to steam navigation
and its effects on seafaring populations in the Mediterranean and the Black
Sea between the 1850s and the 1920s. Historians and researchers in this
project investigate, besides others, the maritime labour market, the evolving
relations among ship-owners, captain, crew and local societies, and the
development of new business strategies, trade routes and navigation patterns,
during the transitional period from sail to steam.

The information management challenge that SeaLiT faces is the ability to
faithfully catalogue historical data sources for use as a primary source for
research, while integrating the data into a common form from which historical
analysis and questions can be carried out efficiently. The information sources
used in SeaLiT range from handwritten ship logbooks, crew lists, payrolls and
student registers, to civil registers, business records, account books and
consulate reports, gathered from different authorities (i.e., not following a
common format) and written in different languages (Spanish, Italian, French,
Russian, Greek).

###  2.2. Data Management Requirements and Challenges

The data management requirements can be grouped into three categories:

  * •

Data transcription

  * •

Data consolidation

  * •

Data analysis and exploration

Data transcription (or digitisation) refers to the ability of the researchers
to faithfully catalogue their information sources, aiming at long-term
preservation and exploitation of the data beyond the objectives of a
particular research activity. Here the main challenges include: i) how to
transcribe/catalogue as much relevant information as possible and as exact as
possible, ii) how to support fast data entry, and iii) how to handle
uncertainty in the original data. With respect to the latter, a common problem
in SeaLiT is the difficulty in recognising one or more characters in some text
of the original source. In some cases the historian can make a guess, while in
other cases this is impossible.

Data consolidation refers to the integration of data coming from different
sources into a common form from which analysis and questions can be carried
out efficiently. It also involves the correction or normalisation of values
and the disambiguation of entity identities, like the indication that two
different person instances (written in different ways or with spelling errors)
are actually the same person. Typically known as instance matching, we stress
in this context the managing of varying identity assumptions. Here the main
challenge is how to integrate and curate the digitised information of the
different data sources and in parallel keep track of where the information
comes from and what its original form is. A particular technical challenge is
how to handle identities and support a multi-level instance matching process
(within the same document, across related documents, across independent
documents), each time with different default assumptions and background
knowledge that combines automation and manual intervention (where ‘identity’
is understood as what the author meant writing the expression at hand).
Another challenge is how to handle vocabularies or thesauri of terms, i.e.,
universals in contrast to instances, especially in case of multilingual data.
The key success factor is the transformation of the data to a single, curated,
connected and provenance-aware resource that can be queried and analysed
efficiently.

Data analysis and exploration refers to the exploitation of the consolidated
data, e.g. for quantitative analysis of collective phenomena for drawing
conclusions on possible impact factors. Researchers need to be able to find
answers to complex information needs that require combining complementary
information from different sources. The challenge here is how to provide users
with user-friendly interfaces that support them in finding the needed
information easily and in different forms, like tables or charts, for direct
use in their research. Thus, the key success factors are usability as well as
trustworthiness which can be achieved by providing access to the original
sources (e.g., for validation).

Apart from the aforementioned technical challenges, there are some common
organisational-related challenges. At first, in large research projects there
are usually research teams that are spread across the world and which need a
common and secure place for storing and accessing their data, sharing it
internally, and releasing parts of it to a wider audience when they want to do
so. This requires a plan and tools for collaborative work. Moreover, there
might be inconsistent access to Internet during particular research activity,
like field studies or working in remote, local archives. This might require
offline data entry and synchronisation when Internet is available. Finally, in
ongoing research projects where new data sources might become available at any
time, there is the need of a clear strategy on how to handle updates. New data
sources means new structures for data entry and storage, new entity instances
that require instance matching, new vocabulary terms, etc.

In this paper, our focus is on data transcription and data consolidation,
while we also try to cope with the organisational-related challenges.

##  3\. Overall Approach and Related Work

The focus of the overall approach we follow for coping with the challenges
described in the previous section is semantic interoperability (Ouksel and
Sheth, 1999). This notion is defined as the ability of computer systems to
exchange data with unambiguous, shared meaning. This can be achieved by adding
metadata about the data and linking each data element to an ontology or
controlled/shared vocabulary. Semantic interoperability is a requirement to
enable, amongst others, knowledge discovery, inference and data federation
between information systems, in particular when data sources from different
providers need to be combined.

To this end, the semantic artefacts that we consider for allowing both humans
and machines to locate, access and understand the data and metadata are the
following:

  * •

A core domain ontology, in particular the ISO standard CIDOC-CRM (more below),
used for representing all entities and entity associations that appear in the
data.

  * •

A set of controlled vocabularies (thesauri of terms), represented through the
SKOS W3C recommendation222https://www.w3.org/TR/skos-reference/; the
vocabularies are used for controlling the data entry process and ensuring
uniformity and consistency in the storage and retrieval of the different
notions that appear in the data.

  * •

A configurable URI generation policy applied during the data transformation
process for assigning unique (Web) identifiers to the entities that appear in
the data.

Fig. 1 depicts the overall data management methodology we follow for
supporting semantic interoperability in Digital Humanities and similar forms
of empirical research. There are four main steps in this methodology, each one
supported by a software system: i) data entry (supported by FAST CAT), ii)
data curation (supported by FAST CAT TEAM), iii) data transformation
(supported by X3ML and 3M Editor (Marketakis et al., 2017)), and iv) data
analysis and exploration (supported by tools like AQUB (Kritsotakis et al.,
2018) or ResearchSpace (Oldman and Tanase, 2018)).

Below, we first detail each of these steps (Sections 3.1-3.4) and then discuss
related works (Section 3.5).

Figure 1. The overall data management approach

###  3.1. Data Entry

The first step is data entry which is supported by the FAST CAT system
(detailed in the next section). This can be done by a user either online, or
offline with possibility of automated synchronisation when the user gets
online.

