# Towards a semantic approach in GLAM Labs: the case of the Data Foundry at
the National Library of Scotland

Gustavo Candela1,21,2affiliationmark:  11affiliationmark:  National Library of
Scotland 22affiliationmark:  Centro Biblioteca Virtual Miguel de Cervantes,
Universidad de Alicante (Spain)

###### Abstract

GLAM organisations have been exploring the benefits of publishing their
digital collections in a wide variety of forms since the 2000s. Many
institutions, and in particular libraries, have adopted the Semantic Web and
Linked Data principles to their main catalogues. Recent advances in technology
and innovative approaches concerning the reuse of the digital collections by
means of computational access have paved the way for the creation of Labs
within GLAM organisations. In this work, we present a framework to transform
the datasets made available by GLAM organisations under open licenses into
LOD. The framework has been applied to three metadata datasets made available
by the Data Foundry at the National Library of Scotland. The results of this
work are publicly available and can be applied to other domains such as
digital humanities and data science.

###### keywords:

Semantic Web, Linked Open Data, Collections as Data, Digital Libraries, Data
Quality

##  1 Introduction

During the last decade, technological advances have paved the way for a new
context in the GLAM (Galleries, Libraries, Archives and Museums) sector, in
which their rich collections are expected to play an important role in the
research community. Institutions are adapting to this new environment in terms
of services, collections, metadata and skills.oclc-padilla ; oclc-metadata ;
rluk-manifesto  New approaches are focused on improving the access of the
digital collections by using computational methods and increasing datafication
based on Data Science, Artificial Intelligence and Machine
Learning.padilla_thomas_2019_3152935 ; datafication

In this context, GLAM Labs have emerged as a new trend addressing the
publication under open licenses and reuse of the digital collections in
innovative and creative ways.open_glam_lab  For example, the Data Foundry at
the National Library of Scotland publishes data openly and in a variety of
file formats,11endnote: 1https://data.nls.uk/ LC Labs22endnote:
2https://labs.loc.gov/ supports digital transformation at the Library of
Congress and KB Lab makes available for the public experimental tools and
datasets based on the collections published by the National Library of the
Netherlands.33endnote: 3https://lab.kb.nl/

The Semantic Web was introduced early in the 2000s as an extension of the Web
based on standards to make it available as machine-readable data.w3csw  The
Semantic Web enables the publication of Linked Open Data (LOD) by using URIs
to identify the resources and creating links to external repositories in order
to enrich them.w3cld  Many organisations have explored the benefits of
adopting the Semantic Web principles to their catalogues including the Library
of Congress,44endnote: 4https://id.loc.gov/ the Bibliothèque nationale de
France55endnote: 5https://data.bnf.fr/ and the Biblioteca Virtual Miguel de
Cervantes,DBLP:journals/semweb/RomeroECS18  amongst others.

While organisations provide digital collections as datasets ready for reuse,
these are made available in many cases using traditional standards such as
MARC (Machine-Readable Cataloging), less expressive vocabularies such as
Dublin Core and text formats such as CSV. In this sense, the application of
the Semantic Web principles by GLAM organisations has several benefits
including: i) the description of the information with machine-readable,
standard and controlled vocabularies; ii) the enrichment with external
repositories; and iii) the reuse by third-party actors. In addition, this is a
key aspect in order to be part of international and collaborative initiatives
such as the Linked Open Data Cloud66endnote: 6https://lod-cloud.net/ and
Wikidata.

The objective of the present study is to introduce a framework to transform
into LOD the datasets made available by GLAM organisations under open licenses
following best practices.padilla_thomas_2019_3152935 ;
matias_frosterus_2021_4572501  The framework was then applied to three
metadata datasets made available on the Data Foundry at the National Library
of Scotland. The results of this study are publicly available and can be
applied to other domains such as digital humanities and data science. The main
contributions of this paper are as follows: (a) a framework to transform
metadata datasets into LOD following best practices; (b) a compilation of
Jupyter Notebooks to reproduce the transformation; and (c) the results of the
application to a selected set of datasets made available by the National
Library of Scotland. These contributions are intended to encourage GLAM
institutions to adopt LOD as a key element for publishing their datasets.

The paper is organised as follows: after a brief description of the state of
the art in Section 2, Section 3 describes the framework employed to transform
the datasets into LOD. The application of the method and results are shown in
Section 4. The paper concludes with an outline of the adopted method, and
general guidelines on how to use the results and future work.

##  2 Related work

The following subsections describe previous approaches of publication of LOD
by GLAM organisations, examples of reuse and data quality assessments.

###  2.1 Publishing datasets in GLAM institutions

The publication of digital collections by GLAM institutions has increased
during the last decade. These may include different material including maps,
images, metadata, OCR text, sound or video. Datasets are available in a wide
variety of repositories such as GitHub, Zenodo or dedicated websites.

In this context, Labs have emerged as a new area in GLAM institutions to make
available for the public digital collections under open licenses in order to
promote their reuse in innovative ways. Labs provide diverse datasets
published under open licenses, prototypes of tools that reuse the contents,
APIs, software, collections of reproducible Jupyter Notebooks, etc. In
addition, datasets are in general classified by type of material such as
metadata, digitised content, maps, etc. For example, the Data Foundry at the
National Library of Scotland provides digitised collections, metadata in MARC
and Dublin Core, maps and spatial data and organisational information as
datasets. Table 1 shows an overview of GLAM Labs made available by
institutions.

Table 1: Overview of GLAM Labs published by relevant GLAM institutions.

Institution |  URL
---|---
Austrian National Library |  https://labs.onb.ac.at/en/dataset/lod
Biblioteca Nacional de España |  https://bnelab.bne.es
Biblioteca Virtual Miguel de Cervantes |  https://data.cervantesvirtual.com
Bibliothèque nationale de France |  https://api.bnf.fr
Bibliothèque nationale du Luxembourg |  https://data.bnl.lu
British Library |  https://www.bl.uk/projects/british-library-labs
German National Library |  https://www.dnb.de/EN/lds
Library of Congress |  https://labs.loc.gov
National Library of the Netherlands |  https://lab.kb.nl
National Library of Scotland |  https://data.nls.uk
Royal Danish Library |  https://labs.kb.dk
Royal Library of Belgium |  https://www.kbr.be/en/projects/digital-research-lab

Many organisations have started to explore the benefits of applying the
Semantic Web and Linked Data principles to their main catalogues following
best practices.matias_frosterus_2021_4572501  Table 2 shows an overview of LOD
repositories made available by relevant GLAM institutions. Previous works have
addressed a preliminary analysis of how the transformation could be performed
such as the National Bibliography of Scotland.ifla:nls  While the different
controlled vocabularies available and used to describe the bibliographic
information share the common goal to help users to find, identify, select, and
obtain the resources they need, in some cases, classes were not part of all
models or have been renamed.ala:models  Some examples of controlled
vocabularies are:

  * •

Bibliographic Framework (BIBFRAME): it was developed by the Library of
Congress to produce linked metadata in RDF (Resource Description
Framework).lc:bibframe

  * •

Bibliographic Ontology (BIBO):bibo  provides main concepts and properties for
describing citations and bibliographic references such as books and articles
on the Semantic Web.

