Linking Library Data for Quality Improvement
and Data Enrichment
Uldis Bojārs, Artūrs Žogla and Elita Eglīte
National Library of Latvia, Mūkusalas iela 3, Riga, Latvia
Keywords: Data Quality, Library Authority Files, Linked Data, Library of Congress Subject Headings, VIAF.
Abstract: Dataset interlinking holds the potential for data quality improvement and data enrichment as demonstrated
by the Linked Open Data project. This paper explores the library domain characterized by carefully curated
datasets that require high quality standards. It presents the results of an experiment in dataset quality
improvement and data enrichment conducted by linking library datasets and analysing the results. The
experiment was performed using subject authority files from the National Library of Latvia and the Library
of Congress. The paper concludes by discussing how Linked Data can be used for data enrichment.
The interlinking of machine-readable datasets
creates the potential for rich reuse of information
contained within these datasets. The increased
availability of open data on the Web provides a large
number of datasets that could be linked to one
another. Connections between datasets can have
multiple uses such as improving data quality by
comparing information from multiple sources,
enriching datasets with information from other
linked datasets and facilitating the development of
data-based applications that use these datasets.
Open datasets available on the Web create new
opportunities for dataset linking and enrichment.
Especially interesting in this context is Linked Open
Data (LOD) that applies the principles of Linked
Data to publishing datasets on the Web and linking
them together, creating a web of interlinked, open
datasets that is also known as the Linked Open Data
cloud (Bizer et al., 2009). A significant portion of
this cloud consists of library-related information
such as the Virtual International Authority File
(VIAF) and Linked Data sources provided by the
Library of Congress (Summers et al., 2008; Hickey
and Toves, 2014).
This paper presents work in progress on
interlinking library data at the National Library of
Latvia (NLL). In particular, it explores how
interlinking of library authority records helps
improving data quality by identifying and correcting
errors, and how library datasets can be enriched
using information from other related data sources.
Dataset linking activity consists of (1) the dataset
analysis phase and (2) the matching phase. This
study focuses on record-level linking of library
datasets but the same approach can be applied to
other types of data.
During the analysis phase, experts examine the
datasets involved and determine which datasets to
link and how to perform record matching. If both
datasets to be linked are not provided in advance
(e.g. an organization aims to link its data to the open
data available on the Web), then the first analysis
task is choosing a dataset to link to. The next task in
the analysis phase is to define the matching function
f(x,y) that indicates if the two records are equivalent.
This task consists of identifying record fields from
both datasets that will be used in the matching
function and the transformations that may be
necessary in order to make the records comparable.
During the dataset matching phase the matching
function is applied to pairs of records from both
datasets. Links are created between pairs of
matching records. These links may be recorded in
one or both datasets (thus enriching the datasets).
Boj¯ars U., Žogla A. and Egl
ite E..
Linking Library Data for Quality Improvement and Data Enrichment.
DOI: 10.5220/0005554901840188
In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 184-188
ISBN: 978-989-758-103-8
2015 SCITEPRESS (Science and Technology Publications, Lda.)
The results of dataset linking consist of two
types: links between records; and errors detected.
Errors may be found during linking or while
reviewing links for false positives and false
negatives. Both the links and the errors may be used
for improving these datasets.
We will distinguish between (a) errors that
impact data linking (i.e. they influence whether or
not the records involved will match) and (b) those
that do not. In the latter case, dataset errors can be
detected by comparing information in related
records from both datasets (e.g. if the fields that
should be the same differ between the datasets). If
one of the datasets can be defined as the
authoritative source then errors can be fixed
automatically by using data from the authoritative
The former case – errors that affect record
linking – is more challenging. In the case of false
negative results the errors prevent us from finding
matching records (i.e. the records that are related
and should be linked together) and taking further
steps for data improvement and enrichment. There
may also be false positive results where records are
mistakenly linked to records from the other dataset
that they are not related to. These false matches may
result in doing "quality improvement" over incorrect
data, leading to further errors. The errors that affect
record matching can be detected by domain experts
reviewing the results of dataset linking (or a subset
of the results) or by comparing the results a golden
standard of known correct links. Consequently, the
matching algorithm may be improved so that it
works around these errors or alerts users about errors
that should be fixed. As a result, dataset linking is an
iterative process where results of the initial runs of
the linking algorithm are used for improving the
next runs and the overall quality of datasets
In this paper we examine the process of linking
the National Library of Latvia subject authority
records where errors in record label fields directly
affect the results of linking. Dataset quality is
improved by (1) attempting to link the datasets, (2)
examining the results for false positives and false
negatives and (3) modifying the matching function
to take into account the errors detected and to
increase the matching precision and recall.
