Entity Resolution in Large Patent Databases: An Optimization Approach
Emiel Caron and Ekaterini Ioannou
Department of Management, Tilburg University, Tilburg, The Netherlands
Entity Resolution, Data Disambiguation, Data Cleaning, Data Integration, Bibliographic Databases.
Entity resolution in databases focuses on detecting and merging entities that refer to the same real-world
object. Collective resolution is among the most prominent mechanisms suggested to address this challenge
since the resolution decisions are not made independently, but are based on the available relationships within
the data. In this paper, we introduce a novel resolution approach that combines the essence of collective
resolution with rules and transformations among entity attributes and values. We illustrate how the approach’s
parameters are optimized based on a global optimization algorithm, i.e., simulated annealing, and explain how
this optimization is performed using a small training set. The quality of the approach is verified through an
extensive experimental evaluation with 40M real-world scientific entities from the Patstat database.
Entity Resolution (ER) is a fundamental task for data
integration, cleaning, and search. It aims at detecting
entities, also referred to as instances, profiles, descrip-
tions, or references, that provide information related
to the same real-world objects. Such entities are then
merged together. For example, we can merge entities
together that provide information about a particular
real-world event, a location, an organization, or a per-
This paper focuses on the resolution of large col-
lections of structured data. The processing combines
the essence of collective resolution with rules among
entities attributes and values. Thus, instead of just fol-
lowing relations among entities as done by traditional
collective resolution methods, use the rules related to
the particular entities. To the best of our knowledge,
this is the first technique that investigates such a com-
bination. The method also illustrates that it is possible
to optimize the overall resolution performance by in-
corporating a global optimization algorithm by using
simulated annealing.
The introduced method starts by pre-cleaning the
entities and extracting values. Next, it constructs rules
based on these values and make use of the tf-idf algo-
rithm to compute string similarities. It then creates
clusters of entities by means of a rule-based scoring
system. Finally, it perform precision-recall analysis
using a golden set of clusters and optimizes the pa-
rameters of the algorithm.
The contributions of this paper are outlined as fol-
We advocate a novel generic approach that in-
vestigates collectivity for resolution using rules
among attributes and values. The approach is ca-
pable to operate over collections of very large vol-
The approach’s parameters are optimized based
on a global optimization algorithm and using a
tiny percentage of the collection instances for the
training part.
We evaluate quality using real-world scientific
references, i.e., Patstat database with 40M in-
stances and a high number of entities describing
the same entity.
The remainder of this paper is structured as follows.
In Section 2, we briefly discuss related work. Section
3 defines the problem and introduces our method for
entity resolution as well as the parameter optimiza-
tion approach. Section 4 presents and analyzes the
results of the experimental evaluation. Section 5 pro-
vides conclusions and discusses future directions.
During the last decades a plethora of ER techniques
have been proposed. Each of these techniques intro-
duce mechanisms to handle particular data challenges
and/or environment characteristics. As discussed in
Caron, E. and Ioannou, E.
Entity Resolution in Large Patent Databases: An Optimization Approach.
DOI: 10.5220/0010527501480156
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 148-156
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
recent surveys Papadakis et al. (2021); Dong and Sri-
vastava (2015), the primary focus of the introduced
techniques was on collections with an increasing data
volume using pay-as-you-go mechanisms, e.g., Pa-
penbrock et al. (2015); Wang et al. (2016), or on col-
lections with unstructured data with high levels of het-
erogeneity, e.g., Papadakis et al. (2011, 2013).
Although the need for handling huge collections
of data with high levels of heterogeneity is clear, a
large portion of current applications are still using
structured data with relatively small amounts of up-
dates. Scientific collections are the most common ex-
ample used in a plethora of research publications, i.e.,
data describing publications and authors. The primary
ER challenges in such collections are related to dupli-
cates and noise present in the values of the instances.
The typical setting is having a single collection, usu-
ally of a very large size, in which a number of enti-
ties corresponds to the same real-world object. For
example, the Worldwide Patent Statistical Database
(European Patent Office, 2019) contains 40M entities
and has a large number of entities describing the same
real-world object (i.e., around 80).