  * •

CIDOC Conceptual Reference Model (CIDOC CRM):cidoc  it represents an ontology
for cultural heritage information. It describes the concepts and relations
relevant to the documentation of cultural heritage.

  * •

Europeana Data Model (EDM):europeana  the formal specification of the classes
and properties that are used in Europeana.

  * •

Functional Requirements of Bibliographic Records (FRBR):ifla:frbr  a
conceptual model published by IFLA originally in 1997 known as WEMI (Work,
Expression, Manifestation, and Item).code4lib:coyle  The FRBR model was
complemented and further developed with FRAD (Functional Requirements for
Authority Data) and FRSAD (Functional Requirements for Subject Authority
Data). An ontology has been published based on this model.77endnote:
7https://vocab.org/frbr/core

  * •

IFLA Library Reference Model (LRM):ifla:lrm  the original models FRBR, FRAD,
and FRSAD were replaced by the IFLA LRM model. It was developed to resolve
inconsistencies between the three separate models.

  * •

Resource, Description and Access (RDA):88endnote:
8http://www.rdaregistry.info/ a compilation of entities and properties as
controlled vocabularies based on RDA is available for the public at the RDA
Registry as RDA element sets and RDA value vocabularies in RDF.

  * •

Schema.org:99endnote: 9https://schema.org/ a collaborative approach to create,
maintain, and promote schemas for structured data on the Internet.

Table 2: Overview of LOD repositories published by GLAM organisations.

Institution |  Vocabulary |  URL
---|---|---
Austrian National Library |  EDM, BIBFRAME, RDA |  https://labs.onb.ac.at/en/dataset/lod
Biblioteca Nacional de España |  FRBR |  http://datos.bne.es
Biblioteca Virtual Miguel de Cervantes |  RDA |  https://data.cervantesvirtual.com
Bibliothèque nationale de France |  FRBR |  https://data.bnf.fr
Bibliothèque nationale du Luxembourg |  - |  https://data.bnl.lu
BNB Linked Data Platform |  BIBO |  https://bnb.data.bl.uk
Europeana |  EDM |  https://pro.europeana.eu/page/sparql
German National Library |  BIBFRAME |  https://www.dnb.de/EN/lds
Library of Congress |  BIBFRAME |  https://id.loc.gov
National Digital Data Archive of Hungary |  DC, FOAF, schema.org, DBpedia |  http://lod.sztaki.hu
National Library of Finland |  schema.org, BIBFRAME |  https://data.nationallibrary.fi
National Library of the Netherlands |  schema.org, LRM |  https://data.bibliotheken.nl
National Library of Sweden |  KB Base Vocabulary |  https://libris.kb.se/sparql
Rijksmuseum |  EDM, SKOS |  https://data.rijksmuseum.nl/controlled-vocabularies

Other approaches are based on the aggregation of bibliographic catalogues and
authority files from several institutions such as the Library of Congress and
the national libraries of Norway and Finland.1010endnote: 10https://share-
vde.org/ In addition, innovative linked data editors within the library domain
have been developed in order to facilitate the adoption of controlled
vocabularies such as BIBFRAME Editor1111endnote:
11https://github.com/lcnetdev/bfe/ and Sinopia.1212endnote:
12https://sinopia.io/

###  2.2 Data quality assessment and reuse

Several aspects can be considered when reusing digital collections such as the
license used and the data quality. Previous works are focused on the
definition of data quality criteria classified by dimensions to assess LOD
repositories.DBLP:journals/semweb/ZaveriRMPLA16 ;
DBLP:journals/jis/RomeroECS22 ; DBLP:journals/semweb/FarberBMR18  Many of
these criteria are assessed by using SPARQL queries. More advanced approaches
are based on the definition of node constraints in the form of Shape
Expressions (ShEx) to assess RDF datasets that can be used as additional
documentation of the repositories.DBLP:journals/kbs/Fernandez-Alvarez22  Other
initiatives are based on the data quality evaluation of traditional metadata
formats for bibliographic records, such as MARC.DBLP:conf/datech/Kiraly17
Table 3 shows an overview of previous approaches to assess the data quality of
LOD repositories.

Table 3: Overview of previous works regarding LOD data quality assessment
classified by approach.

Domain |  Approach description
---|---
Libraries |  SPARQL and othersDBLP:journals/jis/RomeroECS22 ; DBLP:conf/kgswc/Hidalgo-Delgado21
Libraries |  ShExswebj:candelashex
General |  ShExDBLP:journals/kbs/Fernandez-Alvarez22
General |  SPARQL and othersDBLP:journals/semweb/ZaveriRMPLA16 ; DBLP:conf/icwe/LangerSGG18 ; DBLP:journals/semweb/FarberBMR18
General |  Ontology approachDBLP:conf/ruleml/NayakBL21

Recently, there has been a significant focus on the application of Machine
Learning (ML), Computer Vision and Artificial Intelligence to the digital
collections published by GLAM
institutions.https://doi.org/10.48550/arxiv.2207.02960 ; oclc-padilla ;
quratorSBB ; locAI  Several reuse approaches based on the LOD provided by GLAM
institutions addressed the creation of visualisations charts such as maps.
Others initiatives such as the RDA Entity Finder1313endnote:
13https://lab.kb.nl/tool/rda-entity-finder enable users to browse through the
bibliographic Work, Expression, Manifestation and Item entities by using web
interfaces. Other approaches have proposed the use of the LOD for Digital
Humanities techniques based on knowledge
discovery.DBLP:journals/semweb/Hyvonen20

With regard to the data enrichment, Wikidata has played a relevant role in
GLAM institutions. Properties have been created to link the resources such as
authors and works.DBLP:journals/jis/RomeroECS22  Wikidata follows a
collaborative edition approach in which the community is able to enrich the
resources. Moreover, Wikidata enables the storage of events, datasets or
projects by defining a model to store the information (e.g., the Coding da
Vinci cultural hackathons1414endnote:
14https://www.wikidata.org/wiki/Wikidata:WikiProject_Coding_da_Vinci and the
information about the members and computational access projects of the
International GLAM Labs Community). In addition, Wikibase, the software that
serves as storage system for Wikidata, has been explored and analysed as a
potential tool for cataloguing purposes in libraries.oclc-wikibase  Other
repositories with which to enrich a GLAM dataset may include Virtual
International Authority File (VIAF), the GeoNames gazetteer, Getty
Vocabularies1515endnote: 15https://www.getty.edu/research/tools/vocabularies/
or Library of Congress Subject Headings (LCSH).1616endnote:
16https://www.loc.gov/aba/publications/FreeLCSH/freelcsh.html

These efforts provide extensive demonstration of how digital collections can
be made available for the public and transformed to LOD using different
vocabularies and assessment methods. Nevertheless, while previous works are
based on the publication of the main catalogues of the institutions as LOD, to
our best knowledge, none of the work to date have considered the use of the
datasets made available by GLAM Labs. In addition, none of the previous
approaches have been focused on the datasets provided by the National Library
of Scotland. This analysis will be useful for the GLAM community to identify
and agree best practices regarding the publication of datasets as LOD.

