DQRDFS

Towards a Semantic Web Enhanced with Data Quality

Ismael Caballero, Eugenio Verbo

Indra Software Labs, UCLM - Indra Research and Development Institute

Ronda de Toledo s/n – 13004 Ciudad Real, Spain

Coral Calero, Mario Piattini

ALARCOS Research Group, Information Systems and Technology Departament

UCLM - Indra Research and Development Institute, Paseo de la Universidad 4 s/n – 13071 Ciudad Real, Spain

Keywords: Data Quality, Semantic Web, DQRDFS, DQMetadata, ISO 15939, Data Quality Measurement, Quantity of

Data Quality.

Abstract: Nowadays, data is of critical importance as a resource. Using data of poor quality can be the source of

several problems when developing a project. The World Wide Web is currently the main showcase for a

vast amount of data. It would be desirable that machines can process the quality of the data contained on the

Web Documents. This paper introduces a new view of the Semantic Web based on the concept of Quantity

of Data Quality (QDQ), in which Data Quality issues will be used as a basis to enable machines to process

the Semantic Web Documents for different activities like information retrieval or document filtering. This

view can open new challenges in Semantic Webs oriented to improve users’ satisfaction with the Internet.

1 INTRODUCTION

Advances in software technologies and networks

have lead to the development of new Information

Systems running on the Web, as a means for

organizations to be as close as possible to all their

customers, stakeholders and other organizations.

This kind of system allows companies to publish, via

the web, documents containing data related to the

tasks which must be carried out by specific users. If

data is not of high enough quality, then users cannot

correctly complete their projects. So it is important

to take care with data quality (hereafter DQ) in order

to ensure that users achieve a better standard of

project. Up to now, users have been responsible for

assessing the quality of the data, since they are the

only ones who are able to understand it and its

meaning.

In their definition of Semantic Web, (Bernes-Lee

et al., 2001) state that “the Semantic Web is an

extension of the current web in which information is

given well-defined meaning, better enabling

computers and people to work in cooperation”. This

definition opens the possibility for machines to

understand data contained in web documents: if they

can understand it, they can also manage the quality

of it. Furthermore, machines could apply this

managerial capability to doing tasks like

discriminating documents attending to their data

quality levels before deploying them to users.

But in order to turn these proposals into reality,

some value must be added to the Semantic Web, and

to the way in which Semantic Web Documents can

be performed.

This paper has a twofold goal: (1) to provide

readers with a brief background of DQ (section 2)

and (2) to show how we have applied DQ

fundamentals in order to enhance the quality of Web

Documents for Semantic Web (section 3).

2 DATA QUALITY

BACKGROUND

2.1 Data Quality Dimensions

Data is said to be of quality if it fits the purpose for

which is used (Strong et al., 1997). One of the most

178

Caballero I., Verbo E., Calero C. and Piattini M. (2008).

DQRDFS - Towards a Semantic Web Enhanced with Data Quality.

In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 178-183

DOI: 10.5220/0001526701780183

 SciTePress

interesting strategies for tackling the study of DQ for

a context, is to break it down into “minor qualities”

known as DQ dimensions (Lee et al., 2006). Each

scenario requires some dimensions which best fit the

use of the data. The sets of DQ Dimensions usable in

a context are known as DQ model. Literature shows

several examples of DQ models for specific

scenarios: medical and healthcare (Al-Hakim, 2007),

decision support system (Gendron and D'Onofrio,

2002), or web (Caro et al., 2007), to name a few.

ISO, at this moment, is working on the draft of the

ISO/IEC 25012 standard (ISO-25012, 2007), a part

of the SQUARE family that will propose a DQ

model for IS. In any case, the generic classification

proposed by (Strong et al., 1997) has been widely

used. These authors group DQ dimensions into four

categories making reference to the point of view

from which DQ can be observed: Intrinsic DQ

(dimensions of accuracy, objectivity, credibility,

reputation) refers to the quality of the data itself;

Accessibility DQ category contains dimensions

(accessibility, Access security) providing meaning

about the extent to which data can be accessed;

Contextual DQ (Relevancy, Value-Added,

Timeliness, Completeness, Amount of data) refers to

those DQ dimensions which deal with the use of

specific data in a context; Representational DQ

category (interpretability, ease of understanding,

concise representation, consistent representation) is

centred on those characteristics of the representation

of data which make it usable. A more complete

definition of the meaning of these dimensions can be

found through DQ literature, the most interesting

works being those proposed by (Batini and

Scannapieco, 2006, Lee et al., 2006, English, 1999).