The dataset used in this experiment is a random
sample of NLL's subject authority records consisting
of 1280 entries or ~3.5% of all NLL's subject
authority records. The size of the dataset was chosen
small enough to enable manual validation of record
linkage, results of which are reported in Section 4.
Library authority files
are controlled
vocabularies that provide standard names and
identifiers for different types of entities – people,
organisations, places and concepts (subjects) – that
library catalogues may need to refer to in a unified
way (Hickey and Toves, 2014). Authority records
contain at least a preferred name of the concept and
an identifier, and may also contain alternative labels
(e.g. other spellings) and links to related records.
NLL's subject headings (NLL-SH) – a taxonomy
of topical terms used by libraries in Latvia – was
developed based on the Library of Congress Subject
Headings (LCSH). Most of NLL-SH records were
adapted from LCSH by translating preferred labels
to Latvian while other records, specific to NLL and
Latvia, were introduced without having a matching
LCSH concept (Stūrmane et al., 2014). These two
datasets – LCSH and NLL's subject headings – were
selected to be linked as a part of this experiment
because (1) it could be expected that a majority of
NLL-SH records would be linked to LCSH; (2)
library experts were available that could examine the
results of linking the two datasets in order to identify
false positive and false negative results.
NLL's name authority records were considered
as another candidate for linking but they are already
linked as a part of a large-scale Linked Data project
, which interlinks authority records from
libraries worldwide. Linking in VIAF is done using
both the authority records and bibliographic records
associated with them (Hickey and Toves, 2014).
3.1 Data Formats
The experiment involved linking library authority
data represented in different formats. The LCSH
dataset available online via the Library of Congress
Linked Data Service
is represented in SKOS
(Summers et al., 2008; Miles and Bechhofer, 2009).
It consists of taxonomy concepts that each have a
preferred label (skos:prefLabel) and may have a
number of alternate labels (skos:altLabel). Concepts
may have links to other concepts both inside the
The experiment used a SKOS version of the LCSH data-
set, in N-Triples RDF serialization, published on 27-Oct-
2014. Library of Congress Linked Data service is
available online at
taxonomy and outside it (e.g. links to related
concepts in datasets from other libraries).
The NLL-SH dataset uses the MARC21 format
for authority data
. Similar to LCSH, NLL-SH
records have one preferred label and may have a
number of alternate labels. These records consist of
MARC fields whose type is identified by 3-digit
numbers. In the case of subject heading records the
preferred label is located in MARC field 150 while
alternate labels use field 450. Other fields may
contain additional information such as links to
records that describe broader, narrower or related
taxonomy concepts.
3.2 Matching Algorithm,
Applied to Experimental Datasets
An NLL-SH record that has an equivalent LCSH
record should contain the LCSH record's English
label as one of its alternate labels. This should allow
us to link both datasets (based on English language
alternate labels of NLL-SH records) and to evaluate
the quality of the NLL-SH dataset.
Authority records may either have simple labels
or complex labels consisting of multiple
. The way in which complex labels are
represented differs between the two datasets: NLL-
SH records use separate MARC subfields for the
components of complex labels while the LCSH
SKOS dataset concatenates label components using
a "--" separator.
In order to make records from both datasets
comparable, the matching function converted
complex NLL-SH labels to the same format as used
by the LCSH SKOS dataset.
The matching algorithm iterates through all pairs
of NLL-SH and LCSH records. The matching
function compares all alternate labels (MARC field
450) of a given NLL-SH record to the preferred
label of the LCSH concept and returns True if any of
them match.