One mechanism found successful for ER focused
on rules among the entities attributes and/or values,
also known as mappings, transformations, and corre-
spondences Yan et al. (2001); Tejada et al. (2002). For
example, Active Atlas Tejada et al. (2002) starts with
a collection of generic transformations (e.g., abbrevi-
ation for transforming ”3rd” to ”third”, acronym for
transforming ”United Kingdom” to ”UK”) and learns
the weight of each transformation given a particular
application domain.
Another mechanism that was proven to be very
successful is collective resolution Rastogi et al.
(2011); Ioannou et al. (2008); Dong et al. (2005);
Kalashnikov et al. (2005). The idea here is to lever-
age the global information (i.e., collective) in order
to detect pairs with low matching similarity (likeli-
hood) and infer indirect matching relations through
relationships detected during the processing. For col-
lections with scientific data this typically means prop-
agating information between detected pairs of authors
and publications to accumulate additional evidences,
i.e., positive and negative.
In supervised ER approaches, a data set with
labelled records is used to train the learning scheme.
However, large data sets with manually labelled
records would be expensive to collect and are often
not available. For this reason, research has focused
on developing unsupervised approaches for ER.
Unsupervised approaches use similarity metrics and
clustering algorithms to find clusters of name vari-
ants. While unsupervised approaches do not require
training data sets, they often perform less well than
supervised approaches (Levin et al., 2012). In this
paper, we therefore adopt a hybrid approach, where
we use an unsupervised approach for clustering and
improve the model result’s on limited training set,
reflecting a form of weak supervision.
We now introduce our approach. We first provide
the formal description of the problem (Section 3.1),
then discuss the optimization of the parameters (Sec-
tion 3.2), and finally describe the resolution algorithm
(Section 3.3).
3.1 Problem Definition & Notation
Our optimization problem for ER is expressed in the
following notation. Consider N entities, or represen-
tations of entities, r
, ..., r
, where each entity r
features. For i = 1, ..., N, the entity r
has feature vec-
tor f
consisting of M features f
= ( f
(1), ..., f
Every feature f
( j) has a domain F
, i.e. f
, ..., f
(M) F
which is independent of i.
We label two entities similar if their correspond-
ing feature vectors are similar. Stated more precisely,
we define
: F
× F
{0}, (1)
such that σ
( f
(l), f
(l)) measures the similarity of
any pair ( f
(l), f
(l)) F
× F
, where i 6= j, and we
define a distance function
d( f
(l), f
(l)) =
( f
(l), f
The degree to which r
and r
are different is presented
in the vector S:
, r
) = (d( f
, f
), d( f
, f
), ..., d( f
, f
To obtain a similarity score as a single number, a
NxN matrix S is defined with elements s
i j
and weight
vector w = (w
, ..., w
i j
· d( f
(k), f
where w
0, i 6= j, and
= 1.
Entities that are similar, are grouped into sets, or clus-
ters, in the following way. Suppose we have Q sets
Entity Resolution in Large Patent Databases: An Optimization Approach
, Σ
, . . . , Σ
, and have defined an initial threshold δ,
= {r
, . . . , r
} (3)
, r
implies d
i j
δ, i 6= j (4)
= 1. The sets Σ
, . . . , Σ
are not necessar-
ily disjoint but are chosen to be maximal, i.e., we only
consider solution of (3) and (4) that have the follow-
ing property:
, r) δ = r
Σ. (5)
If we define a undirected graph with vertices r
and r
an edge between r
and r
, and if d(r
, r
) δ then the
sets Σ
, . . . , Σ
, satisfying (3), (4), and (5) are deter-
mined with algorithms that compute the graph’s con-
nected components (Bondy and Murty, 1976) or max-
imal cliques (Bron and Kerbosch, 1973). Note that
the solution depends on w and δ and is not unique but
depends on the order of merging, i.e. selection of r
The weights w and δ are chosen such that there is an
optimal match with a test ‘golden sample’ H of sets
, . . . ,
. The golden sample H is a test sample
with verified sets of entities.
The method’s performance is evaluated using pre-
cision and recall analysis. The F1-score is the har-
monic mean of precision and recall (Fawcett, 2006).
Since the objective is to obtain sets with both high
precision and high recall, we maximize the F1-score.
Therefore we select the parameters w = (w
, ..., w
and δ in such a way that
, δ
= argmax
L(w, δ) (6)
where our objective function L(w, δ) is the total aver-
age F1-score of the optimal match of H with L. Opti-
mizing the method’s parameters is done using a simu-
lated annealing algorithm (Xiang et al., 1997), which
is discussed next.