In FAST CAT, data from different information sources can be transcribed as
‘records’ belonging to specific ‘templates’, where a ‘template’ represents the
structure of a single data source. A ‘record’ in FAST CAT organises the data
and metadata in tabular form (tables), similar to spreadsheets, offering
functionalities like nesting tables and selection of term from a controlled
vocabulary (more below). Here researchers can agree on the patterns to use
when there is some kind of uncertainty in the original data. For instance,
historians in SeaLiT put a question mark when a specific character in the
original archival source is not recognisable, or put the text inside square
brackets when they are not sure about a set of characters.

The advantage of using tables for data entry is twofold. First, historians are
familiar with this form due to the wide use of spreadsheet programs like
Microsoft Excel. Second, the tables facilitate fast data entry and error
detection because the process is iterative and the user can easily inspect the
entries of the previously-filled rows.

###  3.2. Data Curation

The second step is data curation supported through FAST CAT TEAM, an
environment offered by FAST CAT that allows the collaborative curation of the
transcribed data through the management of entities and vocabularies (thesauri
of terms). The reason of decoupling data curation from data entry, and
avoiding curating the data directly in the records, is the need to maintain
the transcribed data as close to the original information sources as possible.
This is of crucial importance for researchers since the data consolidation may
be ambiguous and require further research and repeated revision at any time in
the future. It further enables them to consolidate the data for exploitation
in other services (like the data exploration services that we will see below)
without having to change the information as it appears in the archival sources
(and thus ‘spoil’ the data). This also means that, other researchers working
in some other project can work with the original transcribed data and apply
their own consolidation processes.

With respect to the management of entities, researchers can inspect the main
entity instances that appear in the data (names of persons, locations, ships,
and legal entities, in the case of SeaLiT) and start curating them. Here a
first automated curation step considers a set of rules for giving the same
identity to a set of entity instances having some common
characteristics/properties. For example, all person instances in the same
source that have the same firstname, lastname, father’s name and birth date
must be considered as the same person and thus have the same identity. Then,
the available curation actions include: i) corrections of entity names or
other entity properties (e.g., changing “Vodowice” to “Wodowice”, or “G??rge”
to “George”), ii) indication that two or more entity instances refer to the
same real-world entity, thus they must have the same ‘identity’ (manual
instance matching process), and iii) indication that a specific instance from
a set of automatically matched instances is a different entity and thus must
have a different ‘identity’. Note here that, when making corrections on the
entity names or properties, the transcribed data in the records does not
change. All entity instances are linked to their occurrences in the
transcripts. This means that researchers can ‘go back’ at any time, visit the
original transcribed data, and inspect how exactly a particular entity appears
in the original data sources. As we described previously, this is very
important for validation purposes and for keeping track of provenance
information.

With respect to the management of vocabularies, researchers can maintain lists
of terms in different languages, thus allowing for cross-lingual data
exploration. Specifically, for each term in a vocabulary, users can provide a
preferred term in English as well as its broader term (if any). The storage of
broader terms provides an hierarchy for the terms, which can be very useful
when exploring the data. For example, one can retrieve all data related to a
general term through its narrow terms.

The curated data derived by this step, together with the original transcribed
data, can be directly exploited by other applications before their
transformation. For example, the work in (Petrakis et al., 2021) shows how the
data of ship logbooks of the nineteenth and twentieth centuries, digitised and
curated using FAST CAT, are visualised on a web-accessible map application
called Ship Voyages.333http://www.sealitproject.eu/digital-seafaring

###  3.3. Data Transformation

The third step in our data management pipeline is data transformation. Here
the objective is to model and semantically represent the (curated) data and
metadata using established data models, for supporting data exchange,
interoperability and long-term validity, and make the data exploitable beyond
a particular research problem or project.

To achieve this, we first need to decide on the domain ontology to use for
representing and integrating the data of all FAST CAT records. Here we choose
to create a model that is compatible with the CIDOC Conceptual Reference Model
(CRM)444http://www.cidoc-crm.org/ (Doerr, 2003). CIDOC-CRM is a high-level,
event-centric ontology (ISO
standard555https://www.iso.org/standard/57832.html) of human activity, things
and events happening in spacetime, providing definitions and a formal
structure for describing the implicit and explicit concepts and relationships
used in cultural heritage documentation and beyond.

Given the domain ontology, we then have to create the schema mappings that map
the elements of a template (e.g., a table column) to classes and properties of
the domain ontology. This is a time-consuming process that need to be done for
each different template and which may require many revisions as long as the
data engineer better understands the data or changes are made to the FAST CAT
templates. This process is supported by the X3ML mapping framework and its
user interface 3M Editor (Marketakis et al., 2017). The framework allows data
engineers to relate equivalent concepts or relationships from the source
schemata (FAST CAT templates, in our case) to a target schema (the domain
ontology), as well as to define the URI generation policies that assign
identifiers to the entities that appear in the data.

After having defined the schema mappings for each different FAST CAT template
as well as each category of entities in FAST CAT TEAM, the data can be
automatically transformed (through X3ML) to a rich semantic network of Linked
Data (RDF triples) (Heath and Bizer, 2011) and be ingested in a semantic
repository (RDF database/triplestore), from where data analysis and
exploration can be initiated. Whereas data columns in the transcripts
basically appear associated in the order next to each other as in the original
sources, possibly in nested tables, the transformed data form a connected
semantic network that completely connects fields in the data columns with
meaningful relationships (RDF properties) and other implicitly inferred
entities.

In Section 5 we provide an example of how we model maritime history data in
the context of SeaLiT using a CIDOC-CRM compatible domain ontology, as well as
an example of a schema mapping and the corresponding transformed data. Since
the definition of schema mappings and the data transformation process is out
of the score of this paper, we suggest the reader to refer to (Marketakis et
al., 2017) for further information.