##  3 A Linked Open Data framework to enhance digital collections in GLAM
institutions

This section introduces the Linked Open Data framework to enhance digital
collections in GLAM institutions. The scope of this study is limited to
metadata datasets provided by GLAM institutions under open licenses, such as
the ones published in GLAM Labs. The steps of the framework are described
below.

###  3.1 Selecting a dataset

The selection of a dataset is the first step and refers to the identification
of a digital collection that will be used. GLAM institutions provide digital
collections in several locations such as websites, code repository platforms,
datasets repositories, etc. Recently, GLAM Labs have emerged as innovative
section in GLAM institutions in which open datasets can be found. Relevant
aspects to consider when choosing a dataset are for example: i) using known
open licenses such as Creative Commons; ii) providing additional documentation
such as formats available, provenance information and examples of use; and
iii) using a permanent identifier to ease the
citation.doi:10.1177/01655515211060530 ; padilla_thomas_2019_3152935

In addition, data quality is becoming a key element when using advanced
methods based on NLP, ML and AI.DBLP:conf/qurator/Neudecker22 ;
DBLP:conf/icaart/StrienBAHMC20  Recently, carbon footprint information has
become a crucial element for example in terms of processing, training AI
models, and generation and curation of the datasets that can be additionally
considered when selecting a dataset for
reuse.https://doi.org/10.48550/arxiv.2207.02960 ; Mariette_2022

###  3.2 Data source analysis

This step includes the preliminary analysis of the original contents provided
by the dataset. In some cases, this information might be provided as
additional documentation such as the case of the Theatre Posters published by
the Data Foundry at the National Library of Scotland.1717endnote:
17https://data.nls.uk/data/metadata-collections/theatre-posters/ Other types
of useful documentation may include research guides about the datasets, such
as the case of Chronicling America,1818endnote:
18https://guides.loc.gov/chronicling-america-topics and Jupyter Notebooks that
explores the content and provides a preliminary idea of the information (e.g.,
dates, titles, etc.).nls-doc

When this information is not available, the difficulty depends on the original
data format. For example, CSV files can be easily explored using the Python
software Pandas.111https://pandas.pydata.org Other formats based on XML such
as MARCXML and Dublin Core may require other tools to extract the contents.
Several aspects can be considered for the analysis including:

  * •

the total number of resources.

  * •

the types of resources included (e.g., writer, maps, works, paintings, etc.).

  * •

the number of different metadata fields.

  * •

the different values contained in a particular metadata field (e.g., roles or
places).

  * •

main subjects or topics provided by the contents.

When using large datasets in terms of size this step may require an additional
task to split the original source or to transform it to an manageable format.
For instance, Pandas enables users to explore large data sets efficiently
using the chunksize attribute. Other approaches are based on the use of Hadoop
and Spark.10.1145/2814864.2814880

This process can provide as an output useful information about the size of the
dataset and the expressiveness of the vocabularies used.

###  3.3 Extracting information

The extraction of information consists on the identification of the key
metadata elements that are relevant to be included in the final dataset. Some
examples include title, author, publisher, copyright, sources, etc. Depending
on the original format the metadata fields may be accessed in different ways.
For example, a title in Dublin Core is provided in a dc:title metadata field
while in MARCXML the title is provided by the field 245.

Several software libraries can be used to extract the metadata such as Pandas,
XSLT1919endnote: 19See, for example, https://github.com/vioil/Transforming-
MARCXML-with-XSLT/blob/master/marc-to-dc.xsl and pymarc2020endnote:
20https://pypi.org/project/pymarc/ for MARCXML.

Note that when using a transformation tool that automatically generates the
RDF output, this step is not strictly required as is described in the next
step.

###  3.4 Data modelling and transformation to RDF

The adoption of the Semantic Web concepts requires the use of standard and
controlled vocabularies that describe how the information is modelled in terms
of classes and properties. RDF provides the basis to model and define the data
by using triples.

While best practices recommend reusing existing vocabularies to foster the
connection of resources,ld-bp  there exist tools to facilitate the creation of
new vocabularies. DBLP:journals/aimatters/Musen15  Some examples of
vocabularies are introduced in Section 2.

When using controlled vocabularies, classes represent a particular type in a
domain. For example, the class author represents a writer in a library.
Instances of a particular class are identified by URIs that may follow
patterns (e.g., /author/{id}). Other approaches are based on the use of
generic URIs (e.g., identifiers).

The transformation to LOD relies on a mapping process using as input the
original sources and generating as an output the metadata described using
controlled vocabularies. Each resource is identified by an URI following the
patterns established. Then, the different properties are assigned to the
resource.

The transformation can be performed using different methods and tools. For
example, many software libraries in several programming languages (e.g., Java,
Python or PHP) are available to work with RDF. However, the use of these
software libraries requires highly specialised and extensive technical skills.
Table 4 presents an overview of methods and tools for working with and
creating RDF.

Table 4: Overview of approaches and tools for working with and creating RDF.

Tool |  Description
---|---
Apache Jena2121endnote: 21https://jena.apache.org/ |  Java software library
BIBFRAME Editor |  HTML interface for editing BIBFRAME metadata
EasyRdf2222endnote: 22https://www.easyrdf.org/ |  PHP software library
marc2bibframe22323endnote: 23https://github.com/lcnetdev/marc2bibframe2 |  XSLT script to transform from MARC to BIBFRAME
OpenRefine2424endnote: 24https://openrefine.org/ |  Transformation interface
RDF JavaScript LibrariesrdfJavascript  |  RDF JavaScript Libraries
RDFLib2525endnote: 25https://rdflib.readthedocs.io |  Python software library

Other approaches are based on the use of tools that transform the original
metadata into a specific vocabulary. For instance the Library of Congress
provides a set of tools for the transformation from MARC to BIBFRAME.lc-
marc2bib  The use of these tools requires the adjustment of several
configuration parameters including the domain name (by default
http://example.org), the record that contain the identifier or the
serialization format to obtain the output result. Table 5 shows an overview of
previous works addressing the transformation of MARC into RDF controlled
vocabularies.