3.2 Optimization
In Eq. (6) the objective function is nonlinear and
yields many local optima. The number of local op-
tima typically increases exponentially as the number
of variables increases (Erber and Hockney (1995)),
here represented by w, δ. For this reason, the opti-
mization problem cannot be solved straightforwardly
with linear programming, and a global optimization
method is needed such as, the simulated annealing al-
gorithm, in order to find a global optimum, instead of
getting trapped in one of the many local optima that
might appear.
Maximizing the F1-score with respect to w, δ in
Eq. (6) is a combinatorial optimization problem. Re-
search in combinatorial optimization focuses on de-
veloping efficient techniques to minimize or maxi-
mize a function of many independent variables. Since
solving such optimization problems exactly would re-
quire a large amount of computational power, heuris-
tic methods are typically used to approximate optimal
solution. Heuristic methods are typically based on a
iterative improvement strategy. That is, the system
starts in a known configuration of the variables. Then
some rearrangement operation is applied until a con-
figuration is found that yields a better value of the ob-
jective function. This configuration then becomes the
new configuration of the system and this process is re-
peated until no further improvements are found. Since
this method only accepts new configurations that im-
prove the objective function, the system is likely to
be trapped in a local optima. This is where simulated
annealing plays its part in the method.
The simulated annealing algorithm is inspired by
techniques of statistical mechanics which describe the
behavior of physical systems with many degrees of
freedom. The simulated annealing process starts by
optimizing the system at a high temperature such that
rearrangements of parameters causing large changes
in the objective function are made. The “tempera-
ture”, or in general the control parameter, is then low-
ered in slow stages until the system freezes and no
more changes occur. This cooling process ensures
that smaller changes in the objective functions are
made at lower temperatures. The probability of ac-
cepting a configuration that leads to a worse solu-
tion is lowered as the temperature decreases (Kirk-
patrick et al., 1983). To optimize Eq. (6) the
dual annealing() function of the SciPy library in
Python (SciPy.org, 2021) is used. This implemen-
tation is derived from the research of Xiang et al.
3.3 Resolution Algorithm
In this section the optimization problem is captured in
a practical general method for ER. This method is in-
spired by (Caron and Eck, 2014; Caron and Daniels,
2016). An overview of the method and its main steps
is presented in Figure 1. The method’s inputs are N
entities with features f
, i.e. the raw data related to an
ER-problem, and a golden sample H. The method’s
output is a set of parameters w
, δ
that produce opti-
mized sets Σ
, ..., Σ
, that represent clusters of name
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 1: Graphical overview of the ER method.
The method consists of five generic steps:
1. Pre-processing;
2. Filtering;
3. Rule-based scoring and Clustering;
4. Post-processing;
5. Optimization.
In the method first steps 1 4 are executed in succes-
sion on the sample data, after that equation (6) is op-
timized in step 5. Therefore, steps 3 5 are executed
in iteration until the total F1-score is maximized. Af-
ter the final iteration the parameters w
, δ
are ob-
tained. With these parameters the steps 1 4 are ap-
plied again on the whole data set to disambiguate all
records. This final part is typically executed offline.
In the remainder of this section we describe the steps
in a concise way to give overview of general tasks
within each step. Typically, these tasks need to be
configured for the practical ER-problem at hand, e.g.
company name disambiguation, (author) name reso-
lution, or the cleaning of scientific references, and so
(1) Pre-processing. The method starts by pre-
processing the data. During this step the entities N
in raw data are pre-cleaned and harmonized to re-
duce basic variability in the data on the record level.
In addition, the set of features M is completed, with
additional descriptive labels by using techniques like
regular expressions combined with match and replace
actions. Finally, the feature set is evaluated for cor-
(2) Filtering. The feature set M is the input for
this step. Here rules are based on (combinations of)
the features of the entities, and often combined with
string similarity measures, e.g. the tf-idf algorithm
(Salton and Buckley, 1988),to start finding similar
pairs of entities. With the rule set, i.e. the input for the
distance function, similarities are computed between
entities using Eq. (1). By constructing rules based on
the features, ‘proof’ is collected for the similarity be-
tween records and based on that candidate entity pairs
are created. The output of this step is a pool of can-
didate pairs for further evaluation, where pairs that do
not match are filtered out.
