A Fuzzy Approach for Data Quality Assessment of Linked Datasets

Narciso Arruda

1 a

, J. Alc

antara

1 b

, V. M. P. Vidal

, Angelo Brayner

1 c

M. A. Casanova

2 d

, V. M. Pequeno

3 e

and Wellington Franco

1 f

Departamento de Computac¸

ao, Federal University of Cear

a, Fortaleza, Cear

a, Brazil

Department of Informatics, Pontiﬁcal Catholic University of Rio de Janeiro, Brazil

TechLab, Departamento de Ci

encias e Tecnologias, Universidade Aut

onoma de Lisboa Lu

ıs de Cam

oes, Portugal

Keywords:

Quality Assessment, Linked Data Mashup, Fuzzy Inference System, Data Quality, Logic Fuzzy.

Abstract:

For several applications, an integrated view of linked data, denoted linked data mashup, is a critical require-

ment. Nonetheless, the quality of linked data mashups highly depends on the quality of the data sources. In

this sense, it is essential to analyze data source quality and to make this information explicit to consumers of

such data. This paper introduces a fuzzy ontology to represent the quality of linked data source. Furthermore,

the paper shows the applicability of the fuzzy ontology in the process of evaluating data source quality used

to build linked data mashups.

1 INTRODUCTION

A special kind of web application, called Linked

Data Mashup (LDM), is responsible for combining,

aggregating and transforming data available on the

Web of Data (Schultz et al., 2011). Hence, Linked

Data Mashup applications are confronted with the

challenge of building an integrated view of different

linked data sources. That view is denoted Linked Data

Mashup view (LDM view).

Nonetheless Linked Data sources may present

data in different quality levels. For this reason, before

triggering the process of creating a given LDM view,

it is essential to ensure that data belonging to each

data source are in the quality level required by the data

consumer (user or application). Accordingly, in order

to deﬁne which data sources should be integrated two

criteria are critical: (i) data source relevance, and; (ii)

data quality.

Data quality evaluation is quite often done by ap-

plying a hierarchy of category, dimension and metric.

The highest hierarchy level, category, is composed

https://orcid.org/0000-0003-3873-8468

https://orcid.org/0000-0002-4297-2970

https://orcid.org/0000-0001-9820-9133

https://orcid.org/0000-0003-0765-9636

https://orcid.org/0000-0002-6424-0252

https://orcid.org/0000-0001-9093-0428

of Accessibility, Contextual, Intrinsic and Representa-

tional (Zaveri et al., 2016). The second level presents

several dimensions, such as Integrity, Accuracy, and

Conciseness. In turn, each dimension contains qual-

ity metrics (the lowest hierarchy level). One key goal

of quality assessment is to ﬁnd heuristics which ex-

press data consumer’s requirements for data quality,

based on the aforementioned hierarchy.

Linked data quality evaluation has been subject

of recent researches (Zaveri et al., 2016), (Debattista

et al., 2014b). Thus, several quality indicators have

been proposed along with techniques for assessing

data quality based on the proposed indicators. Al-

though those techniques offer ways to assess data

quality, their outputs are presented through quantita-

tive data (an absolute numerical value) and/or statis-

tical function, e.g., maximum, minimum, or average.

In some approaches, quality is expressed by means of

values belonging to the interval [0, 1] (or an alternative

isomorphic scale), which can reinforce difﬁculties for

interpreting quality measures.

In this paper, we propose the use of fuzzy logic to

model the domain of data quality as a way to over-

come imprecision and subjectivity. The idea is to

allow users to express data quality requirements by

means of a set of linguistic expressions on quality in-

dicators. In order to achieve our goal, a fuzzy ontol-

ogy to represent data quality is presented. The pro-

posed ontology reuses terms of W3C Data Quality

Arruda, N., Alcântara, J., Vidal, V., Brayner, A., Casanova, M., Pequeno, V. and Franco, W.

A Fuzzy Approach for Data Quality Assessment of Linked Datasets.