Table 5: Overview of previous works concerning the automatic transformation of
the original sources into RDF vocabularies.

Reference |  Approach description
---|---
MarimbaDBLP:journals/lht/Vila-SueroG13  |  Convert MARC records to FRBR
marc2bibframe2lc-marc2bib  |  Convert MARC records to BIBFRAME
MARC2RDAmarc2rda  |  Mapping between MARC and RDA
MINTDBLP:conf/ercimdl/CharlesITH13  |  Cross-domain metadata to EDM
marc2dc222https://github.com/vioil/Transforming-MARCXML-with-XSLT |  Convert MARC records to DC

In addition, there are tools to enable users to edit the data using a specific
vocabulary. For example, the BIBFRAME Editor is an HTML user-friendly
interface that enables users to edit the metadata.2626endnote:
26https://github.com/lcnetdev/bfe/

Recently, mappings between traditional metadata formats and advanced
controlled vocabularies have been published.2727endnote: 27See, for example,
https://www.loc.gov/bibframe/mtbf/ In addition, dedicated websites have been
published to describe the classes and properties that can be used to describe
the information as RDF.2828endnote: 28See, for example,
https://www.iflastandards.info/lrm/lrmer Previous works have addressed the
comparison of bibliographic models such as LRM and
BIBFRAME.DBLP:journals/ires/AalbergTM19

###  3.5 Data enrichment

Linked Data enables the interconnection of resources by using the property
owl:sameAs. The use of this property eases the enrichment process by adding
contextual information from external repositories. In other cases, new
properties are specifically created to add links from a particular dataset.
For example, Wikidata provides different properties for each
repository.2929endnote: 29See, for example, the property P268 for the
Bibliothèque nationale de France. Section 2 provides a list of potential
repositories to use for enriching a dataset.

In order to identify resources in text and link them to external repositories
several techniques can be used. For example, NER (Named Entity Recognition)
enable the identification of entities in the text such as authors,
organisations and locations. More advanced approaches are based on the
identification and linking to repositories such as Wikidata and
DBpedia.DBLP:conf/clef/LabuschN22  An example of entity linking tool is
DBpedia spotlight that can be used to automatically annotating mentions of
DBpedia resources in text.DBLP:conf/i-semantics/MendesJGB11  Other software
libraries such as spaCy3030endnote: 30https://spacy.io/ and
CoreNLP3131endnote: 31https://stanfordnlp.github.io/CoreNLP/ enable the
creation of pipelines and the training of models for NER and entity linking
methods.

###  3.6 Quality assessment

Data quality has become a crucial aspect when publishing a digital collection.
Several techniques can be used in this sense based on manual and automatic
tasks, and data quality criteria as is described in Section 2.

While SPARQL is a powerful language to query an RDF dataset (e.g., see, for
example, Listing LABEL:lst:sparql, more sophisticated approaches are based on
the mining of ShEx schemas to define node constraints to be tested against RDF
datasets. Listing LABEL:lst:shex shows an example of ShEx schema to validate
resources typed as foaf:Person and schema:Person.

⬇

SELECT (COUNT(distinct ?s) as ?total)

WHERE {

?s dc:subject ?subject .

FILTER regex(?subject, ”Gaelic”)

}

⬇

ex:Person {

rdf:type [foaf:Person] ;

rdf:type [schema:Person] ;

foaf:name xsd:string ;

skos:prefLabel xsd:string ;

schema:name xsd:string

}

Additional aspects to consider when assessing a LOD repository include the
inclusion of labels in multiple languages, the use of a public SPARQL
endpoint, the provision of machine-readable licensing information or the
validation of the external URIs.DBLP:journals/semweb/FarberBMR18 ;
DBLP:journals/jis/RomeroECS22

###  3.7 Publication and Exploration

The final step corresponds to the publication of the datasets in a public
repository. Recently, several platforms have become popular in the research
community such as DataCite, Zenodo and Hugging Face. The use of permanent
identifiers such as DOI (Digital Object Identifier) is crucial to support the
citation.

LOD repositories are in many cases made available using a public SPARQL
endpoint. This requires the set up of an environment to install an RDF storage
system. Some examples include Virtuoso,3232endnote:
32https://virtuoso.openlinksw.com/ Jena TDB3333endnote:
33https://jena.apache.org/documentation/tdb/ and RDF4J.3434endnote:
34https://rdf4j.org/

In addition, RDF datasets can be described by means of controlled vocabularies
such as the Vocabulary of Interlinked Datasets (VoID).w3c-void  This
information can be useful for data cataloging and archiving of the datasets as
well as to provide additional metadata to users. Listing LABEL:lst:void shows
an example of VoID description describing an overview of the properties
available.

⬇

:MovingImageArchive a void:Dataset;

dcterms:title ”Moving␣Image␣Archive”;

dcterms:description
”RDF␣data␣extracted␣from␣the␣Moving␣Image␣Archive␣dataset”;

dcterms:license <https://creativecommons.org/publicdomain/mark/1.0/>;

dcterms:publisher :NLS;

dcterms:contributor :GC;

dcterms:source <https://data.nls.uk/data/metadata-collections/moving-image-
archive/>;

dcterms:source <https://github.com/hibernator11/nls>;

dcterms:modified ”2022-11-09”^^xsd:date;

void:feature <http://www.w3.org/ns/formats/Turtle>;

void:dataDump

<https://raw.githubusercontent.com/hibernator11/nls/master/rdf/datasetEnriched.ttl>;

void:vocabulary <http://xmlns.com/foaf/0.1/>;

void:vocabulary <http://www.europeana.eu/schemas/edm/>;

void:vocabulary <https://schema.org/>;

void:classes 7;

void:exampleResource <https://example.org/film/0001>;

void:properties 23;

void:triples 263476;

Platforms such as the Linked Open Data Cloud enable the inclusion of the LOD
datasets in aggregators that can be useful in terms of visibility.

The publication can include examples of use based on prototypes and tools. New
approaches have recently emerged to show how to explore a dataset that enable
the combination of code and text and can be executed in the cloud based on the
use of Jupyter Notebooks.3535endnote: 35See, for example, https://glam-
workbench.net/

##  4 Results

This section presents the application of the framework proposed in Section 3
to three datasets provided by the Data Foundry at the National Library of
Scotland:

  * •

Moving Image Archive (MIA): this dataset represents the descriptive metadata
from the Moving Image Archive catalogue which is the Scotland’s national
collection of moving image.nls-mia  The dataset is provided as a zip file
containing a MARCXML and a Dublin Core files. The dataset is available under a
license Creative Commons Zero 1.0 Universal.