(3) Rule-based scoring and clustering. In this
step, for the set of candidate pairs, initial weights w
are assigned to the rules based on ’the strength of each
rule’, to determine Eq. (2). Typically, the strength of
rules are first based on domain knowledge, after that
the weights are optimized in iteration in step 5. Fur-
thermore, the total score, i.e. the combined weights,
for every pair is stored in a dataframe and compared
with an initial threshold δ
to obtain the sets in Eq.
(3). The clusters of name variants are now deter-
mined with the connected-components or the maxi-
mal cliques algorithm.
(4) Post-processing. In this step, entities for which
no duplicates are identified in the previous step are
assigned to new single-record clusters.
(5) Optimization. In this step, the sets are eval-
uated on the golden sample H using precision and
recall analysis. The parameters w, δ are adjusted to
achieve higher values in precision and recall. Using
simulated annealing (Xiang et al., 1997) we obtain the
optimal parameters w
, δ
. Here the average F1-score
over all sets is used, i.e. the harmonic mean of the pre-
cision and recall, as the objective function (Fawcett,
2006) defined in Eq. (6).
We now present the results of our experimental eval-
uation. The focus was on investigating the quality
of return results as well as the effects of the intro-
duced parameter optimization. The following para-
graphs present the evaluation settings (Section 4.1),
also illustrating the challenges of entity resolution of
the particular data collection. After that, the results of
the evaluation are analyzed (Section 4.2), followed by
the details of the implementation (Section 4.3).
4.1 Setting
For the experimental evaluation, we use the World-
wide Patent Statistical Database (Patstat) (European
Patent Office, 2019). This is a product of the Euro-
pean Patent Office designed to assist in statistical re-
search into patent information. One of the available
tables, namely TLS214, holds information on scien-
tific references that are cited by patents. These refer-
ences are collected from patent applications, in which
Entity Resolution in Large Patent Databases: An Optimization Approach
Figure 2: A sample, i.e., 20 of 80 records, matching the exact title “A relational model of data for large shared data banks”.
patent applicants reference scientific papers and pro-
ceedings to acknowledge the contribution of other
writers and researchers to their work.
In the 2019 Spring Edition of Patstat, the TLS214
table contained a bit more than 40 million records
with scientific references. The particular table is an
important point of reference for researchers that wish
to study the connection between science and technol-
ogy. The main issue of this data, in addition to the col-
lection size, is that amongst the scientific references
there are many name variants of publications caused
by missing data, inconsistent input convention, differ-
ent order of items, typos, etc. These variants make the
usage and analysis of the data very difficult.
To illustrate the problem with the TLS214 table,
we posed a query search for an exact title of an ar-
ticle. The query returned 80 records, however, there
may be more records referring the same real-world
object. Figure 2 illustrates the first 20 results (i.e., en-
tities) of this query. Although every record refers to
the same real-world object they are stored in different
ways or simply duplicated. For example, in entity 11,
Codd’s initials are missing, while record 7 holds an
abbreviation of the word “Communications”. Besides
textual differences the database treats every record as
a unique entity due to the primary keys. These prob-
lems make it difficult to properly retrieve information.
In order for table TLS214 to be a reliable point of
reference for research, its records need to be disam-
Table 1: Total number of entities (i.e., records) per golden
sample GS1 and GS2.
Journal papers 17,348 11,163
Proceedings 0 1,093
Total 17,348 12,256
4.2 Result Analysis
We now discuss the experiments and results. Our op-
timization method is executed over the TLS214 table
with the 40M entities, i.e., entities. Our goal is to in-
vestigate quality and for this we used two golden sam-
ples that contain the expected matches among the en-
tities. The golden sample 1 (GS1) contains 100 clus-
ters (with 17,348 records), which refer to 100 highly
cited scientific papers, based on a top 100
. In ad-
dition, we used golden sample 2 (GS2) with in to-
tal 12,256 records, contains references to 50 unique
journal publications and 50 unique conference pro-
ceedings. Both samples are evaluated by human do-
main experts in order to incorporate the expected en-
tity matches (else referred to as clusters). Typically,
the patterns for proceedings are more difficult to clean
because they often show more variation. As can be
seen in Table 1, references to journal publications oc-
cur more often than conference proceedings in Patstat.
