=Paper=
{{Paper
|id=Vol-2548/paper-11
|storemode=property
|title=Methods and Techniques for Data Quality Improvement of (Linked) (Open) Data
|pdfUrl=https://ceur-ws.org/Vol-2548/paper-11.pdf
|volume=Vol-2548
|authors=Maria Angela Pellegrino
}}
==Methods and Techniques for Data Quality Improvement of (Linked) (Open) Data==
https://ceur-ws.org/Vol-2548/paper-11.pdf
Methods and Techniques
for Data Quality Improvement
of (Linked) (Open) Data
Maria Angela Pellegrino
Dipartimento di Informatica, Universitá degli Studi di Salerno, Italy
mapellegrino@unisa.it
Abstract. Good decisions need good data. Hence, only by exploiting
good data it is possible to make effective decisions. The goodness of
data is usually related to the task they will be used for. However, it is
possible to identify some task-independent quality dimensions which are
merely related to the data themselves. In order to improve the intrin-
sic data quality, we propose a proactive approach. Our goal is to offer
data providers (and consumers) a set of methods and techniques to guide
them in assessing and improving the quality of data they are interested
in. We mainly focus on Linked (Open) Data. Since the published data
might also contain personal data, there is the need to make the data
set compliant with the General Data Protection Regulation (GDPR).
Therefore, besides quality problems, we are also interested in discover-
ing any privacy breach and - if needed - in proposing corrective actions.
The final goal is to give data providers the possibility of publishing bet-
ter data. The proposed approach is pragmatic. Thus, we will not only
design but also implement it. We plan to wrap it into a social platform,
already used by several public administrations, which enable us to test
the applicability of the proposed methods in real settings.
Keywords: Data quality, Privacy breaches, Quality assessment, Quality
improvement, Privacy awareness, Data publication
1 Problem statement
Data Quality (DQ) can be defined as “as the level of compliance of the data with
the purpose they will be used for ” [1]. Thus, data quality is defined in terms of
fitness of use. The exploitation of data not ready-for-use may lead to incorrect
conclusions and poor decisions. Only by using high-quality data it is possible to
achieve effective decision making. Thus, data providers should make an effort to
improve the quality of data sets under definition to simplify their exploitation.
Moreover, data providers might also deal with data sets containing personal data.
The General Data Protection Regulation (GDPR) [22] defines which data are
considered personal and in which case the individual privacy can be compromised
in order to increase the utility for the community. Therefore, data providers have
to make data sets compliant with the GDPR before the publication.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Maria Angela Pellegrino
The problem we want to face is how to design a unified approach which allows
to publish high-quality data while preserving individual privacy. Since we are
interested in both Quality and Privacy, we call our approach Qualicy aware.
The general workflow to assess and improve quality/privacy aspects should be:
1) choose the quality dimensions of interest, 2a) assess the quality and 2b) de-
tect privacy problems, 3a) improve the overall data set quality and 3b) prevent
privacy breaches. By a reactive approach, the quality can be improved after the
data set publication, for instance when it has to be used in a practical use case.
The reactive philosophy can be summarised by “publish first, refine later ”. The
alternative is to adopt a proactive approach by improving the data set quality
as early as possible. Ideally, it might be improved during the publishing phase.
Our proposal is to provide data publishers a set of (semi-)automatic techniques
to identify quality problems and improve the overall quality of a data set before
its publication. Moreover, we aim to take privacy concerns into account and pre-
vent personal information leakage. Our pragmatical approach will be integrated
into SPOD (Social Platform for Open Data) which can be used by citizens, Pub-
lic Administrations (PAs), associations, and every kind of stakeholder in order
to produce and consume Open Data (OD) also in Linked format. Our approach
must not require technical skills to be compliant with the SPOD audience. Since
data can be both in tabular and linked format, we plan to work with data in
general and try to define strategies independent of data format. Only when it is
necessary we intend to use peculiarities of the specific data format.
In conclusion, we can summarise our goal as the definition of strategies to assess
and improve data quality and manage privacy aspects of (Linked) (Open) Data.