  * •

National Bibliography of Scotland (NBS): this dataset corresponds to the
bibliographic records for the National Bibliography of Scotland and part of an
ongoing programme of work to create and expand the bibliography.nls-nbs  The
dataset references materials published in Scotland from National Library of
Scotland’s main catalogue.

  * •

Bibliography of Scottish Literature in Translation (BOSLIT): it was a Voyager
database that was maintained by the BOSLIT Committee including information
about Scottish literature in translation which aimed to serve the needs of
academic researchers, writers and translators, libraries, schools, literature
administrators and general readers.nls-boslit

We have selected the Data Foundry since it is one of the most relevant GLAM
Labs/services providing digital collections as datasets amenable for
computational use.ames_cerl ; Ames_2021 ; ames_sarah_2020_3862050 ;
doi:10.1177/2053951720970576  The datasets have been selected according to the
following criteria: i) they are included in the metadata datasets section; ii)
they are available under open licenses; and iii) they are available as MARCXML
and Dublin Core format.

The vocabularies presented in Section 2 were analysed in order to identify the
most appropriate vocabulary to describe the metadata provided by each of the
datasets. As a result, we used as main vocabularies Schema.org for the Moving
Image Archive and BIBFRAME for the National Bibliography of Scotland and
BOSLIT datasets. Table 6 presents an overview of the features of the final
datasets.

The code repository including a collection of reproducible Jupyter Notebooks
is available on GitHub.3636endnote: 36https://github.com/hibernator11/nls-
fellowship-2022-23 Each of the Jupyter Notebooks included in the collection
describes a step in the framework applied to the dataset. The LOD datasets
generated are available as dump files.

###  4.1 Data modelling and transformation to LOD

The three datasets were transformed to LOD using different techniques and
vocabularies as is described below.

For the Moving Image Archive dataset, the transformation was based on the
vocabulary Schema.org, using as main entity the class VideoObject. Schema.org
was used since it was adopted by the web community as the shared vocabulary
among search engines and provides a rich set of properties to describe
multimedia resources such as videos.DBLP:journals/jodl/FreireRHMI20
Additional classes and properties from EDM and FOAF have been used to describe
the resources. Note that the original identifier is used to create the URIs in
the RDF dataset (e.g., http://example.org/(filmRef)0002#Instance)

Table 6: Overview of the RDF datasets generated from the Data Foundry.

Description | MIA | NBS | BOSLIT
---|---|---|---
Classes | 7 | 190 | 124
Properties | 23 | 185 | 129
External links | 75 | 4274 | 611
Triples | 263476 | 47882223 | 4503316

Following previous approaches,DBLP:journals/ires/AalbergTM19 ;
DBLP:journals/lht/Vila-SueroG13 ; DBLP:journals/semweb/RomeroECS18  the
MARCXML datasets National Bibliography of Scotland and BOSLIT were transformed
into BIBFRAME using a XSLT template that was applied to each original record.
The final RDF dataset was loaded into a Apache Jena TBS RDF storage system.
Figure 1 shows an overview of the steps followed.

Figure 1: Overview of the steps to transform into RDF the National
Bibliography of Scotland and BOSLIT datasets using as main vocabulary
BIBFRAME.

The XSLT converter application process the fields of each MARC record and
build the two main elements, a bf:Work and a bf:Instance. In addition, the
process generates a bflc:adminMetaData property of the bf:Work to include
provenance information and generates the bf:hasItem properties of the
bf:Instance. URL patterns are based on the baseuri parameter (default
http://example.org/), the record ID of the MARC record (by default the value
of the 001 field), and a hash URI for the new element. The output is
serialized as RDF/XML. For elements that are not typed as bf:Work or
bf:Instance, the hash URI is constructed from the element class, the field
number, and the position of the field in the MARC record (e.g.,
http://example.org/9923749153804341#Agent100-12). Figure 2 shows an overview
of the main classes used to model the data based on BIBFRAME.

Figure 2: Overview of the main BIBFRAME classes used to model data based on
the National Bibliography of Scotland.

In BIBFRAME, the class bf:Hub is used as an abstract resource that functions
as a bridge between two works. MARC metadata fields (e.g., 130 and 240) aiming
at storing information about works that has appeared under varying titles may
generate an additional resource typed as bf:Hub including the uniform title
and the author when available. Figure 3 shows an example of how the metadata
representing translations of the work Treasure island in the BOSLIT dataset
are modelled according to the BIBFRAME vocabulary.

Figure 3: Example of BBIFRAME data modelling based on the work Treasure
island.

###  4.2 Data quality assessment

Following previous approaches,DBLP:journals/semweb/FarberBMR18 ;
DBLP:journals/jis/RomeroECS22 ; DBLP:journals/kbs/Fernandez-Alvarez22  a
validation of the RDF datasets was performed in terms of several dimensions as
is described below. Table 10 shows the results obtained.

With regard to the _accuracy_ dimension, the _syntactic validity of RDF
documents_ was assessed based on a random sample of 100 resources per dataset
and using the W3C RDF Validation Service.3737endnote:
37https://www.w3.org/RDF/Validator/ All the documents were assessed as
correct.

With regard to the _semantic validity of literals_ criterion, regular
expressions were used to test textual descriptions included such as roles and
text dates. A sample of 100 resources per dataset was tested using a manual
revision in order to identify inconsistencies. For example, some
inconsistencies in text descriptions concerning roles (e.g., auhtor,
orgasnizer, ”.”, etc.) were identified in the National Bibliography of
Scotland dataset. Some textual metadata fields in the Moving Image Archive
provide a list of abbreviations (e.g., ”a.d.” is the abbreviation for ”art
director”) that will require a text processing task to extract all the
information. Additional examples of abbreviated roles (e.g., ed.) are included
in the BOSLIT dataset.

The criterion _semantic validity of triples_ was evaluated using a gold
standard as a reference in order to assess whether the metadata was correct.
The metadata of a random sample of 50 resources were manually evaluated
against VIAF and Wikidata in order to assess whether the metadata information
such as title, date of birth, date of death and name was correct. All the
datasets satisfied this criterion.

With regard to the _check of duplicate entities_ criterion, some potential
issues were identified in the National Bibliography of Scotland and BOSLIT RDF
datasets:

  * •

since the RDF transformation is focused on work records, similar authors that
potentially are the same are treated as different resources by using different
URLs. See, for example, Listing LABEL:lst:nbs-stevenson. The use of external
identifiers such as VIAF can help in this sense to facilitate the clustering
of records.