2.2 Measuring and Assessing DQ

Let us give the name stakeholder to any person or

process involved in the use of the data or of

resources which have data. Any stakeholder will

need to assess how good a piece of data is for the

task to be executed. We would like to highlight the

difference between the concepts of “measuring DQ”

and “assessing the DQ level” of a piece of data for a

task. For each DQ Dimension belonging to the DQ

model used for the assessment, some measurements

must be taken.

Both measurement and assessment are going to

depend on the intended use of the data and on the

nature of the DQ dimension (which determine the

measurement method (ISO/IEC, 2000)). For

measuring, a base or a derived measure must be

drawn. In this case, a measurement method or a

measurement function is required. On the other

hand, it could possibly be said that for the

assessment and indicator might be enunciated.

According to literature, typical derived DQ

measures have a measurement function based on the

percentage of the Number of Data Units which do or

do not satisfy a DQ criterion (Batini and

Scannapieco, 2006, Lee et al., 2006). This fact

confers to the measurement of a ratio scale (see

Figure 1):

Measure

umberOfDataUnitsNotSatisfyingADQCriterion

TotalNumberOfDataUnits

=−

Figure 1: Typical DQ Measure.

In the formula of Figure 1, there are two base

measures: NumberOfDataUnitsNotSatisfyingACrite-

rion and TotalNumberOfDataUnits. The measure-

ment method for both consists of counting a number

of data units. For the second one, there is only one

problem, which is counting all data units of the piece

of data. In the first, the counting is limited to those

data units affected by the criterion. A criterion is

usually defined as a business rule to warranty the

soundness of the data (English, 1999, Loshin, 2001,

Wang, 1998). The result of deciding if the data unit

satisfies the criterion can be “True” or “False”. So,

in order to obtain a value for the measure, a count of

data units having obtained a “true” value must be

done. But the intrinsic difficulty is addressed at

defining how the data unit satisfies the criterion.

Sometimes, some metadata is necessary for each

piece of data to complete its meaning in order to be

able to decide whether or not it satisfies the criteria.

To make a decision, a rule based on this metadata is

needed. This rule can consist of objectively or

subjectively determining if the value of metadata

belongs to a given domain. (Naumann and Rolker,

2000) identify the following as possible sources for

values of metadata: a stakeholder, the information

manufacturing process or even the same data store.

Different authors in the DQ field agree that values

for metadata coming from users are probably

subjective, whereas the ones coming from the proper

data stores are objective.

Having to add some metadata to the data, a new

problem arises: how to attach the metadata to the

data and how to store it conveniently. In (Wang et

al., 1995), a possible solution for the relational

model is proposed. It consists of tagging data: attach

the DQ metadata as if it were another common

attribute. It could be seen as a way of semantic

annotation. (Caballero et al., 2007) propose another

solution based on (Wang et al., 1995) for XML.

DQRDFS - Towards a Semantic Web Enhanced with Data Quality

179

They propose an XML Schema named DQXSD that

allows making such annotations for XML files (see

figure 2): The qualityData is used as the root of the

XML document; Entity is anything containing data

(a relational schema, an XML documented). Each

entity can have attributes, the DQ of which must be

studied, like relational attributes or elements in

XML files. Authors use the name

measurableConcept for DQ Dimensions in order to

align their model to ISO/IEC 15939 (ISO/IEC,

2000). Finally, for each measurableConcept, zero or

more DQMetadata-Attributes are defined, and

given a value which is used to assess the DQ level of

each entity regarding the measurable concept.

«element»

entity

+id : String

«element»

qualityData

+name : string

+entityCategory : string

+dataSourceType

«element»

attribute

+name : String

0..*1

«element»

DQMetadataAttribute

+name : string

-value : string

0..*

0..* 1

0..*

«element»

measurableConcept

+name : String

0..*

Figure 2: DQXSD (Caballero et al., 2007).

Next, we are going to explain how these

fundamentals could be introduced into the Semantic

Web.