DOI: 10.5220/0007718803990406

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 399-406

ISBN: 978-989-758-372-8

399

Vocabulary (Debattista et al., 2016b) (DQV). More-

over, it provides the necessary concepts and terms to

specify data quality of linked data sources by means

of fuzzy terms. To assess the potentials of the pro-

posed approach, simulations on real data of 128 dif-

ferent LOD datasets have been conducted. The ob-

tained results are presented and discussed.

The remainder of this paper is structured as fol-

lows. Section 2 discusses related works. Section

3 discusses how to represent fuzzy quality concepts.

Section 4 presents fuzzy data quality vocabulary and

our proposal for data quality assessment. Section 5

shows experiments to validate our proposal. Finally,

Section 6 contains the conclusions.

2 RELATED WORK

Data quality is commonly conceived as a multi-

dimensional construction with dimensions such as

timeliness, completeness, consistency, interoperabil-

ity, conciseness, representational conciseness and

availability (Wang and Strong, 1996). The quality

dimensions are composed of quality metrics, which

measure the quality of the data along the dimen-

sions (Bizer and Cyganiak, 2009). More speciﬁcally,

quality metrics are heuristics designed to ﬁt a speciﬁc

assessment situation (Wang, 2005). Usually, quality

dimensions are grouped into categories. According

to (Zaveri et al., 2016), the dimensions can be classi-

ﬁed into four categories, namely: accessibility, con-

textual, intrinsic and representational. Table 1 shows

examples of three quality categories with some qual-

ity dimensions and their quality metrics.

Data quality assessment can be computed

automatically (Gu

eret et al., 2012) or semi-

automatically (Mendes et al., 2012; Debattista

et al., 2014b). Due to the many quality indicators,

it is difﬁcult for the user to judge whether a dataset

is ﬁt for use, which is a problem for the automatic

approach. In the semiautomatic approach, the user

interacts with the tool to deﬁne the adequacy of

the data to the intended use. The Luzzu quality

assessment framework (Debattista et al., 2014b), for

example, implements part of the cataloged quality

metrics shown in (Zaveri et al., 2016) and generates

quality metadata from assessed datasets, which is

used for ranking datasets based on the quality aspects

prioritized by the user. For ranking, the authors

in (Debattista et al., 2014b) proposed a user-driven

ranking algorithm where users can deﬁne weights on

their preferred categories, dimensions or metrics. In

Luzzu (Debattista et al., 2014b) and Sieve (Mendes

et al., 2012), the quality required by the user is

informed through a conﬁguration interface, using

a numerical approach. Thereby, the user chooses

quality indicators that are more appropriate for

their purposes, deﬁne weights, and deﬁne how this

indicator must be aggregated. The problem of using

a numerical approach to evaluate the quality of a

dataset is how this information can be reused in a

context where the same set of data will be used in

different scenarios.

In the process of creating the Linked Data Mashup

view, some approaches have been proposed, for

example, LDIF (Schultz et al., 2011) and OD-

CleanStore (Knap et al., 2012). Typically, these ap-

proaches use the data quality as part of the data fusion

process. Sieve (Mendes et al., 2012) is a module in-

cluded in Linked Data Integration Framework (LDIF)

that is dedicated to quality assessment and fusion

of Linked Data. Sieve uses metadata about named

graphs to assess data quality as deﬁned by users. In

ODCleanStore (Knap et al., 2012) quality metadata

(containing data provenance and quality scores) can

be used in the data fusion process. Its quality as-

sessment component checks whether the dataset (con-

verted in a named graph) satisﬁes custom consistency

policies and them calculate the quality score of the

dataset (Knap et al., 2012). Therefore, the fuzzy qual-

ity metadata can be easily adapted and used in that

tool. The use of fuzzy quality assessment allows a

reasonable justiﬁcation for the evaluation result.

In recent years, ontologies have been speciﬁed to

conceptualize the quality of Linked Data (Debattista

et al., 2014a), (Debattista et al., 2016b). In (F

urber

and Hepp, 2011) the authors propose the DQM (Data

Quality Management) ontology with a vocabulary to

represent data quality requirements or rules. How-

ever, this ontology does not represent the results of the

data quality assessment, only the data quality require-

ments. The daQ ontology (Debattista et al., 2014a)

represents information about the quality of linked

datasets and meets generic measures of data quality.