###  3.4. Data Analysis and Exploration

The user-friendly exploration of a semantic repository (containing the
semantic network derived from the data transformation process) can be
performed through two main general access methods: i) keyword search, where
the user submits a free text query and gets back a ranked list of results that
are relevant to the query terms (e.g., (Kadilierakis et al., 2020a, b; Nikas
et al., 2020)), and ii) interactive access, where the user explores the data
through intuitive interactions with a data access system, e.g., using a
faceted search interface (Tzitzikas et al., 2017) or assistive query building
(e.g., (Kritsotakis et al., 2018)).

In SeaLiT we make use of ResearchSpace (Oldman and Tanase, 2018), an open
source platform build on top of the metaphactory platform (Haase et al.,
2019). The platform offers a variety of functionalities, including an
assistive query building interface that allows users to gradually build
complex queries and analyse the results quantitatively through different
visualisations. Such queries actually follow paths in the semantic network,
defined through one or more relationships between the main entities that
appear in the data.

Fig. 2 shows a screen dump of the system, where the user inspects a bar chart
showing the embarkation location of persons that were crew members at ships of
type brigantine. In the upper part of the figure we see the query built by the
user (Person - was crew at - Ship, where Ship - has ship type - Brigantino),
while in the lower part the user has selected to view a bar chart of the
results using the visualisation context (aggregation function) ‘embarked at’.

Figure 2. Data analysis and exploration in the ResearchSpace platform.

###  3.5. Related Work

Since the focus of our work is data entry and consolidation for semantic
interoperability and the production of a semantic network of Linked Data
(Heath and Bizer, 2011), below we discuss works that cover this analysis
pipeline.

At first, the original data of interest may be stored and managed in a wide
range of formats, e.g., spreadsheets, relational databases, and/or textual
documents (unstructured or semi-structured). In descriptive and empirical
sciences, like History, spreadsheet software (like Microsoft Excel or Google
Sheets) and relational database management systems (RDBMS), like Microsoft
Access or MySQL, are still the dominant tools for data entry, storage and
management.

With respect to RDBMS-based systems, HEURIST666http://heuristnetwork.org/ is
an open source platform that allows Humanities researchers to design, manage,
analyse and publish their own MySQL databases through a user-friendly web
interface. HEURIST offers a wide range of functionalities for the management
of research data, including the ability to import existing datasets, upload
media files, create web pages, as well as manage sophisticated searches. In
the same context, nodegoat777https://nodegoat.net/ is a data management tool
that allows scholars to build datasets based on their own data model, offering
relational modes of data analysis. The system follows an object-oriented
approach treating people, events, artefacts and sources as objects that are
connected through relations. Each object can be supplemented with geographical
and temporal attributes, thus allowing geographic and diachronic
visualisations. Nodegoat is also capable of processing complex queries by
means of filtering and analysis over the networks formed by the relations
between the objects. Our data management methodology and the FAST CAT system
described in this paper follow a much different approach compared to the above
systems. Our approach is oriented towards semantic interoperability, so that
both humans and machines can locate, access and understand the data and
metadata.

Data curation includes all the processes needed for controlled data
maintenance and management, together with the capacity to add value to data
(Miller, 2014). From a theoretical point of view, the white paper in (Muñoz
and Renear, 2011) provides background and provokes discussions about the
skills, professional roles, training, and institutional support needed for
data curation in humanities research. (Palmer et al., 2013) describes an
approach to data curation education that integrates traditional notions of
curation with principles and expertise from library, archival, and computer
science. (Henry, 2014) describes the life cycle of data curation for the
Humanities, including discussions about staffing, infrastructure, roles,
responsibilities, documentation as well as interesting cases studies.

From a technical perspective, the Data Tamer (Stonebraker et al., 2013)
system, and its commercial realisation Tamr888https://www.tamr.com/, is an
example of an end-to-end data curation system, offering components for schema
integration, entity consolidation, data visualisation as well as crowd-
sourcing. An important sub-process in data curation is data cleaning, which
deals with detecting and removing errors and inconsistencies from data in
order to improve their quality (Rahm and Do, 2000). (Chu et al., 2016)
presents a taxonomy of the data cleaning literature, describes the state-of-
the-art techniques, and also highlights their limitations.
OpenRefine999https://openrefine.org/ (Verborgh and De Wilde, 2013) is a
popular desktop application for data cleaning, also supporting the
transformation of the data to other formats. It operates on rows of data which
have cells under columns, which is very similar to relational database tables.
(Krishnan et al., 2016) presents the results of an interesting user survey of
29 data analysts and infrastructure engineers about data cleaning processes.
The results highlight three important themes: i) the non-linear and iterative
nature of data cleaning, ii) the lack of rigour in evaluating the correctness
of data cleaning, and iii) the disconnect between the analysts who query the
data and the infrastructure engineers who design the cleaning pipelines.
Contrary to existing works on data curation that mainly focus on correcting
errors and inconsistencies in the data, in FAST CAT we follow a provenance-
aware approach that also incorporates instance matching processes and the
management of vocabularies of terms.