The parentheses delimit the parts which can be omitted. In other words, we plan
to work i) with Data in general, ii) with Open Data in tabular format (3-star
data according to Tim Berners-Lee’s rating system [3]), iii) with Linked Data,
iv) also released with the open license as 5-star data [3], i.e. Linked Open Data.
1.1 Data Quality
Several quality dimensions and taxonomies have been defined to evaluate data
quality. Ballou and Pazer [2] identify accuracy, completeness, consistency, and
timeliness as main quality dimensions. Wand et al. [29] classified quality dimen-
sions in intrinsic, accessibility, contextual, and representational DQ: data should
be i) intrinsically of a good qualitative level and ii) accessible; iii) they should
be compliant with the context they will be used for and iv) also the format itself
should be qualitatively good. Besides these general definitions, data quality di-
mensions can been specialised for the Linked (Open) Data (LOD). According to
Zaveri et al. [30], accuracy and completeness belong to the intrinsic data quality.
They further distinguish syntactic and semantic accuracy.
Syntactic accuracy. A value is syntactically accurate when it is valid, i.e. it
belongs to the set of acceptable values according to the domain of interest [12].
Therefore, the syntactic accuracy (also called syntactic validity) is the degree of
Data Quality Improvement of (Linked) (Open) Data 3
conformity to the syntactic rules determined by the modelled domain.
The metrics identified for the syntactic validity are
– detecting the explicit definition of the allowed values for a certain data type,
– detecting the compliance of values with syntactic rules (e.g. patterns),
– detecting the presence of outliers,
– detection of typos in literals.
Semantic accuracy. According to Zaveri et al. [30], the semantic accuracy is
defined as the degree to which data values correctly represent real-world facts.
For instance, supposing that the flight between Paris and New York is A123,
while in a data set the same flight instance is represented as A231. In this case,
the instance is semantically inaccurate since the flight ID does not represent its
real-world state [30]. The metrics identified for semantic accuracy are:
– detection of outliers by using distance-based methods,
– detection of inaccurate values comparing values of different properties,
– detection of inaccurate classifications and labelling.
Completeness. Fürber et al. [12] classified completeness into i) schema complete-
ness, ii) column completeness, iii) population completeness, and iv) interlinking
completeness. The Schema completeness is the degree of completeness of the on-
tology, i.e. there are no relevant classes and properties not represented in the
ontology. The column completeness can be defined as the number of missing val-
ues for a specific property/column. The population completeness is the percentage
of the coverage of all the real-world objects of a particular type represented in the
data sets. The interlinking completeness (specific for LOD) refers to the degree
to which the instances contained in the data set are interlinked.
2 Relevancy
By providing methods to publish high-quality data, the effort and the time
needed to make data ready-for-use will be reduced. Since we want to improve
the (Linked) (Open) Data quality, the problem is relevant for all data publishers,
contributors, and consumers. Moreover, the proposed approach will be wrapped
into SPOD which is already adopted by several users, such as our national Public
Administration and cultural associations. Therefore, on one side they can benefit
from our results; on the other side, they can also be involved in the evaluation
phase of our approach in order to assess its applicability in real settings.
3 Related work
Linked (Open) Data quality assessment. SWIQA [12] is a quality assess-
ment framework which relies solely on Semantic Web technologies, without any
external source. As our proposed approach, SWIQA may be used both by data
4 Maria Angela Pellegrino
consumers to find high-quality data sources and by data owners to evaluate the
quality of their own data. They selected quality dimensions which rely only on
the data source, without caring about the specific task they will be used for.
Thus, they aim to provide an objective - i.e. task independent - quality assess-
ment. If we limit ourselves to the intrinsic DQ, they cover syntactic and semantic
accuracy, completeness, timeliness, and uniqueness. In general, they consider a
wider range of metrics. They evaluate the metrics based on the Closed Worlds
Assumption (CWA), i.e. everything that is not known can be assumed as false.
This hypothesis is due to the metric definitions. However, typically the Semantic
Web assumes an open world, i.e. everything we do not know is not defined yet.
The CWA might be not always applicable since LOD suffer from incompleteness.