Also, its vocabulary can be extended as needed. The

Data Quality Vocabulary (DQV) (Debattista et al.,

2016b) was designed to act as a model covering many

aspects of data quality and was inspired by the daQ

ontology. The DQV vocabulary goes one step fur-

ther, emphasizing feedback, annotation, agreements

and quality policies, all describing the quality of a set

of data. However, this vocabulary enables data pub-

lishers to compute the quality of the data so that data

consumers can ﬁnd out if the data is suitable for the

intended use and not represent the quality of the data

expected by the consumer.

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

400

Table 1: Examples of metrics, dimensions and categories.

Category Dimension Metric

Intrinsic Consistency M1 (Usage of incorrect domain or range data type)

M2 (Misuse owl:DatatypeProperty or owl:ObjectProperty)

M3 (Entities as members of disjoint classes)

Conciseness M4 (Provides a measure of the redundancy of the dataset)

Accessibility Availability M5 (desereferentiability of the URI)

M6 (SPARQL endpoint availability)

M7 (RDF dump availability)

Representa- Interopera- M8 (existing terms reuse)

tiol bility M9 (existing vocabulary reuse)

Concision M10 (short URIs)

3 FUZZY LOGIC BACKGROUND

3.1 Fuzzy Set

Fuzzy sets have been introduced by Zadeh in (Zadeh,

1965) to represent and manipulate non-precise data.

A fuzzy set is characterized by a membership function

deﬁned on the universe of discourse. This function

maps elements of the universe of discourse to a range

covering the interval [0,1] and indicates its member-

ship degree concerning the fuzzy set (i.e., the degree

to which the elements of the universe of discourse is

a member of the fuzzy set). A membership function

value of 0 means the corresponding element is not an

element of the fuzzy set, the value of 1 means that

the element entirely belongs to the set, and the values

between 0 and 1 represent fuzzy members, which be-

long to the fuzzy set only partially. A fuzzy set A is

formally deﬁned as a set of ordered pairs, such as

A = {(x, µ

(x)) | x ∈ X },

where X is the universe of discourse, µ

is the mem-

bership function that represents a fuzzy set A; and

(x) is the membership degree of the element x in

the fuzzy set A (µ

(x) indicates how much x is com-

patible with the set A).

Especially in data quality, in most of the cases, it

is impossible to give exact deﬁnitions or descriptions

for quality concepts and relationships between these

concepts. To resolve this problem, we use fuzzy sets

to represent the quality of the data. For example, in

our case study, we deﬁned three fuzzy sets to repre-

sent the quality levels: low, medium and high. The

triangular functions are more practical and more used

(Ivezi

c et al., 2008). Therefore, we use as our mem-

bership functions. A triangular function f is deﬁned

on the scalar parameters a, b and c, as shown below:

f (x, a, b, c) =











0, x ≤ a

x−a

b−a

, a < x ≤ b

c−x

c−b

, b ≤ x < c

0, c ≤ x

3.2 Linguistic Variable and Fuzzy Rule

One of the fundamental concepts of fuzzy set theory

is the concept of linguistic variable (Ross, 2009). Its

values are declarations in natural language (linguistic

values), which are names of fuzzy sets deﬁned in a

universe of discourse. A linguistic variable V is for-

mally deﬁned as a triple

V = (N, T (N), X ),

where N is the name of the linguistic variable, T (N) is

the set of linguistic values of N deﬁned in the universe

of discourse X .

One way to represent human knowledge is to form

natural language expressions of the “IF THEN” type.

This way of representing knowledge is entirely ap-

propriate in the linguistic context because it expresses

human empirical and heuristic knowledge in our com-

munication language (Ross, 2009).

In general, a fuzzy rule is an inference rule ex-

pressed as

IF (x

is A

) ⊗ · ·· ⊗ (x

n−1

is A

n−1

) THEN (x

is A

where ⊗ is the logical conjunction operator (AND) or

the logical disjunction operator (OR), x

, x

, . . ., x

are

linguistic variables, and A

, A

, . . . , A

are linguistic

values assumed by these variables, respectively. The

“IF” part of the rule is called antecedent, and in the

“THEN” part is called consequent.