With respect to the final step of transforming the data sources to Linked
Data, there is a plethora of solutions to execute mappings from different file
structures and serialisations to RDF. R2RML101010https://www.w3.org/TR/r2rml/
is a W3C standard for mapping relational databases into RDF, while (Dimou et
al., 2014) describes the RML mapping language, an extension of R2RML for
mapping heterogeneous sources into RDF. (Michel et al., 2014) surveys existing
approaches on mapping relational databases to RDF, analyse their commonalities
and differences, and underline common patterns in the orchestration of
solutions applicable to each step of the translation process. X3ML (Marketakis
et al., 2017) is an XML-based mapping language and framework for the
management of the core processes needed to create, maintain and manage mapping
relationships between different data sources. It allows the description of
both schema mappings and URI generation policies, and offers tools (like the
3M Editor) for managing, editing, visualising and executing those mappings.
The same work also provides a detailed overview and categorisation of works on
mapping heterogeneous data sources to RDF. In the same context, MatWare
(Tzitzikas et al., 2014) is a framework that automates the construction and
update of a semantic warehouse (RDF database), by implementing a pipeline that
integrates data from multiple different sources, also offering services for
instance matching and quality assessment. In our work, we make use of the X3ML
framework for mapping the curated data to the domain (target) ontology.

As regards the domain of historical research (our use case), the survey in
(Meroño-Peñuela et al., 2015) studies the joint work of historians and
computer scientists in the use of Semantic Web methods and technologies as
well as how such works help in solving open problems in historical research.
The survey provides an extensive analysis on works and systems for i)
knowledge modelling (historical ontologies), ii) text processing and mining,
iii) search and retrieval, and iv) data integration for semantic
interoperability, discussing also aspects of the Semantic Web that could be
furtherly exploited in historical research. Two such aspects are “non-
destructive data transformations” and “linking more historical data”. The high
provenance-awareness of our approach (which decouples data curation from data
entry; as described in Section 3.2), together with the use of standard models
for representing and publishing the data in the FAST CAT records (CIDOC-CRM,
SKOS), are characteristics of our approach that help towards this direction.

In the same context of historical research, (Koho et al., 2019) presents the
WarSampo knowledge graph, a shared semantic infrastructure for publishing data
about WW2 and Finnish military history as Linked Data. The shared semantic
infrastructure is based on the idea of representing war as a spatio-temporal
sequence of events that soldiers, military units, and other actors participate
in. The considered metadata schema is an extension of CIDOC-CRM, supplemented
by various military history domain ontologies. In addition, to support
sustainability, a repeatable automatic data transformation and linking
pipeline has been created that rebuilds the whole semantic network from the
individual source datasets. Note here that, contrary to our work which focuses
on data entry and curation (performed by the researchers), WarSampo is a pure
data transformation infrastructure for historical datasets. Other semantic
databases of Finnish history are accessible through the Linked Data Finland
research initiative.111111https://www.ldf.fi/datasets.html

##  4\. The FAST CAT Tool

Below we detail the functionality and the user interface offered by FAST CAT.
We first describe how we can configure templates of records, each one
representing a different data source (Section 4.1). Then, we show how one can
create records and start filling the data entry forms (Section 4.2), and
describe the data curation functionality offered by FAST CAT TEAM (Section
4.3). Finally, provide some technical implementation details (Section 4.4).

###  4.1. Configuration of Templates

For initiating the data entry process, we first need to construct the
templates, i.e., the data entry forms used for transcribing the data of a
particular source. A template actually represents the structure of a data
source and allows the documentation of metadata information about both the
source and the data entry process (like who makes the transcription). Given a
template, the user can then create a record and start transcribing the source
data.

Fig. 3 depicts a class diagram of the main notions involved in the
configuration of a template. At first, a template consists of one or more
tables and a table of one or more columns. A column is an abstract class with
three different specialisations: i) colspan column, ii) plain column, iii)
nested-table column. A colspan column is a table column which does not accept
values but just organises a set of other columns. For example, PERSON is a
colspan column which groups together the columns: FIRST NAME, LAST NAME,
FATHER’S NAME, BIRTH DATE. A plain column is a table column which accepts
values, i.e., the user can provide a value while filling a table row, like
writing some text, or selecting a term from a controlled vocabulary. A nested-
table column is a table column which spans to another table, so the user can
link the value of this column to a whole table of multiple rows. For instance,
a table whose each row describes information for a particular person can
contain a nested-table column with name CHILDREN which allows filling
information (like FIRSTNAME, LASTNAME and BIRTH DATE) for all person’s
children.

Figure 3. Class diagram describing the configuration model in FAST CAT.

A nested-table column can be implemented in different ways, depending on the
functionality and flexibility we want to offer to the end-users. FAST CAT
currently supports two implementations of a nested-table column: i) plain-
columns table, and ii) full-nested table. A plain-columns table is a simple
table consisting of one or more plain columns (so the user can provide
multiple values organised in plain columns), while a full-nested table is a
table consisting of columns of any type. The former type is applicable when
the user needs to provide multiple values to one or more columns within a
table row (e.g., multiple ship owners). The latter type is applicable for
cases where we want to add additional information about a table record (i.e.,
an entire table row) and this information requires multiple columns (e.g.,
information about all students enrolled in a course). Examples of the
different column types are shown in Figure 6.

A plain column accepts values of different types, in particular: i) Entity
(the value is the name or property of an entity, e.g., of a person), ii)
Vocabulary Term (the value is a term from a controlled vocabulary), iii)
Literal (the value is a literal, e.g., a free text, a number, or a date). Each
of these value types can contain additional information, like the type of
literal in the case of ‘Literal’ (e.g., String, Integer, Date, etc.), or the
type of entity in the case of ‘Entity’ (e.g., Person, Location, Ship, etc.).

###  4.2. Data Entry

Data entry is performed through FAST CAT’s web interface. Its home page shows
a table containing all the available templates together with some basic
information for each template (Fig. 4). For initiating the data entry process,
the user first needs to create a new record for one of the available
templates. After filling some basic record information, the record is created
and the user can start filling the data entry forms. Whenever a user saves a
record, FAST CAT saves it locally and also (if online) remotely. In most
cases, no conflicts arise since one particular record is usually managed by a
single person. In case of a conflict, we handle it by checking the “Last
modified” field and declaring the newer version as the final one, or allowing
the user to set the correct version.