Sieve [19] is based on the opposite assumption: it considers data quality strictly
dependent on the task. Therefore, the user can customise the settings by spec-
ifying metrics, scoring functions, and aggregation functions in an XML file. It
evaluates both the semantic accuracy and the completeness of the queried LOD.
About how to assess data quality and display results, Langer et al. [15] report a
clear workflow to evaluate a set of metrics and report results. Looking at the pro-
vided results, users can also change quality desiderata. It implies a cyclic process
in order to define/evaluate/refine quality metrics. This theoretical workflow is
implemented into SemQuire [15] which is focused on quality assessment. SHACL
Shapes Constraint Language1 is a W3C standard to validate LOD against a set
of conditions. It is useful for different purposes, e.g. data integration.
Other interesting works can be found in a survey written by Zaveri et al. [30].
Cited work focus only on data quality assessment without considering the im-
provement step. Moreover, they do not provide a privacy-aware process.
Linked (Open) Data quality improvement. In his survey, Hadhiatma [13]
underlines the need for a framework which helps in improving the LOD data
quality. They count several approaches which exploit inductive learning methods
in order to enrich and complete LOD. Among them, Paulheim [23] defined an
algorithm able to detect co-occurrences and patterns in DBpedia types. Sleeman
and Finin [26] worked on a labelled training set to predict the type of instances.
In general, machine learning, statistical methods, and external knowledge are the
mainly employed methods to detect patterns and find missing information [13].
DaCura [8] is a framework developed to help data set curators. Because their
users may not have technical skills, we share the same audience. The framework
is made up of a collection of tools able to detect and curate quality problems
over the evaluation of linked data sets. Therefore, it is used both to assess and to
improve the data set quality. DaCura and our proposed approach share the idea
that the quality should already be affected in the definition stage. Moreover, the
process has to be cyclic. If we consider only the intrinsic DQ, Freeney et al. [8]
address both the accuracy and the completeness quality metrics. In general,
they consider a wider set of metrics, including several metrics which we are not
1
https://www.w3.org/TR/shacl/
Data Quality Improvement of (Linked) (Open) Data 5
considering at the moment. On the other side, our goal is to consider both the
quality and privacy awareness - which is completely absent in DaCura.
Privacy awareness in LOD. A typical content-based data leakage preven-
tion system (DLPS) works by monitoring sensitive data mainly by using regular
expressions, data fingerprinting and statistical analysis. Regular expressions are
normally used under a certain rule such as detecting social security numbers and
credit card numbers. Dataguise, a leader in data privacy protection and com-
pliance, will demonstrate how DgSECURE is supporting enterprise administra-
tors as the basis for secure data analytics, application testing and development,
and the general protection of sensitive data across enterprise cloud reposito-
ries. DgSECURE [6] enables you to discover, count, and report on sensitive data
assets through a sophisticated regular expression (regex) pattern builder; it com-
bines structured, semi-structured, or unstructured content and it finds sensitive
data - such as credit card numbers, SSN, names, email addresses. For what con-
cerns the anonymization, it is well-consolidated approach [16,18,28] in relational
data. However, its counterpart on LOD is still under development [17,31]. The
main concern is that both the de-anonymization techniques and LOD base their
strength on interlinking. However, researchers working on heterogeneous graph
de-anonymization are trying to reuse and adapt approaches already used in a
homogeneous graph, e.g. social networks. These approaches are mainly based
on clustering and graph modification [33]. One of the considered approaches is
k-RDF-Neighbourhood Anonymity [14] which adapts the k-Neighbourhood [32]
algorithm to LOD released as RDF graphs. It is rare to find a technique able
to manage both the graph structure and the attributes attached to each node.
k-Neighbourhood is able to manage both structural and attribute aspects.
4 Research questions
Our research questions (RQs) deal with both the assessment and the enhance-
ment steps and both considering quality dimensions and the avoidance of privacy
breaches. The RQ related to the assessment steps can be summarised as follows:
RQ1. To what extent data quality and privacy concerns can be assessed indepen-
dently of the data format? In which case - if any - is there the need to consider
the original data format? In order to define only once the quality dimensions and
reuse it both for OD and LOD, we want to investigate if the quality dimensions
- in particular focusing on accuracy and completeness - can be defined indepen-
dently of the data format without loosing in precision. The same consideration
holds for privacy aspects.