3 DQRDFS: DQ AT SEMANTIC

WEB

The main aim of this paper is to enable a new

perspective in which machines can automatically

process the quality of the data contained in the

Semantic Web Documents in order to increase user’s

satisfaction with Semantic Web applications in tasks

like information retrieval or semantic searches. This

implies that machines need to measure and assess

this data by making corresponding DQ semantic

annotations in “such way that can be used for more

effective discovery, automation, integration and

reuse across various applications (Guha et al.,

2003)”

For this reason, we are going to show how to

integrate DQ issues into Semantic Web processing

by following (Wang et al., 1995)’s ideas through the

proposal of (Caballero et al., 2007), but adapted to

RDF. In the “traditional” view, data on Semantic

Web is modelled like a directed labelled graph,

wherein each node corresponds to a resource

(subjects and objects) and each arc is labelled with a

predicate. As a first approach to integrate DQ issues

in Semantic Web, what we propose in this paper is

to annotate RDF with values (metadata following

DQ nomenclature or DQMetadataAttribute

following (Caballero et al., 2007)) corresponding to

DQ dimensions (measurableConcepts) which are of

interest for the different stakeholders. This metadata

might have an objective value used to compute a

measure. Having measures for all best fitting DQ

Dimensions, machines can process an assessment for

the Semantic Web Document. This assessment

represents the perception of the DQ for a Web

Document of a stakeholder for a given application.

We have named it as Quantity of DQ (QDQ).

The QDQ could be interpreted as a weight. It

enables viewing the Semantic Web as a weighted

directed graph for a specific task and stakeholder.

This view will open new fields in machine-

processing data having as basis DQ: for instance,

Semantic Searchers can delimit the quantity of found

results, showing to the users only those whose QDQ

is within an acceptance threshold range (not only the

“relevance”); or ordering the results according to a

ranking model (Ding et al., 2005) based on DQ

requirements. Another kind of application that can

be improved is that oriented to automate a task, like

the one described for (Bernes-Lee et al., 2001).

In order to achieve this goal, several challenges

must be tackled: (1) identify which attributes must

be studied from DQ point of view, (2) how to

identify which are the proper measurableConcepts

for those resources and how to identify the necessary

metadata (when required) to make the

measurements, (3) how to get values for that

metadata and how to annotate them, and finally (4)

how to compute the QDQ according to the

perception of DQ of different groups of stakeholders

through their selected dimensions.

3.1 Identify Attributes to be Annotated

The first step in order to enable DQ in Semantic

Web consists of identifying from the Data Quality

User Requirement Specification (DQ-URS) which

elements need to be studied. The elements are

related to the level of granularity at which the study

is necessary. (Ding et al., 2005) identify the

following levels of granularity according to the

levels in which Semantic Web can be queried: RDF

Database, Semantic Web Document (SWD), RDF

subgraph or Semantic Web. According to DQXSD

by (Caballero et al., 2007), our entities are going to

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be the RDF. Their attributes are Subject, Predicates,

Object and Sentences. In this approach we are going

to focus only on sentences, our aim being to write

DQ Sentences about sentences. This process is

known as reification (Daconta et al., 2003).

For instance, let us consider the sentences

“Buddy owns business” and “business has-Website

http://www.buddy.com”-borrowed directly from

(Daconta et al., 2003) and showed in figure 3-. Let’s

imagine that somebody could be interested in

knowing, for instance, how reliable, reputable or

timely the sentences are.

Business

Buddy

Http://

www.buddy.

com

owns

has-Website

Timeliness

Reliability Timeliness

Reputation

PersonWhoProvidesData DateOfSaying

DateLastAccess FisrtApparitionInGoogle

Figure 3: DQ Dimensions for different sentences.

3.2 Identify DQ Dimensions and

Related Metadata

Once the attributes susceptible to study have been

identified, the next step is to identify the DQ

dimensions or measurable concepts that best fit the

problem of assessing DQ for these elements. The

easiest way to tackle this problem is to choose as a

guide a suitable specific DQ Model (see section 2.1)

As previously mentioned, and according to the

nature of each DQ Dimension, some metadata would

be required. Sometimes, metadata can already be

part of the RDF file or it may be necessary to add it.

For instance, in Figure 2, a stakeholder could need

two DQ dimensions (Timeliness and Reliability) to

compute the QDQ of the sentence “Buddy owns a

Business”. Some metadata complementing the

meaning of the Reliability dimension is required. Let

us suppose that it has been decided that knowing the

PersonWhoProvidesData can help to interpret and

determine if the sentence is worthy or not. Please,

note that on one hand we have the values

corresponding to metadata for measuring a DQ

Dimension by using a measurement method, and on

the other hand we have the measures of the DQ

Dimension used to calculate the QDQ for the

predicate by aggregating those values through an

indicator.

3.3 Getting and Annotating Values for

Metadata

This is the great challenge since it implies three key

aspects in measuring DQ:

(1) Who must provide these values?

(2) How and where to store these values? and

(3) How to get a representative value for

different values of the same

DQMetadataAttribute for all stakeholders

in order to calculate the QDQ?