Figure 4. Available templates and creation of a new template record in FAST
CAT.

Figure 5 shows an example of a record from the SeaLiT project (transcription
of an archival Crew List), while examples of the different column types (as
described in Section 4.1) are shown in Fig. 6. In a plain column (Fig. 6-a),
the user can either write some text, select a term from a controlled
(predefined or dynamic) vocabulary of terms, or fill a date. In case of a
nested-table column of type plain-columns table (Fig. 6-b), the corresponding
columns are displayed in blue colour and data entry is performed through a
‘pop-up’ table below the columns. On the contrary, a full-nested table is
displayed in a new area below the first table (Fig. 6-c). Users are also able
to export the record data to Excel or XML. This allows them to perform
external analysis of (part of) the data, e.g., for creating charts in Excel.

Figure 5. The data entry forms in a FAST CAT record. Figure 6. Examples of the
different column types in FAST CAT.

###  4.3. Data Curation using FAST CAT TEAM

The collaborative curation of the transcribed data, in particular the
vocabularies and the entities, is performed through a dedicated FAST CAT
environment called FAST CAT TEAM. The home page of FAST CAT TEAM shows a table
of all publicly shared records (Fig. 7). For each record, the table shows its
ID and title, the corresponding template title, as well as some metadata
information like the author of the record, its last modification date and its
status. The status information can take one of the following values: ‘Under
processing’ (data entry is still in process), ‘Ready for review’ (data entry
has been finalised but review and/or curation is still in process), ‘Reviewed
and Ready for publishing’ (data entry and curation has been finalised and the
data is ready for publication), ‘Published’ (the data has been transformed and
ingested in the data analysis and exploration service). This workflow is not
linear, i.e., the user can transition from one state to any other state. For
example, the user can move from the ‘published’ state to the ‘under
processing’ state because, for instance, new data needs to be added in the
corresponding record. Similarly, one can move from ‘published’ to ‘Ready for
review’ because additional curation processes need to be performed.

Figure 7. The home page of FAST CAT TEAM.

Through the ‘Vocabularies’ menu item, the user can see a table with all
vocabularies used in the FAST CAT records and start their curation (Fig. 8).
The table shows the name of the vocabulary, the source language(s), the
template(s) in which it is used, and the involved organisations (that take
care of the curation). For each term in a vocabulary, the user can set a
preferred term in English and its broader term.

Figure 8. Management of vocabularies in FAST CAT TEAM.

For curating the entity instances, FAST CAT TEAM shows (in a left menu) a list
with all the (predefined) entity types whose instances exist in the records
(Fig. 9). In SeaLiT, for example, there are four entity types: ‘Legal
Entities’, ‘Locations’, ‘Persons’, and ‘Ships’. When selecting to start
curating one of the entity types, the user is shown a table of all its
instances. The information shown in the table is different for each type of
entities. For persons, for example, the table shows the following information:
name, surname(s), maiden name, father’s name, place and date of birth, date of
death, registration number, status/capacity/role. The user can select an
entity and change/set information, or select two or more entities and indicate
that they correspond to the same real-world entity (manual instance matching).
In the latter case, the user must also indicate the preferred value for the
entity properties (if there is a conflict). Note here that, any change in the
values do not alter the original values as stored in the FAST CAT records,
while the user can also directly inspect and visit all FAST CAT records in
which the entity instance appears. The user is also able to filter the
displayed entity instances based on a template type or record, as well as
export them to Excel for further offline analysis.

Figure 9. Management of entities in FAST CAT TEAM.

The curated information in the vocabularies and the entity instances is
exploited for offering better quantitative analysis and exploration services.
Specifically, this data together with all data in the records are
automatically transformed to RDF using the X3ML framework (cf. Section 3.3).
In X3ML, a schema mapping file has to be created for i) each different
template, ii) each type of entities, and iii) the vocabularies. As we
described in the previous section, a schema mapping indicates how the data
must be mapped to elements of the target (domain) ontology. Finally, the
transformed (and integrated) data are ingested in an RDF triplestore, which is
the database of the ResearchSpace data exploration platform (cf. Section 3.4).

###  4.4. Technical Details

FAST CAT is a web-based software system that runs on a web browser, either
online or offline as a standalone application. It has an embedded web server,
but may as well run using a static HTML page. The system has been developed
using Bootstrap, JavaScript, and jQuery. For managing the data entry forms, we
make use of the JavaScript libary Handsontable121212https://handsontable.com/.
All the data are saved in JSON format and stored locally on the web browser
using multiple PouchDB131313https://pouchdb.com/ databases. The local data are
synchronised from time to time with the online ‘central’ data stored in
multiple databases on an Apache CouchDB141414https://couchdb.apache.org/
database management system.

In more detail, PouchDB is an open-source JavaScript database, inspired by
Apache CouchDB, that is designed to run well within the browser. Its main
advantage is easy replication from and to CouchDB and compatible servers.
Every time user starts FastCat, the templates are replicated from the server
if there is an internet connection. Otherwise, templates are retrieved from
user’s local PouchDB database. Whenever user saves a record, it is saved
locally in user’s local PouchDB database. If user is online when saving,
record is also replicated to the server for safekeeping. In addition, if user
has also decided to share a record publicly, then on save, information about
terms and instances used inside the record are also replicated to the server
to their corresponding databases.