Rules are used to infer the quality of dimensions,

categories, and datasets. We deﬁne three types of

rules: dimension rules, category rules, and quality

A Fuzzy Approach for Data Quality Assessment of Linked Datasets

401

rules. In the dimension rules, the antecedents are ex-

pressions about the quality of the metrics, and the fol-

lowing ones are expressions about the quality of the

dimensions. In the category rules, the antecedents are

expressions about the quality of the dimensions and

the following ones are expressions about the quality

of the categories. In the quality rules, the antecedents

are expressions about the quality of the metrics, di-

mensions or category and the following ones are ex-

pressions about the quality of the dataset.

3.3 Running Example

Consider that, for the quality metric M1, M2 and M4,

and consistency and conciseness dimensions, one has

deﬁned:

• linguistic variable M1 = (M1, T (M1), X ),

where X = [0, 1], T (M1) = {low, medium, high}

and the membership functions are µ

low

(x) =

f (x, 0, 0, 0.5), µ

medium

(x) = f (x, 0, 0.5, 0.1) and

high

(x) = f (x, 0.5, 1, 1);

• linguistic variable M2 = (M2, T (M2), X), where

X = [0, 1], T (M2) = {low, medium,high}

and the membership functions are µ

low

(x) =

f (x, 0, 0.9, 0.95), µ

medium

(x) = f (x, 0.9, 0.95, 1)

and µ

high

(x) = f (x, 0.95, 1, 1).

• linguistic variable M4 = (M4, T (M4), X), where

X = [0, 1], T (M4) = {low, medium,high}

and the membership functions are µ

low

(x) =

f (x, 0, 0, 0.5), µ

medium

(x) = f (x, 0, 0.5, 1) and

high

(x) = f (x, 0.5, 1, 1).

• linguistic variable consistency =

(consistency, T (consistency), X), where

X = [0, 1], T(consistency) = {low, medium, high}

and the membership functions are µ

low

(x) =

f (x, 0, 0, 0.5), µ

medium

(x) = f (x, 0, 0.5, 1) and

high

(x) = f (x, 0.5, 1, 1).

• linguistic variable conciseness =

(conciseness, T (conciseness), X), where

X = [0, 1], T(conciseness) = {low, medium, high}

and the membership functions are µ

low

(x) =

f (x, 0, 0, 0.5), µ

medium

(x) = f (x, 0, 0.5, 1) and

high

(x) = f (x, 0.5, 1, 1).

Note that the indicators are deﬁned by different

linguistic values. However, to simplify the nota-

tion, we denote the values with the same name (low,

medium, high).

The fuzzy quality of dimensions is inferred by the

rules of dimension. Thus, we use the metrics M1 and

M2 to infer the quality of the consistency dimension,

and the metrics M4 to infer the quality of the concise-

ness dimension. Table 2 shows the metric rules that

Table 2: Fuzzy Rules used to infer the quality of the consis-

tency dimension.

Rules

R1 IF M1 is high AND M2 is high

THEN consistency is high

R2 IF M1 is medium AND M2 is medium

THEN consistency is medium

R3 IF M1 is low OR M2 is low

THEN consistency is low

Table 3: Fuzzy Rules used to infer the quality of the con-

ciseness dimension.

Rules

R1 IF M4 is high THEN conciseness is high

R2 IF M4 is medium

THEN conciseness is medium

R3 IF M4 is low THEN conciseness is low

infer the quality of the consistency dimension, and Ta-

ble 3 shows the metric rules that infer the quality of

the conciseness dimension.

3.4 A Fuzzy Inference System

A Fuzzy Inference System (FIS) captures and offers a

way of the subjective human knowledge of real pro-

cesses manipulating practical knowledge with some

level of uncertainty. A fuzzy inference system may

be organized into the four components:

• Knowledge Base: Composed of a rule database,

containing a number of fuzzy rules, and a function

database which deﬁnes the membership functions

of the fuzzy sets used in the fuzzy rules.