The main response to question (1) might be

found in social annotations like those in Web 2.0

(e.g. del.icio.us or flickr) (Bao et al., 2007). This

situation enables on one hand, the possibility of

easily getting values for the same metadata, with all

the connotations and backgrounds of each user. And

on the other hand, it is possible to determine through

users’ experiences the relationships amongst the

most important DQ Dimensions when assessing the

DQ from their corresponding backgrounds in order

to create specific DQ Models for each context.

Figure 4: Conceptual Representation of DQ metadata.

To answer question (2), in Figure 4, a cube is

shown with the following information: On the X

axis, we have the DQ dimensions; the Y axis shows

all users having annotated a value; the Z axis gathers

all possible sentences of an RDF. Each individual

block stores the value V

ijk

for a metadata given for a

DQ dimension D

by a user U

for the sentence S

So, column i contains values of metadata for the DQ

dimension D

, whereas the row j gathers the values

given for a user u

for all DQ dimensions involved in

the evaluation of a sentence S

. Since not all

dimensions are implied at the same time in the

calculus of QDQ for each sentence, not all values

are required. Each block of figure 4 has been

particularized with values for the example proposed

in Figure 3. All these values must be stored together

with the RDF in a RDF Server or in a XML

DQRDFS - Towards a Semantic Web Enhanced with Data Quality

181

Database. The DQXSD proposed by (Caballero et

al., 2007) is used for describing how to attach and

store the value for metadata to their corresponding

sentence (an attribute for DQXSD).

In order to make this DQXSD operative in this

context, we have developed a counterpart RDF

Schema, which is shown in Figure 5. This Schema is

what we have named DQRDFS. As an example of

its use, figure 6 shows how the sentence “Buddy

owns a Business” and its corresponding

measurableConcepts (see figure 3) can be

represented by using the proposed DQRDFS.

<?xml version='1.0' encoding='UTF-8'?>

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-

ns#"

xmlns:dq="'http://alarcos.inf-cr.uclm.es/dqrdf/1.0'"

xmlns:rdfs="'http://www.w3.org/2000/01/rdf-schema ">

<rdfs:Class rdf:about="&dq;DQMetadataAttribute"

rdfs:label="DQMetadataAttribute">

<rdfs:subClassOf rdf:resource="&rdfs;Resource"/>

</rdfs:Class>

<rdf:Property rdf:about="&dq;DQMetadataAttributes"

rdfs:label="DQMetadataAttributes">

<rdfs:domain rdf:resource="&dq;measurableConcept"/>

<rdfs:range rdf:resource="&rdfs;Class"/>

</rdf:Property>

<rdfs:Class rdf:about="&dq;attribute"

rdfs:label="attribute">

<rdfs:subClassOf rdf:resource="&rdfs;Resource"/>

</rdfs:Class>

<rdf:Property rdf:about="&dq;attributes"

rdfs:label="attributes">

<rdfs:domain rdf:resource="&dq;entity"/>

<rdfs:range rdf:resource="&rdfs;Class"/>

</rdf:Property>

<rdfs:Class rdf:about="&dq;entity"

rdfs:label="entity">

<rdfs:subClassOf rdf:resource="&rdfs;Resource"/>

</rdfs:Class>

<rdf:Property rdf:about="&dq;id"

rdfs:label="id">

<rdfs:domain rdf:resource="&dq;entity"/>

<rdfs:range rdf:resource="&rdfs;Literal"/>

</rdf:Property>

<rdfs:Class rdf:about="&dq;measurableConcept"

rdfs:label="measurableConcept">

<rdfs:subClassOf rdf:resource="&rdfs;Resource"/>

</rdfs:Class>

<rdf:Property rdf:about="&dq;measurableConcepts"

rdfs:label="measurableConcepts">

<rdfs:domain rdf:resource="&dq;attribute"/>

<rdfs:range rdf:resource="&rdfs;Class"/>

</rdf:Property>

<rdf:Property rdf:about="&dq;name"

rdfs:label="name">

<rdfs:domain rdf:resource="&dq;DQMetadataAttribute"/>

<rdfs:domain rdf:resource="&dq;attribute"/>

<rdfs:domain rdf:resource="&dq;measurableConcept"/>

<rdfs:range rdf:resource="&rdfs;Literal"/>

</rdf:Property>

<rdf:Property rdf:about="&dq;nestedEntity"

rdfs:label="nestedEntity">

<rdfs:domain rdf:resource="&dq;DQMetadataAttribute"/>

<rdfs:range rdf:resource="&rdfs;Class"/>

</rdf:Property>

<rdf:Property rdf:about="&dq;value"

rdfs:label="value">

<rdfs:domain rdf:resource="&dq;DQMetadataAttribute"/>

<rdfs:range rdf:resource="&rdfs;Literal"/>

</rdf:Property>

</rdf:RDF>

Figure 5: DQRDFS: a RDF Schema supporting DQ.