RQ2. Can (automatic) data type inference be useful (in terms of effectiveness
and efficiency) in (linked) (open) data quality assessment?
RQ3. Can (automatic) data type inference be useful (in terms of effectiveness
and efficiency) in discovering privacy breaches?
6 Maria Angela Pellegrino
The main RQ related to the improvement phase can be summarised as follows:
RQ4. How to improve data quality while preventing privacy breaches?
Research questions are presented in the same order in which they are considered
during my Ph.D. It also justifies the different degree of refinement of the RQ.
During this year (which is my first year of Ph.D.), I will mainly focus on quality
and privacy assessment, while in the following years I will focus, first, on how
to improve data quality and, then, how to manage privacy leakages. Therefore,
the fourth question will be further refined in the future.
5 Hypotheses
At this stage of the work, we are able to hypothesise results only about the
quality and privacy assessment. H1 is related to RQ1, while H2 is related to
RQ2 and RQ3.
H1. We hypothesise that it is possible to define approaches which work directly
on values (or their collection) without caring about the original data set format.
H2. We consider our automatic data type inference (which will be detailed in
section 6) a suitable method to address both quality problems and privacy con-
cerns. We defined and implemented an approach to automatically infer the type
exclusively working on values. Inferred data types are consequently used i) to give
an insight about quality aspects and ii) to detect if privacy breaches occurred.
The performance and the scalability of this approach have been tested on open
data sets organised in tabular format. In the near future, we aim to verify if we
gain the same (positive) results also on LOD. In particular, we plan to verify
if it returns correct results and if it is the most efficient way to manage it. If
so, it is a first step in defining promising techniques that are independent of the
original data format. Consequently, we should verify if the same consideration
can be expanded to other assessment and enhancement approaches.
6 Preliminary results
We designed and implemented an approach [9] to assess the quality level and the
occurrence of privacy leakage working on the actual content of each value. Our
approach infers not only basic data types (such as string, number, date) but also
meta data types inspired by the GDPR. The novelty of our approach does not lie
in the type inference step, but in the exploitation of inferred types both to assess
quality aspects and to detect privacy breaches. About privacy breaches, we check
if i) a content privacy breach occurred: we verify if a description (i.e. any field
classified as string without a refined meta data type) contains any structured
sensitive information, such as phone number, IBAN, SSN; ii) a structural privacy
breach occurred: by considering meta data types attached to the columns, we
verify if data provider badly designed the data set by forcing users to fill in cells
with personal information. More in detail, our approach works as follows:
Data Quality Improvement of (Linked) (Open) Data 7
– it takes as input a data set seen as a collection of columns
* for each column seen as a collection of values
1. for each value, the data type is inferred according to its content. Our
approach attaches to each value a basic data type - such as number,
string, and date - and (if possible) a meta data type - such as province,
municipality, ZIP code, SSN, IBAN, email, address, surname, name
and so on. The latter is used to capture the semantic of the value.
By default, each value is a string as basic data type and it has no
meta data type;
2. for string values (i.e. for the values which the type inference approach
fails in refining the meta data type), the proposed approach checks
if a typo occurs. In other words, it verifies if by replacing, adding,
removing or swapping letters a known meta data type is matched;
3. if also the typo check fails, the proposed approach verifies if the value
contains a structured personal data, e.g. if it contains an IBAN, an
email, an address. In that case a content privacy breach occurred;
* to each column we assign the most frequent data types among its values;
* for each collection, the completeness and the accuracy are computed;
– once a data type has been attached to each column, the type inference mod-
ule checks if a structural privacy breach occurred. By structural privacy
breach we mean both the presence of information which exposes individ-
ually personal details - e.g. SSN - or the co-occurrence of quasi-identifier,
i.e. bits of information which identifies unequivocally an individual - e.g. the
co-occurrence of date of birth, gender and ZIP code.
