An Approach for Generating and Semantically Enriching Dataset
Profiles
Natacha Targino
1
, Damires Souza
2
and Ana Carolina Salgado
1
1
Centro de Informática (CIn) - Federal University of Pernambuco (UFPE), Recife, Pernambuco, Brazil
2
Federal Institute of Paraíba (IFPB), João Pessoa, Paraíba, Brazil
Keywords: Datasets on the Web, Dataset Profile, Metadata, Semantic Enrichment, Data Publication and Consumption.
Abstract: The identification of appropriate datasets on the Web for a given task is still a challenge. To help matters,
metadata describing datasets can be provided. It is possible to make these metadata available through a
Dataset Profile (DSP). In this light, this work presents an approach which generates a DSP composed by
descriptive, structural and quality metadata. The DSP is enriched by semantically referencing the provided
metadata and by means of some new metadata, such as the dataset domain and some quality metadata, e.g.
comprehensibility and processability. In order to evaluate the proposed approach, a prototype has been
developed and some experiments have been accomplished.
1 INTRODUCTION
The large amount of datasets available on the Web
enables their usage in diverse scenarios, and
combinations of these datasets can bring important
findings. However, publishers and consumers of
datasets on the web usually do not know each other.
This implies in a fundamental need for a common
understanding between dataset publishers and
dataset consumers. Without this comprehension,
dataset publishers' efforts may be incompatible with
dataset consumers' needs (Lóscio et al., 2017).
In order to facilitate that communication,
metadata regarding the datasets are usually made
available (Clarke et al., 2014). Dataset metadata can
improve the understanding and processing of the
data, both by humans and by machines. Metadata are
usually composed by descriptive information about
the content, structure, quality, and other
characteristics of the datasets. Nevertheless, it is not
yet a common practice for publishers to provide
metadata that completely represent the content of
published datasets (Abele, 2016). Recommended as
a best practice by the World Wide Web Consortium
(W3C) (Lóscio et al., 2017), providing metadata is a
fundamental requirement when publishing datasets
on the Web. It may help humans and machines not
only to understand the data but also important
aspects that describe their distributions (i.e., physical
forms of a dataset).
To help matters, data enrichment processes can
be performed on the metadata in order to generate
new metadata and help in assigning meaning to
them. In this latter case, the metadata can be
semantically referenced by terms of recommended
vocabularies available on the Web (Heath et al.,
2011). For example, considering a dataset metadata
"date of publication", it can be replaced by
dct:issued, a term which belongs to the Dublin Core
Metadata Initiative (DCTerms) vocabulary. In
addition, representing the metadata in semantic data
formats, such as the Resource Description
Framework (RDF) model, can facilitate their
processing and understanding by data consumers.
In order to enable the structuring of dataset
metadata, some authors propose the creation of
dataset profiles (Abele, 2016; Assaf et al., 2015).
However, most of existing dataset profile examples
only provide descriptive metadata. Thus, the need of
an approach that may provide more detailed
information about datasets on the web by means of
enriched profiles is evidenced.
This work presents an approach, called DSPro+
(DataSet Profile with Enrichment), which describes
datasets published on the Web by means of the
creation of enriched Dataset Profiles (DSP). It
extends the DSP generation approach introduced in
Targino et al., (2017). The main objective of this
proposal is to facilitate the understanding between
dataset publishers and dataset consumers. The DSP
90
Targino, N., Souza, D. and Salgado, A.
An Approach for Generating and Semantically Enriching Dataset Profiles.
DOI: 10.5220/0007187400900101
In Proceedings of the 14th International Conference on Web Information Systems and Technologies (WEBIST 2018), pages 90-101
ISBN: 978-989-758-324-7
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

is composed by: (i) descriptive metadata, which
includes keywords, title, knowledge domain and a
recommendation of domain vocabularies; (ii)
structural metadata, which describes the internal
structure of a dataset; and (iii) quality metadata,
which regards quality criteria concerned with dataset
comprehensibility and processability. The generated
dataset profile is represented in machine readable
format (RDF), facilitating its manipulation.
Our contributions are summarized as follows:
(i) We present an approach to generate
enriched dataset profiles;
(ii) We extract most descriptive metadata
which are already available in a dataset;
(iii) We include some new descriptive
metadata and also present structural ones;
(iv) We propose the definition of quality
criteria associated with a dataset which
are also included in a DSP;
(v) We provide a prototype that implements
the proposed approach; and
(vi) We describe some experiments that
evaluate the proposed approach and,
particularly, the semantic enrichment
process.
This paper is organized as follows: Section 2
introduces some concepts; Section 3 proposes the
approach; Section 4 presents the obtained results;
Section 5 discusses related work, and Section 6
exposes some conclusions and future work.
2 FUNDAMENTAL CONCEPTS
Nowadays, the web may be considered as an
appropriate ecosystem for production and
consumption of datasets. In open data portals, such
as the European Union Open Data Portal
(https://open-data.europa.eu/) and the Ireland's Open
Data Portal (https://data.gov.ie/), some metadata are
added to the published datasets. These metadata
usually include dataset creation date, defined usage
license, and dataset formats or distributions. Some
open data portals provide data APIs as well.