For the last question, it is important to realise

that having several values for each metadata, it is

necessary to give a global representative value for

all provided values. If given values are numbers, an

example of this representing global value could be

an arithmetic mean; should these values for metadata

be subjective linguistic labels, then an aggregation

method is required, like the one proposed by

(Herrera-Viedma et al., 2006).

<?xml version="1.0"?>

<rdf:RDF [...]

xmlns:dq=“http://alarcos.inf-cr.uclm.es/ontologies/dqmo#“ >

<rdf:Description>

<earl:asserts rdf:parteType=’Resource’>

<rdf:subject>

<rdf:Description rdf:about=“#Buddy”></rdf:subject>

<rdf:predicate>

<RDFNSId:owns><RDFNSId=’#business’>

</rdf:predicate>

</earl:asserts>

<dq:entity rdf:about="&dq;entity_Instance"

dq:id="entity1"

rdfs:label="entity_Instance">

<dq:attributes rdf:resource="&dq;sentence_Instance"/>

</dq:entity>

<dq:attribute rdf:about="&dq;sentence_Instance"

<dq:name="sentence1" rdfs:label="sentence_Instance">

<dq:measurableConcepts

df:resource="&dq;measurableConcept_Instance_1"/>

<dq:measurableConcepts

rdf:resource="&dq;measurableConcept_Instance_2"/>

</dq:attribute>

<dq:measurableConcept

rdf:about="&dq;measurableConcept_Instance_1"

dq:name="Reliability"

rdfs:label="measurableConcept_Instance_1">

<dq:DQMetadataAttributes

rdf:resource="&dq;metadataAtt_Instance_1"/>

</dq:measurableConcept>

<dq:measurableConcept

rdf:about="&dq;measurableConcept_Instance_2"

dq:name="Timeliness"

rdfs:label="measurableConcept_Instance_2">

<dq:DQMetadataAttributes

rdf:resource="&dq;metadataAtt_Instance_2"/>

</dq:measurableConcept>

<dq:DQMetadataAttribute

rdf:about="&dq;metadataAtt_Instance_1"

dq:name="PersonWhoProvidesData"

rdfs:label="metadataAtt_Instance_1">

<dq:value> “Uge”</dq:value>

<dq:value> “Coral”</dq:value>

</dq:DQMetadataAttribute>

<dq:DQMetadataAttribute

rdf:about="&dq;metadataAtt_Instance_2"

dq:name="DateOfSaying"

rdfs:label="metadataAtt_Instance_2">

<dq:value> “01/09/07”</dq:value>

<dq:value> “08-08-2007”</dq:value>

</dq:DQMetadataAttribute>

</rdf:Description>

</rdf:RDF>

Figure 6: An example of DQRDF.

3.4 Calculating QDQ

Once representative values for each measurable

concept have been obtained, the next step is to

calculate the QDQ for each sentence. As can be seen

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in figures 3 and 4, for each QDQ a set of DQ

Dimensions is involved. The value of QDQ, as

previously said, must be calculated by aggregating

the corresponding measures for the required

measurable Concepts as an Indicator (ISO/IEC,

2000), taking into account the relationships between

the different DQ Dimensions. An interesting

proposal for calculating the QDQ, which takes into

account the possible relationship between DQ

Dimensions, is the one by (Caro et al., 2007), in

which a Bayesian Network (BN) is implemented for

their own DQ model for calculating the level of

Representational DQ of Educational Web Portals.

4 CONCLUSIONS

This paper has introduced some fundamentals of DQ

and has highlighted the importance of annotating

DQ issues of Semantic Web in order to have an

improved web through QDQ Concept. This QDQ

enables a view of the Web as a weighted directed

graph which would open new challenges in

machine-processing Semantic Web Documents in

order to optimize users’ satisfaction. In the future we

will deal with refining and validating the DQRDFS.

A study of how to extend the proposal to the

remainder of the elements of RDF is also planned.

ACKNOWLEDGEMENTS

This research is part of the projects ESFINGE

(TIN2006-15175-C05-05) supported by the Spanish

Ministerio of Educación y Ciencia and MECENAS

(PBI06-0024) supported by the Consejería de

Educación y Ciencia of Junta de Comunidades de

Castilla – La Mancha.

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