  * •

some BIBFRAME works obtained as output in the transformation process included
a bf:expressionOf property linking to a bf:Hub resource. According to the
BIBFRAME documentation, a resource typed as bf:Hub represent an abstract
resource that functions as a bridge between two Works. For example, MARC
fields 1303838endnote: 38https://www.loc.gov/marc/bibliographic/bd130.html and
2403939endnote: 39https://www.loc.gov/marc/bibliographic/bd240.html store
standardised strings such as uniform titles that generates a bf:Hub resource
including the main author, the title and the language, when available in the
original sources. Since the URL patterns for the bf:Hub and the bf:Work are
based on the identifier of the work, the XSLT template may generated repeated
bf:Hub resources with the same information but with different URL.

⬇

PREFIX bf:<http://id.loc.gov/ontologies/bibframe/>

PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>

SELECT ?label ?a

WHERE {

?s bf:contribution ?c .

?c bf:agent ?a .

?a rdfs:label ?label .

FILTER regex(str(?label), ”Stevenson,␣Robert␣Louis”)

}

LIMIT 10

Table 7: Overview of the results retrieved using the SPARQL query shown in
Listing LABEL:lst:nbs-stevenson.

Label |  URL
---|---
Stevenson, Robert Louis, 1850-1894 |  http://example.org/9929751083804341#Agent100-9
Stevenson, Robert Louis, 1850-1894 |  http://example.org/9923749153804341#Agent100-12
Stevenson, Robert Louis, 1850-1894 |  http://example.org/9923749153804341#Agent800-28
Stevenson, Robert Louis, 1850-1894 |  http://example.org/9915244463804341#Agent100-13
Stevenson, Robert Louis, 1850-1894 |  http://example.org/9944502973804341#Agent100-10

Regarding the dimension _trustworthiness_ , all the datasets were manually
curated in a closed system. While provenance information about the datasets is
provided in the Data Foundry, however, no information is available about RDF
statements or resources.

_Consistency_ was measured using several SPARQL queries to identify
constraints provided by the vocabularies based on statements such as
owl:disjointWith. For example, this enables the identification of resources
that are typed as Person and Corporate Body. All the datasets satisfied the
constraints. In addition, a collection of ShEx schemas has been automatically
generated to test against the RDF datasets using the tool
sheXer.DBLP:journals/kbs/Fernandez-Alvarez22  The ShEx schemas can be used as
additional documentation for users and curators to better understand how the
data is modelled.

Concerning the _relevancy_ dimension, none of the datasets supports the
ranking of statements, entities or relations, which could be used to, for
example, state the order of contributors in a work.

With regard to the _completeness_ dimension, the _schema completeness_
criterion measures the extent to which classes and relations are included in a
dataset. Following previous approaches, a gold standard has been defined with
the common classes and properties that could be included based on vocabularies
such as BIBFRAME and Schema.org (see Table 8).

Table 8: Classes and properties selected to assess the completeness criteria.

Pattern |  Description
---|---
Author |  name, date of birth, date of death
Work/Video |  title, date of publication, author
Organisation |  name
Place |  name

With respect to the _column completeness_ criterion, it measures the rate of
instances having a specific property, averaged for all the properties shown in
Table 8. The Moving Image Archive dataset obtained a lower score since authors
are included in many cases as text descriptions using the metadata field
schema:creditText.

With reference to the _population completeness_ criterion, it determines the
extent to which the datasets covers a basic population. The coverage of
resources was compared with a list of resources provided by Wikidata: i) a
list of Scottish poets, essayists, novelists and writers for the National
Bibliography of Scotland and BOSLIT datasets (see Listing LABEL:lst:wikidata-
completeness); and ii) a list of Scottish filmmakers and explorers for the
Moving Image Archive.

⬇

SELECT DISTINCT ?sLabel ?viaf

WHERE {

VALUES ?occupation { wd:Q36180 wd:Q49757 wd:Q6625963 wd:Q11774202}

?s wdt:P31 wd:Q5 .

?s wdt:P106 ?occupation .

?s wdt:P19 wd:Q22 .

?s wdt:P214 ?viaf

SERVICE wikibase:label { bd:serviceParam wikibase:language ”en”. }

}

LIMIT 50

_Timeliness_ dimension includes several criteria to measure for a digital
object the extent to which it is sufficiently up-to-date. The criterion
_frequency_ measures if the resource includes metadata about when was created,
stored, accessed or cited. The frequency of updates was consulted in all the
datasets. However, none of the datasets provide information about the validity
period or the modification date of statements.

Regarding the _easy of understanding_ dimension, user-friendly URLs for the
resources are used in all the datasets. For example, Table 9 shows the URL
patterns used to identify the resources in the Moving Image Archive RDF
dataset. All the resources provided a rdfs:label property to describe them and
text descriptions were only provided in English. In addition, the datasets
used a understandable RDF serialization format such as Turtle. In the
particular case of the National Bibliography of Scotland and BOSLIT datasets,
the tool marc2bibframe only generated RDF/XML as a result of the
transformation using the XSLT template.

Table 9: URL patterns used to identify the resources in the Moving Image
Archive RDF dataset.

Pattern |  Description
---|---
/author/name |  authors
/film/id |  videos
/location/name |  geographic locations
/organisation/name |  organisations

With regard to the _interoperability_ dimension, the datasets do not use blank
nodes or RDF reification. Only one serialization format is provided for
dataset. All the datasets used external vocabularies to describe the metadata
including EDM, FOAF, BIBFRAME and Schema.org.

Concerning the _accessibility_ dimension, since the URLs are not resolvable
due to the use of a testing domain URL, many criteria in this dimension are
not satisfied. While the datasets are not available by means of a public
SPARQL endpoint, they are available as dump files including metadata.

The _licencing_ dimension includes a criterion that measures the extent to
which the datasets provide a machine-readable licence. All the datasets
satisfied this criterion.

The _interlinking_ dimension provides two criteria to measure the external
links included in the datasets. The _interlinking via owl:sameAs_ criterion
computes the rate of instances having at least one external link. In addition
to the property owl:sameAs, other properties were identified that link to
external repositories such as bf:role, bf:language and bf:geographicCoverage,
in particular for the National Bibliography of Scotland and BOSLIT datasets.

With regard to the _validity of external URIs_ criterion, the number of HTTP
errors were computed for a random sample of 500 URLs shown in triples linking
to external repositories. In particular, some issues were identified in the
National Bibliography of Scotland dataset regarding the _interlinking_
dimension:

  * •

the automatic transformation process uses the MARC List for Languages
controlled vocabulary to create links using the original text information.
However, in some cases, the links created are based on non-existent
identifiers (e.g., see, for example, the white space in the following URL
http://id.loc.gov/vocabulary/geographicAreas/e-uk-%20st and
http://id.loc.gov/vocabulary/languages/d).