Figure 3 gives the distribution of the number of
publication name variants per cluster in GS1. Notice
that the largest clusters contain more than 1,500 name
We first focused on the iterative optimization part,
i.e., Step 5 of the method (Section 3.3). The method
is executed on a basic laptop with the intermediate
results stored and compared against the ground sam-
ples. As expected, the method gradually improves the
overall F1-score until this improvement is stabilized.
The latter occurs after approximately 100 hours. At
that point the simulated annealing algorithm stops,
and the final set of parameters w
, δ
is obtained, and
clusters of name variants are derived that maximize
Equation 6.
Details for the top 100 highly cited scientific papers can
be found here:
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 3: Distribution of the number of publication name
variants per cluster in GS1, ordered descendingly.
Figure 4: Simulated annealing increasing the F1-score over
Figure 4 shows in a plot the increase of the total
average F1-score against the time in hours. The first
point of the line is the F1-score of the initial clusters.
This plot shows that the simulated annealing algo-
rithm indeed is able to improve the F1-score and thus
our method produces better clusters compared to the
initial ones. It can be seen that the algorithm makes
large improvements to the F1-score in the beginning
and smaller improvements towards the end. This is
in line with the theory on simulated annealing Xiang
et al. (1997). The F1-score over all clusters increases
from 0.68006 to 0.79304 (16.5%).
Table 2 shows precision-recall-F1 analysis of the
final clusters for both GS1 and GS2.
Table 2: Statistics of optimized clusters for GS1 and GS2.
Precision 0.99997 0.99361
Recall 0.92857 0.92154
F1-score 0.96295 0.95622
The table shows high average values for all per-
formance statistics, all figures are above 0.90. More-
over, it can be observed that the average precision is
slightly higher than the average recall. Obviously, the
clusters obtained after optimization obtain higher pre-
cision and recall values than the initial clusters before
optimization, as shown in increase of the F1-score in
Figure 4.
The plots in Figure 5 give a further break-down of
the improvement of precision-recall-F1 statistics on
the cluster level with the optimized parameters, com-
pared to initial parameters. The best clusters return
Figure 5: Distribution of the precision-recall-F1 scores of
the clusters before and after optimization for GS1 and GS2.
an average precision and recall close to 100% (Ta-
ble 2). The best cluster is defined as the cluster with
the highest value for the F1 measure. The increase
in recall indicates that the new parameters are better
in creating clusters. In combination with a decrease
in clusters we notice that both golden samples have
larger dominant clusters after optimizing. If the three
best clusters of every entity are grouped in one clus-
Entity Resolution in Large Patent Databases: An Optimization Approach
ter the average recall of GS1 increases from 0.92857
to 0.95084 and for GS2 from 0.92154 to 0.96397. In
addition, when analyzing the clusters, we notice that
our method is slightly ‘conservative’, it values pre-
cision over recall. As a result the method is more
likely to create multiple clusters with high precision
than to create one large cluster with potential errors.
Typically, the method splits the name variants of one
golden cluster into one large dominant cluster and
multiple small clusters. The improvement in the re-
call in Figure 5 illustrates the difference in dominant
cluster size. Based on the evaluation we conclude that
with the optimized parameters the rule-based scoring
system finds more evidence to cluster publications to-
The slightly lower recall results in Table 2 for
GS2, might origin from the format of conference pro-
ceedings. Table 3 illustrates the problem with the
format of a conference reference, it shows shows an
example cluster of correctly classified scientific ref-
erences. Although every reference contains the pri-
mary article information (author, title, and year) each
record follows a different format. In some cases, the
institute name is left out, the conference abbreviated,
or contains additional information (e.g. conference
date or subtitle). Due to the additional information
within references to conference proceedings the dis-
ambiguation method is likely to produce some erro-
neous results. As a result, the rule based scoring sys-
tem does not always find sufficient evidence to cluster
all the name variants into one cluster.
Table 3: Different formats for conference proceedings
within one cluster.