• Fuzziﬁcation: Converts the crisp input in a lin-

guistic variable using the membership functions

stored in the database. The input is always a crisp

numerical value limited to the universe of dis-

course of the input variable.

• Inference: Determines the degree of validity of

the consequent of the rules and combines as out-

put the results in a linguistic variable. Meth-

ods of fuzzy inferences perform this process. A

well-known inference is described in Mamdani,

Sugeno and Tsukamoto (see (Ross, 2009) for

more details).

• Defuzziﬁcation: Derives a single crisp value that

best represents the inferred fuzzy values of the

output linguistic variable. There are several meth-

ods of defuzziﬁcation in the literature, the best

known methods are: Maximum value, Mean value

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

402

of maximums, Bisector, Centroid or Center of

Gravity (see (Ross, 2009) for more details).

4 A FUZZY APPROACH TO DATA

QUALITY ASSESSMENT

4.1 Fuzzy Data Quality Vocabulary

The DQV vocabulary distinguishes between three

layers of abstraction (metric, dimensions and cate-

gory), based on a survey presented in (Zaveri et al.,

2016). Quality metrics (dqv:Metric) are grouped

into quality dimension (dqv:Dimension), by property

dqv:inDimension). Quality dimensions are grouped

into quality category (dqv:Category), by property

dqv:inCategory. dqv:QualityMeasurement repre-

sents a quality metric measure of a given resource

(rdfs:Resource), a resource can be a set of data, a

set of links, a graph or a set of triples in which quality

measurement is performed.

The proposed Fuzzy Quality Data Vocabulary

(FQV) extends DQV to represent fuzzy concepts. For

that, terms from Fuzz-Onto ontology (Yaguinuma

et al., 2012) (a meta-ontology for representing fuzzy

ontologies) are incorporated by the proposed ap-

proach. FQV presents the following elements:

• fuz:FuzzyConcept (concept): represents linguis-

tic values which are associated to linguistic vari-

ables. Fuzzy atomic concepts are deﬁned in a

discrete domain. If an atomic concept in a do-

main ontology is a fuzzy concept, it should be

subsumed by fuz:FuzzyConcept to denote that its

individuals belong to the concept with a certain

membership degree in [0, 1]; fuz:FuzzyConcept

is deﬁned by a parameterized membership func-

tion, by the fuz:hasMembershipFunction relation-

ship;

• fuz:hasMembershipDegree (attribute): deﬁnes the

membership degree, which is represented by a

real number in the interval [0, 1];

• fuz:hasFuzzyConcept (relationship): associates

an instance of fqv:QualityIndicator to a fuzzy

concept;

• fqv:QualityIndicator (concept): represents lin-

guistic variables, which can be characterized

by linguistic values represented by fuzzy sets.

These linguistics variables are metrics, di-

mensions, or categories, which involve fuzzi-

ness in their deﬁnition. Each instance of

fqv:QualityIndicator should be associated with

one or more linguistic terms (fuz:FuzzyConcept)

by the fuz:hasFuzzyConcept relationship;

• fuz:hasMembershipFunction (relationship): asso-

ciates a linguistic term to its corresponding mem-

bership function;

4.2 Fuzzy Linked Data Quality

Assessment Process

The Fuzzy Quality Assessment Process receives as

input quality metadata and computes the quality of

datasets based on fuzzy rules and linguistic variables,

using the metadata generated by Luzzu (Debattista

et al., 2016a). This process is composed of two phases

(see Figure 1), described in what follows.

Phase 1: Fuzzy Quality Metadata Computation.

Fuzzy quality metadata are quality metadata, in which

fuzzy concepts are inferred from quality metrics,

based on linguistic variables and fuzzy rules deﬁned

by a specialist.

Step 1: Metric Fuzziﬁcation. Linguistic variables

are deﬁned by data consumers for the relevant quality

metrics. For each linguistic variable, precise values

are speciﬁed. such values represent quality levels of

the metrics. For each measure of the relevant quality

metric in the quality metadata, the values are mapped

into fuzzy sets and determined the degree of member-

ship of each set through its member functions. The

fuzzy values of the metrics are generated as a DQV

vocabulary.