  * •

resources typed as bf:Work are linked to bf:Agent resources by using a
resource typed as bf:Contribution including the name of the author and the
role. The Relators terms vocabulary4040endnote:
40https://id.loc.gov/vocabulary/relators.html provided by the Library of
Congress includes a list of roles that the XSLT template is able to map
against the original sources. Up to 15 roles were mapped including, for
example, http://id.loc.gov/vocabulary/relators/aut and
http://id.loc.gov/vocabulary/relators/ctb. The XSLT template generated
additional roles that were included uniquely as text descriptions instead of
URLs (e.g., translator, illustrator, printer, honoree, presenter, etc.).

Table 10: Summary of results according to the data quality criteria to assess
LOD classified by dimensions based on previous
works.DBLP:journals/semweb/FarberBMR18 ; DBLP:journals/jis/RomeroECS22

Dimension |  Criterion | MIA | NBS | BOSLIT
---|---|---|---|---
Accuracy |  Syntactic validity of RDF documents | 1 | 1 | 1
|  Syntactic validity of literals | 0.98 | 0.96 | 0.91
|  Semantic validity of triples | 1 | 1 | 1
|  Check of duplicate entities | 1 | 1* | 1*
Trustworthiness |  On dataset level | 1 | 1 | 1
|  On statement level | 0 | 0 | 0
|  Using unknown and empty values | 0 | 0 | 0
Consistency |  Consistency of schema restrictions during insertion of new statements | 0 | 0 | 0
|  Consistency of statements with respect to class constraints | 1 | 1 | 1
|  Consistency of statements with respect to relations constraints | 1 | 1 | 1
Relevancy |  Creating a ranking of statements | 0 | 0 | 0
Completeness |  Schema completeness | 1 | 1 | 1
|  Column completeness | 0.61 | 0.79 | 0.69
|  Population completeness | 0.35 | 0.5 | 0.3
Timeliness |  Frequency | 0.5 | 0.5 | 0.5
|  Specification of the validity period of statements | 0 | 0 | 0
|  Specification of the modification date of statements | 0 | 0 | 0
Ease of understanding |  Description of resources | 1 | 1 | 1
|  Labels in multiple languages | 0 | 0 | 0
|  Understandable RDF serialization | 1 | 1 | 1
|  Self-describing URIs | 1 | 1 | 1
Interoperability |  Avoiding blank nodes and RDF reification | 1 | 1 | 1
|  Provisioning of several serialization formats | 0.5 | 0.5 | 0.5
|  Using external vocabulary | 1 | 1 | 1
|  Interoperability of proprietary vocabulary | 1 | 1 | 1
Accessibility |  Dereferencing possibility of resources | 0 | 0 | 0
|  Availability of the repository | 0 | 0 | 0
|  Availability of a public SPARQL endpoint | 0 | 0 | 0
|  Provisioning of an RDF export | 1 | 1 | 1
|  Support of content negotiation | 0 | 0 | 0
|  Linking HTML sites to RDF serializations | 0 | 0 | 0
|  Provisioning of metadata | 1 | 1 | 1
Licensing |  Provisioning machine-readable licensing information | 1 | 1 | 1
Interlinking |  Interlinking via owl:sameAs | 0.004 | 0,82 | 0,74
|  Validity of external URIs | 1 | 0,66 | 1

###  4.3 Exploration

Enriching the data with external repositories such as Wikidata and GeoNames
enables the addition of contextual information that can be useful for
different purposes including visualisation and data analysis. For example,
Figure 4 shows the map visualisation as a result of the SPARQL query that
retrieves from Wikidata all the locations provided by the Moving Image Archive
dataset.

Figure 4: Map visualisation created from a SPARQL query in Wikidata using the
metadata provided by the Moving Image Archive dataset.

The transformation process to BIBFRAME included an enrichment step by using
the controlled vocabularies provided by the Library of Congress such as MARC
List for Languages,lc_lan  MARC List for Geographic Areaslc_ga  and MARC
Relators terms. In addition, Wikidata provides the property P4801 to add
identifiers for an item based on the controlled vocabularies maintained by the
Library of Congress that can be used to add contextual
information.4141endnote: 41https://www.wikidata.org/wiki/Property:P4801
Listing LABEL:lst:nbs-query shows and example of SPARQL query to retrieve the
works in Spanish from the National Bibliography of Scotland dataset.

⬇

PREFIX bf:<http://id.loc.gov/ontologies/bibframe/>

SELECT distinct ?work ?title

WHERE {?work bf:language <http://id.loc.gov/vocabulary/languages/spa> .

?work bf:title ?resTitle .

?resTitle bf:mainTitle ?title

}

Table 11: Overview of the results retrieved using the SPARQL query shown in
Listing LABEL:lst:nbs-query.

URL |  Title
---|---
http://example.org/9944730413804341#Work |  El Palacio de Holyroodhouse
http://example.org/999356403804341#Work |  La gente y los lugares
http://example.org/9929767743804341#Work |  El Ingenioso Hidalgo Don Quixote de la Mancha. (Del Ingenioso Caballero Don Quixote de la Mancha.)
http://example.org/9919385013804341#Work |  Una Gramática colonial del Quichua del Ecuador

The BOSLIT dataset is a source of information about Scottish literature in
translation. In this way, we explored the dataset in order to analyse how the
translations of a work are described using the vocabulary BIBFRAME. Listing
LABEL:lst:boslit-titles shows the number of translations in several languages
for the works Treasure island and Strange case of Doctor Jekyll and Mister
Hyde and the results are shown in Table 12. In addition, Listing
LABEL:lst:boslit-translations shows a SPARQL query to explore the translation
relationships established between the resources in the BOSLIT dataset and the
results are shown in Table 13.

⬇

PREFIX bf:<http://id.loc.gov/ontologies/bibframe/>

SELECT ?mainTitle (COUNT(?work) as ?total)

WHERE {

?work bf:expressionOf ?exp.

?exp bf:title ?title.

?title bf:mainTitle ?mainTitle

}

GROUP BY ?mainTitle

HAVING (?total>1)

ORDER BY DESC(?total)

LIMIT 20

Table 12: Number of translations per language of the works Strange case of
Doctor Jekyll and Mister Hyde and Treasure island included in the BOSLIT
dataset.