Cl. Reference
51 D. Cohen et al., IP Addressing and Routing in a Local Wireless Network,
IEEE Infocom 92: Conference on Computer Communications, vol. 2,
New York (US), pp. 626 632
51 Daniel Cohen, Jonathan B. Postel, and Raphael Rom, Addressing and
Routing in a Local Wireless Network, IEEE INFOCOM 1992, p. 5A.3.1-7
51 Cohen et al., ’IP addressing and routing in a local wireless network’
One World Through Comminications. Florence, May 4-8, 1992, Proceedings
of the conference on Computer Communications (INFOCOM), New York,
IEEE, US, vol. 2, Conf. 11, May 4, 1992, pp. 626632m XP010062192,
ISBN: 0/7803-0602-3
51 Danny Cohen et al.; ’IP Addressing and Routing in a Local Wireless
Network’; One World Through Communications. Florence, May 4-8, 1992,
Proceedings of the Conference in Computer Communications (Infocom),
New York IEEE, US, vol. 2 Cof. 11, May 4, 1992
51 IP Addressing & Routing in a Local Wireless Network, Cohen et al,
IEEE 92, pp. 626 632
. . . . . .
4.3 Implementation
The resolution algorithm is implemented in the
Python programming language. The code
is struc-
tured in the following parts:
entity resolution optimization code.7z
Pre-processing and filtering:
connection.py and rules.py;
Rule-based scoring-clustering:
rule construction.py,
string matching using tfidf.py,
clustering.py, and evaluation.py;
find clusters.py and optimize.py.
In the example case of the Patstat table with am-
biguous scientific references, the entities r
, . . . , r
are found in the records of the table. The features of
the entities are extracted bibliographic meta informa-
tion, such as: publication title and year, author names,
various journal information, and so on. Eq. 1 of the
method refers to the rules that are developed. These
rules provide evidence that two records are similar. In
order to compute the string similarities for the rules
we use an efficient implementation of tf-idf in Python.
The scores that are assigned to the rules correspond to
weight vector w in Eq. 2. The clustering of records
is done using the connected components algorithm
and results in the sets Σ
, . . . , Σ
as described in the
method. Specifically, the method find clusters()
from the script find clusters.py, implements the
ER method and computes the total F1-score. Its input
parameters are a configuration of variables (w, δ) and
a table containing feature vectors. We use this method
as our objective function for the dual annealing()
method described in the script optimize.py. In this
way we obtain the optimal configuration of the pa-
rameters in Eq. 6.
This paper explores a novel approach for performing
ER over large collections of data using a combination
of collective resolution with rules between entity at-
tributes and values. The approach uses simulated an-
nealing algorithm to optimize the related parameters.
As illustrated by the evaluation on the cleaning of sci-
entific references in the Patstat database, the intro-
duced optimization ER approach achieves high effec-
tiveness without requiring a large training set, resem-
bling approaches in weak supervised learning. To op-
timize the method’s parameters, the overall F1-score,
that is captured in the non-linear objective function, is
maximized over a limited golden set of clusters. After
the optimization, the obtained parameters are used to
disambiguate the whole data set.
In the case study, the method is applied to the
cleaning of scientific references, e.g. journal publica-
tions and proceedings, and create sets of records that
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
point to the same bibliographic entity. The method
begins by pre-cleaning the records and extracting bib-
liographic labels. Subsequently, rules are developed
based on the labels combined with string similarity
measures, and clusters are created by a rule-based
scoring system. Lastly, precision-recall analysis is
performed using a golden set of clusters, to optimize
the rule weights and thresholds. The results demon-
strate that it is feasible to optimize the overall F1-
score of disambiguation method using a global opti-
mization algorithm, and obtain the best parameters to
disambiguate the whole database of scientific refer-
ence. By changing the rules, the method can directly
be applied on on similar ER-problems. Therefore, the
method has a generic perspective.
In future research, several directions might be ex-
plored to obtain the optimal configuration for the
method. Firstly, our method can be analyzed on other
datasets for ER, to study whether the results are sta-
ble and to compare the evaluations. Secondly, this
work revealed additional challenges worth investigat-
ing with respect to the incorporated rules. A possi-
ble future direction is to check the algorithm’s be-
haviour when increasing the number of used rules,
and another direction is moving towards rules that can
evolve over time. Thirdly, more information is nec-
essary about the best clustering algorithm applied to
merge similar name variants, e.g. an in-depth com-
parison between the connected components and max-
clique algorithm. Fourthly, alternative optimization
techniques might be used that produce similar or even
better results in terms of efficiency and/or effective-
ness. A comparison between simulated annealing,
Tabu search, and a genetic algorithm is therefore en-
We kindly acknowledge Wen Xin Lin, Colin de
Ruiter, Mark Nijland, and Prof. Dr. H.A.M. Daniels
for their contributions to this work.
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