Both the correctness and the scalability of this approach have been evaluated on
open data sets [9]. The approach is completely independent of the data format:
starting from a tabular or a linked data set, it is possible to work on each
value and to assess the quality and privacy aspects by our approach. We already
provided SPOD with a prototype of this approach. Moreover, SPOD is also
enhanced with a component [7] to query LOD by SPARQL and organise the
results into a tabular format. The results of a SELECT query can be always
organised in tabular format. Therefore, we plan to verify the applicability of our
approach also to LOD by organising queried data in a tabular view.
7 Approach
Our pragmatic approach aims to help data providers 1a) both in assessing data
quality problems and 1b) in identifying privacy leakages and 2) in providing ef-
fective and efficient strategies in solving detected problems. It is interesting to
notice that by improving a quality dimension, the other ones could be compro-
mised. For instance, by making data compliant with the GDPR, the complete-
ness could be compromised: to anonymise ZIP codes we might omit the last two
digits. In this way, the completeness (and also the accuracy) is affected. This
symbiosis of causes and solutions of quality and privacy aspects should be taken
into account when defining a data quality assurance process. The entire process
can be implemented as a cyclic approach, summarised as follows:
8 Maria Angela Pellegrino
1. data quality assessment
(a) definition of the quality dimensions to assess
(b) measurement of the chosen quality dimensions
(c) representation of the results of the measurements
2. data quality improvement
3. check if the improvements negatively affected the other quality dimensions
(also called validation)
In the validation step all the measurements for all the considered quality di-
mensions must be repeated. Thus, the validation step matches the measurement
step. The approach can be graphically represented by Figure 1.
Fig. 1. Schema of the proposed approach.
Quality assessment. We decided to focus on accuracy and completeness.
Syntactic accuracy. We plan to:
– apply our type inference approach on LOD. Then, we want to compare in-
ferred data types with data types specified in the queried LOD. For instance,
supposing to test all the values of the dbo:birthDate property. We can verify
if they are correctly recognised as dates.
– enhance the type inference approach to recognise patterns which are attached
to syntactic rules reported in the queried LOD. For example, supposing
that a relation has a data type pattern not supported by our type inference
approach (e.g. date time), we can add the regex to recognise it and identify
any syntactical wrong values;
– exploit either clustering algorithms or statistical approaches to detect out-
liers. At this moment, we are following the same approach described by
Fleischhacker et al. [10] and we are comparing DBscan, IQR, and Z-score in
order to verify which is the most accurate and efficient technique;
– compare the actual typo detection approach (part of the type inference pro-
cess) with clustering algorithms. The main drawback of the first approach is
the scalability: for each string for which a typo is hypothesised, it computes
all the words by adding, removing, swapping or replacing a letter against
the original word. Obviously, this naı̈ve approach explodes if we consider
more than one error. Our hypothesis is that a clustering algorithm achieves
Data Quality Improvement of (Linked) (Open) Data 9
better results, gaining also in efficiency. The main difficulty is in detect-
ing clustering algorithms able to deal with strings. At this moment, we are
comparing k-means and the agglomerative clustering to identify the most
accurate and efficient approach. An alternative is to exploit word or graph
embedding techniques to convert words (or the whole graph) into vectors
and use them to feed in clustering algorithms. Right now we are evaluating
the performance of KGloVe [5] and RDF2Vec [24] upon the clustering task.
Semantic accuracy. We plan to check if a set of values is semantically accurate,
by forecasting the values by other properties and then compare the predicted
against the actual ones. We aim to exploit either link prediction or external
resources. External resources are strictly dependent on the tested source. For
example, in order to validate data in DBpedia, we can use other well known
Knowledge Bases, such as Wikidata or Freebase;
Completeness. We plan to compare the column completeness calculated by the
type inference module against the one calculated directly on the graph. We will
further consider how to evaluate the other completeness dimensions.