Step 2: Dimension Fuzziﬁcation. Linguistic vari-

ables are deﬁned for quality dimensions associated

with relevant quality metrics (identiﬁed by a special-

ist). For each linguistic variable, the linguistic values

are deﬁned, which are the levels of quality of the di-

mensions. Dimension Rules are created based on the

linguistic variables of the metrics and infer the fuzzy

values of the dimensions. For each dimension of the

relevant quality metric in the quality metadata, dimen-

sion rules infer the fuzzy values. The fuzzy values of

the dimensions are generated as the DQV vocabulary.

Step 3: Category Fuzziﬁcation. Linguistic variables

are now deﬁned for quality categories associated with

relevant quality dimensions. For each linguistic vari-

able, the linguistic values are deﬁned, which are the

levels of quality of the categories. Category Rules

are created based on the linguistic variables of the di-

mensions. For each categories of the relevant quality

dimension, category rules infer the fuzzy values. The

fuzzy values of the categories are generated as a DQV

vocabulary.

Phase 2: Dataset Quality Evaluation. Data quality

fuzzy computation is implemented by a fuzzy infer-

ence system (FIS), capable of inferring data quality

based on fuzzy rules, deﬁned to express quality re-

quirements of data consumer. Thus, a linguistic vari-

A Fuzzy Approach for Data Quality Assessment of Linked Datasets

403

Figure 1: Fuzzy Quality Assessment Process.

able is speciﬁed for the quality of the dataset. The

linguistic values are deﬁned according to the desired

level of quality. Quality Rules are created based on

the linguistic variables of the metrics, dimensions and

categories and should be able to infer fuzzy values for

quality.

5 EMPIRICAL EVALUATION

We have created knowledge bases with generated

fuzzy quality metadata for 128 LOD datasets

. The

quality evaluation published in (Debattista et al., ) has

been chosen as the baseline. In order to achieve our

goal, quality metadata of 33 quality metrics of those

dataset have been collected.

For the sake of clarity, but without loss of general-

ity, we have used dataset quality metrics belonging to

dimensions consistency and conciseness. The evalu-

ated metrics were: (i) M1, which evaluates the use of

incorrect domain or range data type; (ii) M2, which

estimates the misuse OWL data type or object prop-

erties, and; (iii) M4, which provides a measure of the

redundancy of the dataset (see Table 1). For each met-

ric and dimension, we have considered three levels of

fuzzy quality, namely, low, medium and high. Thus,

we deﬁne three linguistic values for each linguistic

variable.

The membership functions of the linguistic val-

ues for metrics M1, M2, M4, and consistency, con-

ciseness dimensions are the same deﬁned in Section

3.3. To infer the fuzzy quality of consistency di-

mension the rules presented in Table 2 have been

The knowledge bases can be accessed at

http://tiny.cc/knowledgebases

deployed, and to infer the fuzzy quality of concise-

ness dimension the rules in Table 3 have been used.

The linguistic variable used in the experiments is

deﬁned as quality = (quality, T (quality),X), where

X = [0, 1], T (quality) = {low, medium, high}. Con-

sider now that the following membership functions

have been deﬁned: µ

low

(x) = f (x, 0, 0, 0.5), µ

medium

(x)

= f (x, 0, 0.5, 1) and µ

high

(x) = f (x, 0.5, 1, 1).

The experiments have been split into two parts. In

the ﬁrst part, the experiments compared the proposed

quality evaluation approach with the baseline (tradi-

tional approach (Mendes et al., 2012)). In the second

part, the experiments measured dataset quality by

applying the proposed approach.

Part I - Dataset Quality Evaluation based on

Metrics

Table 4: Fuzzy Rules used in Experiments of Part I.

Rules

R1 IF M1 is high AND M2 is high

THEN Quality is high

R2 IF M1 is medium AND M2 is high

THEN Quality is high

R3 IF M1 is high AND M2 is medium

THEN Quality is high

R4 IF M1 is medium AND M2 is medium

THEN Quality is medium

R5 IF M1 is medium AND M2 is low

THEN Quality is low

R6 IF M1 is low AND M2 is medium

THEN Quality is low

R7 IF M1 is low AND M2 is low

THEN Quality is low

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

404

hp://dbtune.org/bbc/peel/

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

0 20 40 60 80 100 120

incorrectDomainRange misuseDatatypeObject avg fuzzy

Figure 2: Results of the quality assessment of datasets based on metrics M1 and M2.