This section describes the results of the dataset
The details of MARC21 data formats are beyond the
scope of this paper but we provide some information
that is necessary for understanding MARC record
For example, “History” and “Latvia” are simple labels
while “Latvia--History” is a complex label, combining
both topics.
linking experiment. By using string equality we
were able to link 82.7% of NLL-SH records (1058
out of 1280 records) to matching LCSH records. The
linking algorithm identified matches for both simple
and complex topics. Matching records in LCSH
were identified for 88.3% of simple headings in
NLL-SH (628 of 711 records) and 75.6% of
complex headings (430 of 569 records).
4.1 Analysis of Dataset Linking Errors
The results were examined by library metadata
experts in order to identify false positives and false
negatives. All positively identified matches were
valid (i.e. we did not find any false positives) but
there were 32 false negatives (2.5% of NLL-SH
records in the experimental dataset) that had relevant
LCSH entries but were not matched to them. Table 1
lists the types and the number of linking errors
Table 1: Types of dataset linking errors.
# Error type Errors
Different apostrophe characters used in
2 Shortcomings of matching algorithm 2
Other errors (spelling mistakes, missing
or incorrect MARC fields, etc.)
Total: 32
The most common were errors caused by
differences in the apostrophe symbols used in these
NLL-SH records and matching LCSH records. Since
NLL's dataset records must have the same English
labels as in the LCSH dataset this is considered an
error. This is easy to fix by using the same
apostrophe symbols as in LCSH.
The second type of error is where NLL-SH
records did not contain MARC fields 450 with
English labels because they were identical to the
Latvian labels (MARC field 150). Consequently, the
matching algorithm was improved to include NLL-
SH preferred labels (field 150) in searching for
records to interlink.
The remaining errors were spelling mistakes
(e.g. "ltierature" instead of "literature"), use of
singular instead of plural and other differences
between labels in two datasets, as well as missing or
incorrect MARC fields (e.g. the English label was
added to field 430 instead of 450).
Especially interesting was one record with the
apostrophe error because it also had a semantic error
where a record of different meaning was identified
by the algorithm as a match. The NLL heading for
this record was "Men’s magazines" (“Periodiskie
izdevumi vīriešiem” in Latvian) but its English label
was incorrect and pointed to another LCSH record:
“Women’s periodicals”. Had it not been for the
apostrophe error the matching algorithm would have
missed the more serious error that was detected by
metadata experts when reviewing matching results.
An important task for future study is how to detect
such semantic errors and attempt to correct them.
The next section examines how the results of this
dataset linking experiment can be used for dataset
quality improvement.
4.2 Data Quality Improvement
Once the information about the most common errors
detected is available it can be used to improve the
quality of data. This paper examines how data
quality can be improved by linking datasets to one
another. As discussed in Section 2, data quality
issues can be discovered: (a) by analyzing the errors
found while linking datasets; or (b) by comparing
linked records from both datasets.
In the case of linking NLL-SH and LCSH data
there are no other fields that should be the same
except for English labels used in linking. Therefore
for data quality improvements we concentrated on
fixing the errors that affected the linking process.
The matching algorithm was improved, taking
into account the errors discussed in Section 4.1: (1)
by adding the use of preferred labels of NLL-SH
records to the matching function; and (2) by
introducing fuzzy record matching using the
Damerau–Levenshtein edit distance metric that takes
into account character transposition.
The improved matching algorithm uses fuzzy
matching with edit distance 1 (detecting errors
where labels differ by no more than 1 edit operation)
on all NLL-SH records that were not matched using
string equality. This approach detected most of the
errors identified when linking datasets including
apostrophe errors, extra dots at the end of labels and
other spelling errors.
The second iteration of the matching algorithm
was not aimed at detecting spelling errors that had
edit distance larger than one. It could be modified to
allow for larger edit distances however this is not
advisable because even at distance 1 there were false
positives (e.g. "19th century" instead of "18th
century"; "SETL" instead of "SEAL") that would
end up introducing errors in data if not spotted
during review.
The six remaining errors of type 3 cannot be
detected just by fuzzy matching. Four of these cases
were errors in MARC fields, for example, English
labels not found in field 450 (sometimes misplaced
in other MARC fields). In the remaining two cases:
(a) the record's English label was different from
LCSH; (b) a component of a complex label was not
translated to English.