Detection of privacy breaches. GDPR categorises as personal data “any
information relating to an identified or identifiable natural person; an identifiable
natural person is one who can be identified, directly or indirectly, in particular
by reference to an identifier such as a name, an identification number, location
data, ... ”. While some bits of information may not be uniquely identifying
individuals on their own, they can be potentially identifying individuals when
combined with other attributes [21,27]. The combination of these attributes is
defined as quasi-identifier. [22]. Both the occurrence of personally identifiable
information (PII) and quasi-identifier are detected by our data type inference
approach. It can be further enhanced to recognise a wider range of structural
privacy breaches. We also deal with content privacy breach. As an alternative,
we could exploit sentiment analysis techniques or classification algorithms to
distinguish sensitive and not-sensitive information.
Data quality improvement. To improve the data quality, we plan to perform
data enrichment. It can be realised by exploiting clustering algorithms in order
to identify new classes and grouping. To address the completeness requirement
and enrich the data set, we plan to apply link prediction techniques.
By data cleansing approaches, we aim to recognise erroneous data and clean
them. Applying Machine Learning (ML) approaches to LOD raises several diffi-
culties: LOD lack of negative examples [25], and performing the feature extrac-
tion phase on graphs is particularly expensive. Besides applying ML algorithms
directly on LOD, entities (and relations) can be vectorised by graph embedding
techniques [4,5,24]. The obtained vectors will then be fed in ML algorithms.
Privacy leakage avoidance. The privacy-preserving data publishing [11] guar-
antees methods and tools to publish data set by reaching a good trade-off be-
tween privacy preservation and the overall utility of the published data set. The
10 Maria Angela Pellegrino
anonymization techniques - also suggested by the GDPR - hide personal data
based on the idea that they should not be involved in statistical analyses. A
naı̈ve solution is the removal of PII, e.g. SSN, name, and surname. However,
because of the power of modern re-identification algorithms [20], removing PII
data does not guarantee that the remaining data does not identify individuals.
In order to make data sets compliant with the GDPR, we want to investigate
the k-RDF-Neighbourhood Anonymity [14].
Non-functional requirements. Besides the functional requirements, the proposed
approach has to address the following non-functional requirements:
– reversibility of the actions in order to provide data providers the possibility
to perform the undo of every action;
– traceability of the actions. This requirement is inspired by the potential
occurrence of several actors involved in the definition and maintenance of
data sets under definition. Therefore, there is the necessity to keep track of
the performed actions and their owner;
– efficiency ;
– scalability since the data could increase dramatically;
– interactivity since the data publishing and quality improvement could in-
volve several actors which have to work collaboratively.
8 Evaluation plan
To assess our approach we plan to evaluate the scalability by considering data
sets of increasing size. About the performances we will consider both the time
and the space used. Moreover, we plan to evaluate the correctness by i) manually
checking the results, ii) by using data set as a gold standard, iii) by comparing
them with results obtained by other tools iv) or by evaluating the same metrics
upon a different data format. For instance, the correctness calculated through
the type inference approach described above can be validated against the value
calculated upon the graph. To verify the usability and applicability of our ap-
proach, we plan to involve SPOD users in order to check how it performs in real
settings. The research described here is conducted in strict cooperation with
our PA and their ICT department. Therefore, they are interested in testing our
results and verify if they can be practically exploited in their every-day work.
9 Reflections
To the best of our knowledge, quality aspects and privacy concerns are rarely
managed simultaneously, both in OD and LOD. Therefore, our goal is to fill up
this gap by proposing a framework which helps data providers and consumers
in assessing and improving data quality, while preventing personal information
leakage. Moreover, the features offered by this framework will be integrated into
SPOD in order to reach a wider range of users both to help them in providing
Data Quality Improvement of (Linked) (Open) Data 11
data of better quality and to test the applicability of our proposal. Since this is
my first year of Ph.D., I plan to work on the assessment phase and study how
privacy concerns can be managed by the end of this year. I will dedicate the
next year to the quality improvement both studying the most reliable solutions
and by developing our own approach. To avoid reinventing the wheel, each step
is preceded by a study phase. I plan to reuse the most promising approaches
used in literature and defining our own approach to feel any gap. The third -
and last - year is dedicated to the evaluation and improvement of the proposed
approach by the collected considerations. The main novelty of our approach is
to provide a unique interface to manage both quality and privacy concerns.
Acknowledgement
I would like to thank my supervisor Prof. Vittoro Scarano for his support.
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