##  5\. Use Case: Using FAST CAT in SeaLiT

FAST CAT is currently used in the SeaLiT project by around 30 users in 5
countries. The users are historians, like postdoctoral researchers,
professors, Ph.D. students, and research assistants, of the following 5
organisations: i) Institute for Mediterranean Studies, Foundation for Research
and Technology - Hellas (Rethymno, Greece), ii) T.I.G., University of
Barcelona (Barcelona, Spain), iii) NAVLAB, University of Genoa (Genoa, Italy),
iv) UMR TELEMME, University Aix-Marseille (Aix-en-Provence, France), v)
Department of History, University of Zadar (Zadar, Croatia).

The number of configured templates is currently 20, representing 20 different
types of data sources, and the total number of records is 605. Table 1 gives
the names of all templates and the corresponding number of records for each
template (as of April 2021). The transcribed data are written in 5 languages:
Greek, French, Spanish (Castilian), Italian, and Russian. The template with
the larger number of records is Crew List (Ruoli di Equipaggio) (98 records).
This template contains the following 6 tables:

  * •

FastCat Record Information: Table existing in all templates, containing
metadata information about the record, in particular: id, creation date, last
modification date, author name, author role.

  * •

Source Identity: Table existing in all templates, containing information about
the data source, in particular: name and location of archive/library, register
number, document number, date of release, name and location of issuing
authority, source type, fond number and title, series number, file number and
title.

  * •

Ship Identity: Table containing information about the ship, in particular:
ship name, ship type, tonnage, construction location, construction year,
ammunition information (type, value, unit), registry port, registry number,
owner.

  * •

Crew List: Table containing information about the crew members, in particular:
embarkation port and date, discharge port and date, person (lastname,
firstname, citizedship, location of residence, date of birth, serial number),
profession/rank, wage (form of payment, value), total duration (months, days),
pension fund (in Italian Lira).

  * •

Documented Navigation: Table containing information about the ship navigation,
in particular: start and end date of navigation, total navigation duration
(months, days), first planned destinations, total crew number (captain
included).

  * •

Route: Table containing information about the navigation route, in particular:
departure port, departure date, note about the departure, arrival port,
arrival date, note about the arrival.

An example of a record belonging to this template is available at
https://tinyurl.com/yxfbu5e5.

Table 1. List of FAST CAT templates used in SeaLiT and corresponding number of records (as of April 2021). No |  Template Name |  # Records
---|---|---
1 |  Accounts book |  14
2 |  Census La Ciotat |  63
3 |  Civil Register |  29
4 |  Crew and displacement list (Roll) |  35
5 |  Crew List (Ruoli di Equipaggio) |  98
6 |  Employment records, Shipyards of Messageries Maritimes, La Ciotat |  50
7 |  First national all-Russian census of the Russian Empire |  6
8 |  General Spanish Crew List |  64
9 |  Maritime Register of the State for La Ciotat |  1
10 |  List of ships |  71
11 |  Logbook |  17
12 |  Naval Ship Register List |  2
13 |  Notarial Deeds |  10
14 |  Payroll |  7
15 |  Payroll of Russian Steam Navigation and Trading Company |  14
16 |  Register of Maritime personel |  4
17 |  Register of Maritime workers (Matricole della gente di mare) |  6
18 |  Sailors register (Libro de registro de marineros) |  52
19 |  Seagoing Personel |  52
20 |  Students Register |  10

In FAST CAT TEAM, the total number of vocabularies is 52. Examples of
vocabularies include: ship type, flag, marital status, religion, military
service status, nationality, profession, reason of death, wind direction, wind
strength. With respect to the entity instances, there are currently 76,643
person instances, 8,814 location instances, 2,354 ship instances and 1,197
legal entity instances. Note here that the manual instance matching process
made by the historians (cf. Section 4.3) is still undergoing, which means that
the number of distinct entities (i.e., distinct entity identities) in each of
the four entity categories is expected to be much lower at the end of the
curation process (if not additional source are digitised).

For transforming the curated data of the FAST CAT records into a semantic
network of linked data, we first created a data model compatible with CIDOC-
CRM, which we call ‘SeaLiT Ontology’. Figures 10 and 11 illustrate a (small)
part of the model, showing how information about a ship and a ship voyage is
modelled. Each new class introduced in this model is subclass of a CIDOC-CRM
class and, likewise, each property is sub-property of a CIDOC-CRM property.
The data model is under constant evaluation as long as new FAST CAT templates
are created and analysed.

Figure 10. Modelling information about a ship. Figure 11. Modelling
information about a ship voyage.

Based on this data model, we create the schema mappings using X3ML and 3M
Editor (as described in Section 3.3). In particular, we have to create: a) one
mapping definition file for each distinct template in FAST CAT (for
transforming the record data), b) one mapping definition file for each
category of entities in FAST CAT TEAM (for transforming the curated entity
data), and c) one mapping definition file for the vocabularies in FAST CAT
TEAM (for transforming the curated vocabulary terms). This sums to a total of
(currently) 25 mapping definition files (20 for the templates, 4 for the four
categories of entities, and 1 for the vocabularies). Also, a URI generation
policy file has been created which is required by X3ML for assigning URIs to
the instances during the data transformation process. Fig. 12 shows an example
of a mapping definition file in 3M Editor which corresponds to the FAST CAT
template Crew List (Ruoli di Equipaggio). We see, for example, that the field
construction location of the source schema (mapping #1.5 in the figure),
corresponding to a particular table column in the template, is mapped to the
following path in the data model (cf. Fig. 10): Ship—was constructed by—Ship
Construction—P7 took place at—E53 Place. Considering this mapping definition
and also that a FAST CAT record contains, for instance, the information that
the ships Andrea and Antonio have both construction location Genova, the
derived semantic network and the corresponding RDF triples are those displayed
in Fig. 13.

Figure 12. A part of a mapping definition in 3M Editor for the template Crew
List (Ruoli di Equipaggio). Figure 13. An example of (a part of) a semantic
network and the corresponding RDF triples.