Fuzzy matching is useful for identifying records
that are similar (e.g., it helped us to find two spelling
errors that were not found by metadata experts when
reviewing the results of the initial matching run).
However, in order to further improve data quality,
errors need to be classified based on how certain we
can be that fixing them leads to a valid match and
not a false positive.
Based on data quality requirements "harmless"
errors (e.g. an extra dot at the end of the label) can
be fixed automatically or suggested to the editor as
likely fixes while more serious cases that may lead
to false positives (see above) need to be handled
with extra care. By examining the false positives we
may identify a set of conditions that can help to
determine which cases need an extra review (e.g. to
warn about fuzzy matches in numbers or
Links between datasets provide an opportunity for
enriching the datasets involved. Data enrichment can
take place at the time of linking or on the fly, when
requesting information from datasets.
Data enrichment is a complex task and details of
how it can be performed depend on the datasets
involved. For example, selected data record fields
may be copied from one dataset to the other,
converting and merging data as necessary. In the
case of taxonomies, such as library authority data,
linked records from both taxonomies may contain
labels in different languages and these records can
be enriched by copying labels across datasets,
facilitating creation of multilingual taxonomies.
Authority data records may contain links, both
internal and external, that can be a valuable resource
for data enrichment. Once a link between NLL-SH
and LCSH records is established, NLL-SH records
can be enriched with links to authority records from
the National Library of France and the German
National Library that are included in the LCSH
dataset. Links from NLL-SH to other open datasets
that link to LCSH, for example, to authority records
from the National Diet Library, Japan, may also be
established. The resulting network of authority data
would be a useful tool for facilitating multilingual
discovery of cultural heritage information.
The fact that a link between records has been
discovered is valuable information by itself and this
linkage may be recorded in one or both datasets.
These external links may later be used for enriching
datasets "on the fly" or for monitoring changes to the
linked dataset. An example of this approach is the service from the National Library of
Spain which uses the already established links to
VIAF in order to enrich their records with links to
the authority records of other national libraries
(Vila-Suero et al., 2013).
Linked Data is a technique for publishing data
on the Web in a way that facilitates object
interlinking and data access "on the fly" (Berners-
Lee, 2006; Bizer et al., 2009). It publishes data so
that data identifiers (URIs) can be dereferenced (i.e.
users can access structured information about these
objects online, by making HTTP requests) and
provides a way for including URIs of linked objects
in the data published.
Information published as Linked Data (e.g.
LCSH dataset used in the experiment) is well-suited
for data enrichment: (1) data is published on the
Web, making it possible for users to find it, reuse it
and link to it; (2) the Linked Data model makes it
easy to enrich records with new information; and (3)
these records have web-accessible URI identifiers
for accessing up-to-date information about them.
The National Library of Latvia is in the process
of publishing NLL's authority data as Linked Data.
Once this dataset is published it will enable the
benefits listed above such as the opportunity for
other users to explore and link to NLL's authority
data. The data published by NLL's linked data
service will be enriched with additional information
including Linked Data from other data sources.
Dataset interlinking creates new opportunities for
data quality improvement and data enrichment.
This paper discussed principles for dataset
linking and improvement, and presented results of
an experiment for linking and enriching library
authority data.
The experiment was conducted using the
National Library of Latvia authority file and Linked
Data from the Library of Congress. The experiment
helped us identify and fix data quality issues in the
NLL-SH dataset, and to enrich it using information
from matching LCSH records. Links between
taxonomy records from the two datasets may be
used for multilingual discovery of bibliographic
Datasets that are published as Linked Data are
especially useful for data enrichment as their records
are available "on the fly" and may include links to
other related datasets. The National Library of
Latvia is in the process of publishing its authority
file as Linked Data, making it possible for user
worldwide to reuse it and to interlink it with other
This research is a part of the project "Competence
Centre of Information and Communication
Technologies" by IT Competence Centre, contract
L-KC-11-0003, co-financed by European Regional
Development Fund, Research No. 1.18 “Data array
quality analysis and enhancement technologies”.
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