These experiments compared the proposed ap-

proach against the baseline (average aggregation

method). The quality values may be inferred by the

rules for metrics M1 and M2 (see Table 4), which are

metrics of the consistency dimension.

Figure 2 depicts fuzzy quality values and average

aggregation method (avg) values (baseline). As al-

ready mentioned, fuzzy quality values are computed

by a fuzzy inference system. The used rules are

shown in Table 4. The membership functions are the

same speciﬁed in Section 3.3. Finally, the Mamdani

inference method and the Center of Gravity defuzzi-

ﬁcation method have been used.

Points marked with x (1 to 8) on the avg curve

(Figure 2) highlight the difference between the

baseline and fuzzy assessment methods. Observe

that there exist peaks in values of metric M2 (value

decreasing) and metric M1 (value increasing). In the

baseline (average method), the computed average

values smooth those peaks, giving the idea of a false

positive concerning quality level. Furthermore, the

proposed approach delivers more precise informa-

tion, because a decrease in the value of the metric M2

induces a low quality level. The quality of the metric

M2 is high when the value is greater than 0.95 and,

for values smaller than 0.95, the quality of the metric

M2 is low. Therefore, the fuzzy inference system

has very different behavior, concerning the change of

value.

Part II - Dataset Quality Evaluation based on Di-

mensions

Table 6 shows the results of quality assessment of

some LOD datasets, based on the quality rules of Ta-

Table 5: Fuzzy Rules used in Experiments Part II.

Rules

R1 IF Consistency is high AND

Conciseness is high THEN Quality is high

R2 IF Consistency is high AND

Conciseness is medium

THEN Quality is high

R3 IF Consistency is medium

THEN Quality is medium

R4 IF Consistency is low THEN Quality is low

ble 5. The consistency and conciseness columns show

the degree of membership to the quality levels of the

datasets. The quality column shows the quality as-

sessment value.

The linguistic values of the fuzzy quality vari-

able divide the universe of discourse in a balanced

way. We consider 50% as the balance point of qual-

ity, where values less than 50% represent quality is

insufﬁcient and more than 50% quality is sufﬁcient.

In the Peel and DBLP (L3S) datasets, the qual-

ity is less than 50%. Note that, the conciseness of

the datasets is high (> 85%) and the conciseness is

medium. However, the consistency is not high and

the quality is high only when consistency is high and

the conciseness is high or medium (rules R1 and R2

of Table 5), and on the other hand, the quality is low

when consistency is low (rule R4 of Table 5). Thus,

these datasets do not reach enough quality.

The ELIONET dataset does not have high con-

ciseness, but it has average concision with member-

ship of 75%, and high membership consistency of

65%, the quality is over 50%. According to rule R2 of

A Fuzzy Approach for Data Quality Assessment of Linked Datasets

405

Table 6: Result of Quality Assessment based on Consistency and Conciseness Dimension.

Dataset

Consistency Conciseness

Quality

low medium high low medium high

Peel 58% 41% 0% 0% 11% 88% 41%

DBLP (L3S) 3% 96% 0% 0% 2% 97% 49%

DBpedia 0% 37% 62% 0% 3% 96% 59%

EIONET 0% 34% 65% 24% 75% 0% 60%

Table 5, the quality is high when greater than 50% and

the dataset has high quality since it meets this rule.

6 CONCLUSION

In this work, we proposed the use of fuzzy logic to

model the domain of data quality. We described a

fuzzy ontology to represent data quality. Additionally,

we described and analyzed an approach for evaluating

Linked Data quality based on fuzzy logic. In the pro-

posed approach, quality measurement is inferred by

fuzzy inference systems based on user-deﬁned fuzzy

rules. Results of experiments on real datasets were

reported and discussed.

We emphasize that the proposed approach present

real beneﬁts in the process of providing linked data

mashups with required level of quality. As future

work, we shall investigate the use of fuzzy logic in

computing mashup data quality.