##  6\. Conclusion

We have focused on the common data management challenges faced by many
researchers in descriptive and empirical sciences like History. Our particular
focus is on research that requires some type of quantitative analysis in which
the data can be modelled in tabular form and comes from multiple
sources/providers, which means that data consolidation is required in order to
enable effective analysis and exploration services that combine information of
different sources. To this end, we described a general data management
methodology that focuses on semantic interoperability, long-term validity, and
making the data (re)usable beyond the context of a particular research problem
or project. This motivated the development of FAST CAT, a collaborative system
for data entry and curation in Digital Humanities and beyond. We have
presented the functionality and interface of FAST CAT that supports data
transcription (with emphasis on data entry speed) and data consolidation
(correction, normalisation, multi-level instance matching, ontology-based data
transformation). FAST CAT is innovative in its ability to support features
like nested tabular structures for data entry, embedded instance matching and
vocabulary maintenance processes, and provenance-aware data curation.

The FAST CAT system as well as the data management approach described in this
paper are currently in use by historians working in the context of SeaLiT, a
European research project (ERC) of maritime history, for the transcription and
curation of historical archival sources like handwritten crew lists, civil
registers and logbooks. Nevertheless, the overall data management methodology
is configurable which means that it can be used for transcribing, curating,
transforming and then exploring other data sources, beyond the case of
maritime history. The actual steps for applying this methodology on another
domain are the following: i) creation of the FAST CAT templates (i.e., decide
on the structure of the tables, the value type of each table column, the
entity categories and the vocabularies); ii) selection and/or design of the
domain ontology to use for semantically representing the data of the FAST CAT
records; iii) creation of the schema mappings and the URI generation policies
for each template and each category of entities (which will be used together
with the domain ontology for producing the semantic network / RDF data).

We are currently working on how to make easier (and more standardised) the
configuration of FAST CAT, in particular on how to enable the straightforward
creation or change of a FAST CAT template. Our long-term objective is to
enable plain users (like historians) to create their own templates, or modify
existing ones, through a user-friendly environment within FAST CAT. Another
interesting direction for future work is the integration of the domain
ontology used for data transformation within the structure of the templates,
e.g., by associating each table column to a concept or relationship in the
ontology. That would facilitate the definition of the mapping in X3ML (which
is a quite time consuming task) and enable better handling of the updates in
the schema when there are changes in the templates.

## Acknowledgements

This work has received funding from the European Union’s Horizon 2020 research
and innovation programme under i) the Marie Sklodowska-Curie grant agreement
No 890861 (Individual Fellowship, Project “ReKnow - Research Knowledge
Documentation, Analysis and Exploration in Empirical and Descriptive
Sciences”), and ii) the European Research Council (ERC) grant agreement No
714437 (Project “SeaLiT - Seafaring Lives in Transition. Mediterranean
Maritime Labour and Shipping during Globalization, 1850s-1920s”).