Title | No. of works
---|---
Strange case of Doctor Jekyll and Mister Hyde. Italian | 61
Strange case of Doctor Jekyll and Mister Hyde. Spanish | 61
Strange case of Doctor Jekyll and Mister Hyde. French | 56
Treasure island. Spanish | 172
Treasure island. German | 137
Treasure island. Italian | 116
Treasure island. French | 113
Treasure island. Russian | 64
Treasure island. Japanese | 46
Treasure island. Dutch | 42

⬇

PREFIX bf:<http://id.loc.gov/ontologies/bibframe/>

SELECT ?work ?workMainTitle ?exp

WHERE {

?work bf:title ?workTitle .

?workTitle bf:mainTitle ?workMainTitle .

?work bf:expressionOf ?exp.

?exp bf:title ?expTitle.

?expTitle bf:mainTitle
”Strange␣case␣of␣Doctor␣Jekyll␣and␣Mister␣Hyde.␣Italian”

}

LIMIT 20

Table 13: Editions in Italian of the work Strange case of Doctor Jekyll and
Mister Hyde included by the BOSLIT dataset using BIBFRAME as main vocabulary.
The bf:Work is related to a bf:Hub resource describing the uniform title of
the main work.

Work |  Title |  Hub
---|---|---
http://example.org/15726#Work |  Lo strano caso del dottor Jekyll e del dottor [sic] Hyde ; Il signore di Ballantrae |  http://example.org/15726#Hub240-10
http://example.org/9803#Work |  Lo strano caso del dottor Jekyll e del signor Hyde |  http://example.org/9803#Hub240-9
http://example.org/9962#Work |  Lo strano caso del dottor Jekyll e del signor Hyde |  http://example.org/9962#Hub240-8
http://example.org/16238#Work |  Il dottor Jekyll |  http://example.org/16238#Hub240-9
http://example.org/12727#Work |  Il dottor Jekill [sic] |  http://example.org/12727#Hub240-9
http://example.org/12333#Work |  Lo strano caso del dottor Jekill [sic] |  http://example.org/12333#Hub240-9

###  4.4 Discussion

Popular and relevant vocabularies often are accompanied of documentation,
examples of use, and generation and edition tools. However, while the use of
automatic tools eases the transformation process into RDF, there is need for
additional customization in terms of features such as URL patterns and the use
of external repositories to create links. The process can be constrained by
the level of configuration allowed by the tool. In addition, the use of this
type of tools relies on the assumption that the original data is of high
quality. Moreover, understanding the output of the automatic process, in
particular how the data is modelled, may be a cumbersome task. In this way,
RDF software libraries can be useful in this context to model the data.

The identification of unique items in an RDF dataset can be improved by
including a manual revision. In some cases, the lack of information provided
in the original sources (e.g., birth year) can make this process more complex.

Linked Data promotes the use of URIs to identify resources. Resolvable URIs
for the resources require the use of a domain (e.g., https://data.nls.uk/). In
this work, we have used the domain https://example.org when evaluating the
framework and for testing purposes. In addition, RDF storage systems are
required for an efficient access to the data when using large datasets.

Access and exploration of datasets using traditional metadata formats is
constrained due to the use of the textual descriptions. The use of expressive
languages to model the data enables the analysis of large datasets using a
wide diversity of concepts (e.g., Person, Work, etc.) and properties as access
points. In some cases, metadata fields (e.g., roles, dates, etc.) provide
textual descriptions that can be further processed to provided a more
expressive description of the information. In addition, the use of LOD enables
the creation of innovative visualisations that can be useful not only to
explore the data but also to gain insight.

##  5 Conclusions

Over the past few years, there has been a growing interest in publishing and
reusing the digital collections made available by GLAM institutions. The
adoption of the Semantic Web and Linked Data principles provide several
benefits to the organisations in terms of interoperability and enrichment.

Based on previous work, we defined a framework described in Section 3 for
transforming metadata datasets published by relevant GLAM institutions to LOD.
The framework was applied to three datasets published by the National Library
of Scotland. The evaluation showed that the framework can be useful for other
organisations willing to publish datasets as LOD following best practices.

Future work to be explored includes the evaluation of additional datasets, the
improvement of the framework to include additional types of datasets such as
OCR and the exploration of data spaces to include the final datasets.

This work has been funded by The National Librarian’s Research Fellowship in
Digital Scholarship 2022-23 at the National Library Scotland.

###### Notes

  1. 1 https://data.nls.uk/
  2. 2 https://labs.loc.gov/
  3. 3 https://lab.kb.nl/
  4. 4 https://id.loc.gov/
  5. 5 https://data.bnf.fr/
  6. 6 https://lod-cloud.net/
  7. 7 https://vocab.org/frbr/core
  8. 8 http://www.rdaregistry.info/
  9. 9 https://schema.org/
  10. 10 https://share-vde.org/
  11. 11 https://github.com/lcnetdev/bfe/
  12. 12 https://sinopia.io/
  13. 13 https://lab.kb.nl/tool/rda-entity-finder
  14. 14 https://www.wikidata.org/wiki/Wikidata:WikiProject_Coding_da_Vinci
  15. 15 https://www.getty.edu/research/tools/vocabularies/
  16. 16 https://www.loc.gov/aba/publications/FreeLCSH/freelcsh.html
  17. 17 https://data.nls.uk/data/metadata-collections/theatre-posters/
  18. 18 https://guides.loc.gov/chronicling-america-topics
  19. 19 See, for example, https://github.com/vioil/Transforming-MARCXML-with-XSLT/blob/master/marc-to-dc.xsl
  20. 20 https://pypi.org/project/pymarc/
  21. 21 https://jena.apache.org/
  22. 22 https://www.easyrdf.org/
  23. 23 https://github.com/lcnetdev/marc2bibframe2
  24. 24 https://openrefine.org/
  25. 25 https://rdflib.readthedocs.io
  26. 26 https://github.com/lcnetdev/bfe/
  27. 27 See, for example, https://www.loc.gov/bibframe/mtbf/
  28. 28 See, for example, https://www.iflastandards.info/lrm/lrmer
  29. 29 See, for example, the property P268 for the Bibliothèque nationale de France.
  30. 30 https://spacy.io/
  31. 31 https://stanfordnlp.github.io/CoreNLP/
  32. 32 https://virtuoso.openlinksw.com/
  33. 33 https://jena.apache.org/documentation/tdb/
  34. 34 https://rdf4j.org/
  35. 35 See, for example, https://glam-workbench.net/
  36. 36 https://github.com/hibernator11/nls-fellowship-2022-23
  37. 37 https://www.w3.org/RDF/Validator/
  38. 38 https://www.loc.gov/marc/bibliographic/bd130.html
  39. 39 https://www.loc.gov/marc/bibliographic/bd240.html
  40. 40 https://id.loc.gov/vocabulary/relators.html
  41. 41 https://www.wikidata.org/wiki/Property:P4801

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