ACKNOWLEDGEMENTS

This work was partly funded by CNPq under grants

303332/2013-1 and 442338/2014-7, by FAPERJ un-

der grant E-26/201.337/2014, and by FCT - Fundac¸

para a Ci

encia e a Tecnologia, under the grant SFRH/-

BPD/76024/2011.

REFERENCES

Bizer, C. and Cyganiak, R. (2009). Quality-driven informa-

tion ﬁltering using the WIQA policy framework. Web

Semant., 7(1):1–10.

Debattista, J., Auer, S., and Lange, C. (2016a). Luzzu—a

methodology and framework for linked data quality

assessment. Journal of Data and Information Quality

(JDIQ), 8(1):4.

Debattista, J., Dekkers, M., Gu

eret, C., Lee, D., Mihinduku-

lasooriya, N., and Zaveri, A. (2016b). Data on the web

best practices: Data quality vocabulary.

Debattista, J., Lange, C., and Auer, S. (2014a). daq, an on-

tology for dataset quality information. In Proc. Work-

shop on Linked Data on the Web co-located with the

23rd Int. World Wide Web Conference (WWW 2014).

Debattista, J., Lange, C., Auer, S., and Cortis, D. Evaluat-

ing the quality of the lod cloud: An empirical investi-

gation.

Debattista, J., Londo

no, S., Lange, C., and Auer, S. (2014b).

LUZZU - A framework for linked data quality assess-

ment. CoRR, abs/1412.3750.

urber, C. and Hepp, M. (2011). Towards a vocabulary

for data quality management in semantic web archi-

tectures. In Proc. 1st Int. Workshop on Linked Web

Data Management, LWDM ’11, NY, USA. ACM.

eret, C., Groth, P., Stadler, C., and Lehmann, J.

(2012). Assessing linked data mappings using net-

work measures. In Proc. 9th International Confer-

ence on The Semantic Web: Research and Applica-

tions, ESWC’12, pages 87–102. Berlin, Heidelberg.

Ivezi

c, D., Tanasijevi

c, M., and Ignjatovi

c, D. (2008).

Fuzzy approach to dependability performance evalu-

ation. Quality and Reliability Engineering Interna-

tional, 24(7):779–792.

Knap, T., Michelfeit, J., Daniel, J., Jerman, P., Rych-

novsk

y, D., Soukup, T., and Ne

cask

y, M. (2012). Od-

cleanstore: a framework for managing and providing

integrated linked data on the web. In Int. Conf. Web

Information Systems Engineering, pages 815–816.

Mendes, P. N., M

uhleisen, H., and Bizer, C. (2012). Sieve:

Linked data quality assessment and fusion. In Proc.

2012 Joint EDBT/ICDT Workshops, EDBT-ICDT ’12,

pages 116–123, NY, USA. ACM.

Ross, T. (2009). Fuzzy logic with engineering applications.

John Wiley & Sons, Inc.

Schultz, A., Matteini, A., Isele, R., Bizer, C., and Becker, C.

(2011). LDIF - linked data integration framework. In

Proc. Second Int. Conf. on Consuming Linked Data,

COLD’11, Aachen, Germany. CEUR-WS.org.

Wang, R. Y. (2005). Information Quality (Advances in Man-

agement Information Systems). M. E. Sharpe, Inc.,

Armonk, NY, USA.

Wang, R. Y. and Strong, D. M. (1996). Beyond accuracy:

What data quality means to data consumers. J. Man-

age. Inf. Syst., 12(4):5–33.

Yaguinuma, C., Santos, M. T. P., Camargo, H. A., Nico-

letti, M. C., and Nogueira, T. M. (2012). Fuzz-onto:

A meta-ontology for representing fuzzy elements and

supporting fuzzy classiﬁcation rules. In Proc. 12th Int.

Conf. on Intelligent Systems Design and Applications.

Zadeh, L. (1965). Fuzzy sets. Information and Control,

8(3):338 – 353.

Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann,

J., and Auer, S. (2016). Quality assessment for linked

data: A survey. Semantic Web, 7(1):63–93.

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