## References

  * (1)
  * Chu et al. (2016) Xu Chu, Ihab F Ilyas, Sanjay Krishnan, and Jiannan Wang. 2016\.  Data cleaning: Overview and emerging challenges. In _Proceedings of the 2016 International Conference on Management of Data_. 2201–2206.
  * Delis (2020) Apostolos Delis. 2020\.  Seafaring Lives at the crossroads of Mediterranean maritime history.  _International Journal of Maritime History_ 32, 2 (2020), 464–478.
  * Dimou et al. (2014) Anastasia Dimou, Miel Vander Sande, Pieter Colpaert, Ruben Verborgh, Erik Mannens, and Rik Van de Walle. 2014.  RML: a generic language for integrated RDF mappings of heterogeneous data. In _7th Workshop on Linked Data on the Web_.
  * Doerr (2003) Martin Doerr. 2003\.  The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata.  _AI magazine_ 24, 3 (2003), 75–75.
  * Doerr and Iorizzo (2008) Martin Doerr and Dolores Iorizzo. 2008.  The dream of a global knowledge network—A new approach.  _Journal on Computing and Cultural Heritage (JOCCH)_ 1, 1 (2008), 1–23.
  * Haak et al. (2008) Wolfgang Haak, Guido Brandt, Hylke N de Jong, Christian Meyer, Robert Ganslmeier, Volker Heyd, Chris Hawkesworth, Alistair WG Pike, Harald Meller, and Kurt W Alt. 2008\.  Ancient DNA, Strontium isotopes, and osteological analyses shed light on social and kinship organization of the Later Stone Age.  _Proceedings of the National Academy of Sciences_ 105, 47 (2008), 18226–18231.
  * Haase et al. (2019) Peter Haase, Daniel M Herzig, Artem Kozlov, Andriy Nikolov, and Johannes Trame. 2019.  metaphactory: A platform for knowledge graph management.  _Semantic Web_ 10, 6 (2019), 1109–1125.
  * Heath and Bizer (2011) Tom Heath and Christian Bizer. 2011.  Linked data: Evolving the web into a global data space.  _Synthesis lectures on the semantic web: theory and technology_ 1, 1 (2011), 1–136.
  * Henry (2014) Geneva Henry. 2014\.  Data curation for the humanities.  _Research data management: Practical strategies for information professionals_ (2014), 347–374.
  * Kadilierakis et al. (2020a) Giorgos Kadilierakis, Pavlos Fafalios, Panagiotis Papadakos, and Yannis Tzitzikas. 2020a.  Keyword Search over RDF Using Document-Centric Information Retrieval Systems. In _Extended Semantic Web Conference_. Springer, 121–137.
  * Kadilierakis et al. (2020b) Giorgos Kadilierakis, Christos Nikas, Pavlos Fafalios, Panagiotis Papadakos, and Yannis Tzitzikas. 2020b.  Elas4RDF: Multi-perspective Triple-centered Keyword Search over RDF using Elasticsearch. In _Extended Semantic Web Conference_.
  * Koho et al. (2019) Mikko Koho, Esko Ikkala, Petri Leskinen, Minna Tamper, Jouni Tuominen, and Eero Hyvönen. 2019\.  WarSampo Knowledge Graph: Finland in the Second World War as Linked Open Data.  _Semantic Web–Interoperability, Usability, Applicability_ (2019).
  * Krishnan et al. (2016) Sanjay Krishnan, Daniel Haas, Michael J Franklin, and Eugene Wu. 2016.  Towards reliable interactive data cleaning: A user survey and recommendations. In _Proceedings of the Workshop on Human-In-the-Loop Data Analytics_. 1–5.
  * Kritsotakis et al. (2018) Vangelis Kritsotakis, Yannis Roussakis, Theodore Patkos, and Maria Theodoridou. 2018.  Assistive Query Building for Semantic Data.. In _SEMANTICS Posters &Demos_.
  * Marketakis et al. (2017) Yannis Marketakis, Nikos Minadakis, Haridimos Kondylakis, Konstantina Konsolaki, Georgios Samaritakis, Maria Theodoridou, Giorgos Flouris, and Martin Doerr. 2017\.  X3ML mapping framework for information integration in cultural heritage and beyond.  _International Journal on Digital Libraries_ 18, 4 (2017), 301–319.
  * Meroño-Peñuela et al. (2015) Albert Meroño-Peñuela, Ashkan Ashkpour, Marieke Van Erp, Kees Mandemakers, Leen Breure, Andrea Scharnhorst, Stefan Schlobach, and Frank Van Harmelen. 2015.  Semantic technologies for historical research: A survey.  _Semantic Web_ 6, 6 (2015), 539–564.
  * Michel et al. (2014) Franck Michel, Johan Montagnat, and Catherine Faron Zucker. 2014\.  _A survey of RDB to RDF translation approaches and tools_.  Technical Report hal-00903568v1.
  * Miller (2014) Renée Miller. 2014\.  Big Data Curation. In _Proceedings of the 20th International Conference on Management of Data_ (Hyderabad, India) _(COMAD ’14)_. Computer Society of India, Mumbai, Maharashtra, IND, 4.
  * Muñoz and Renear (2011) Trevor Muñoz and Allen H Renear. 2011.  Issues in humanities data curation.  _White Paper,http://hdl.handle.net/2142/30852_ (2011).
  * Nikas et al. (2020) Christos Nikas, Giorgos Kadilierakis, Pavlos Fafalios, and Yannis Tzitzikas. 2020.  Keyword Search over RDF: Is a Single Perspective Enough?  _Big Data and Cognitive Computing_ 4, 3 (2020), 22\.
  * Oldman and Tanase (2018) Dominic Oldman and Diana Tanase. 2018.  Reshaping the Knowledge Graph by connecting researchers, data and practices in ResearchSpace. In _International Semantic Web Conference_. Springer, 325–340.
  * Ouksel and Sheth (1999) Aris M. Ouksel and Amit Sheth. 1999.  Semantic interoperability in global information systems.  _ACM Sigmod Record_ 28, 1 (1999), 5–12.
  * Palmer et al. (2013) Carole Palmer, Nicholas M Weber, Allen H Renear, and Trevor Muñoz. 2013.  Foundations of data curation: The pedagogy and practice of ”purposeful work” with research data.  (2013).
  * Petrakis et al. (2021) Kostas Petrakis, Georgios Samaritakis, Thomas Kalesios, Enric García Domingo, Apostolos Delis, Yannis Tzitzikas, Martin Doerr, and Pavlos Fafalios. 2021.  Digitizing, Curating and Visualizing Archival Sources of Maritime History: the case of ship logbooks of the nineteenth and twentieth centuries.  _Drassana_ 28 (2021), 60–87.
  * Pitta et al. (2017) Paraskevi Pitta, Maria Kanakidou, Nikolaos Mihalopoulos, Sylvia Christodoulaki, Panagiotis D Dimitriou, Constantin Frangoulis, Antonia Giannakourou, Margarita Kagiorgi, Anna Lagaria, Panagiota Nikolaou, et al. 2017\.  Saharan dust deposition effects on the microbial food web in the Eastern Mediterranean: a study based on a mesocosm experiment.  _Frontiers in Marine Science_ 4 (2017), 117.
  * Rahm and Do (2000) Erhard Rahm and Hong Hai Do. 2000.  Data cleaning: Problems and current approaches.  _IEEE Data Eng. Bull._ 23, 4 (2000), 3–13.
  * Stonebraker et al. (2013) Michael Stonebraker, Daniel Bruckner, Ihab F Ilyas, George Beskales, Mitch Cherniack, Stanley B Zdonik, Alexander Pagan, and Shan Xu. 2013.  Data Curation at Scale: The Data Tamer System.. In _Cidr_ , Vol. 2013.
  * Tzitzikas et al. (2017) Yannis Tzitzikas, Nikos Manolis, and Panagiotis Papadakos. 2017\.  Faceted exploration of RDF/S datasets: a survey.  _Journal of Intelligent Information Systems_ 48, 2 (2017), 329–364.
  * Tzitzikas et al. (2014) Yannis Tzitzikas, Nikos Minadakis, Yannis Marketakis, Pavlos Fafalios, Carlo Allocca, Michalis Mountantonakis, and Ioanna Zidianaki. 2014\.  Matware: Constructing and exploiting domain specific warehouses by aggregating semantic data. In _Extended Semantic Web Conference_. Springer, 721–736.
  * Verborgh and De Wilde (2013) Ruben Verborgh and Max De Wilde. 2013.  _Using OpenRefine_.  Packt Publishing Ltd.