Regarding the common provided metadata, we
argue that they can be enriched to generate better
descriptions of the datasets. A data enrichment
process, in general, refers to a set of tasks that can be
used to enhance, refine or improve raw or previously
processed data (Lóscio et al., 2017). In this sense, it
is possible to include, for instance, the identification
of the knowledge domain of a dataset (e.g., Health,
Music, Education) or even some related quality
criteria in terms of metadata.
The quality of a dataset can have a big impact on
the quality of applications that use it (Lóscio et al.,
2017). Thus, Information Quality (IQ) criteria may
be defined on datasets in order to enrich their
suitability for specific usages. The notion of IQ has
emerged during the past years and shows a steadily
increasing interest. IQ is based on a set of
dimensions or criteria. The role of each one is to
assess and measure a specific quality issue
(Naumann et al., 2000; Wang et al., 1996; Pipino et
al., 2002). Examples of IQ criteria concerned with
datasets on the web are timeliness, consistency,
verifiability and comprehensibility (Zaveri et al.,
2013; Naumann et al., 2000; Flemming, 2011). Each
quality criterion has a set of indicators, which allows
the evaluation of the quality of a data source
(Flemming, 2011). In this work, we consider two IQ
criteria named as comprehensibility and
processability of a dataset, which are defined in
Section 3.3.
Given the need to provide better descriptions of
datasets, some works have used dataset profiles for
this purpose. Abele (2016) defines data profiling as
the process of creating descriptive information and
collecting statistics about the dataset. For Ellefi et
al., (2014) a dataset profile is a set of characteristics,
both semantic and statistical, that allow to describe
in the best possible way a dataset.
3 THE DSPro+ APPROACH
The DSPro+ approach has been defined as a means
to generate a dataset profile with some kinds of
metadata enrichment. Considering the related works
and according to the indications of good practices
for data publication on the Web from W3C (Lóscio
et al. 2017), this work defines some concepts. In this
work, a dataset is defined as follows.
Definition 1. Dataset (ɗ) - A dataset ɗ represents a
collection of data published on the Web that is
available for access through a distribution.
A distribution of a dataset represents a specific
way (e.g., CSV, API) in which a dataset ɗ is made
available to consumers. The same dataset ɗ may be
available in one or more distributions.
The main idea underlying this work is that
datasets are published and searched for
consumption. To facilitate this task, we associate a
dataset with a profile, which is defined as follows.
An Approach for Generating and Semantically Enriching Dataset Profiles
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Definition 2. Dataset Profile (DSP (ɗ)) - A Dataset
Profile DSP(ɗ) consists of a body of semantically-
enriched metadata regarding a dataset ɗ and
composed by descriptive, structural and quality
information on ɗ.
The DSP generation aims to provide a better
understanding of a dataset and its content, making it
more accessible and understandable by both people
and machines. The proposed approach is composed
of some steps, such as data extraction, metadata
identification, generation of new metadata, and
formation of DSP. Figure 1 illustrates the process
that underlies the approach. As depicted in Figure 1,
it receives as input a dataset published on the web,
which can be accessed through its URL, and extracts
its data. Then, it identifies some metadata, which are
originally included in a dataset. These metadata may
be available in different forms, such as a specific
file, in HTML or through a JSON-LD script. Thus,
usually, it is possible to directly obtain some
descriptive metadata, such as a dataset title, its
description and date of publication.
Figure 1: Process for the generation of a DSP(ɗ).
Next, new descriptive metadata (domain and
vocabulary recommendation) as well as structural
and IQ metadata are generated (steps explained in
the following sections). Then a DSP is built in
accordance with the generated and identified
metadata. The resulting metadata are semantically
referenced by means of recommended vocabulary
terms. To this end we use vocabularies, as the Data
Catalog Vocabulary (DCAT) and Data Quality
Vocabulary (DQV), which are recommendations
provided by the W3C (Lóscio et al., 2017).The
resulting DSP is serialized in an RDF distribution.
3.1 Descriptive Metadata
In this work the concept of descriptive metadata is
stated as follows.
Definition 3. Descriptive Metadata (DM(ɗ)) -
Descriptive metadata DM(ɗ) represent information
about the overall features of a dataset ɗ.
In Table 1, the stated DM(ɗ) for a DSP(ɗ) and
their corresponding vocabulary terms are presented.
Some of these metadata regard the scheme proposed
by the W3C Best Practices (Lóscio et al., 2017),
which are the following: title, description, keywords,
date of last modification, date of publication,
publisher, domain (theme) and distribution. The
other ones are new and part of our proposal.
Table 1: DM(d).
Metadata Vocabulary Term
Identifier dcterms:identifier
Title dcterms:title
Description dcterms:description
Keyword dcat:keyword
Domain dcat:theme
Domain.name rdfs:label
Domain.URI void:uriSpace
Domain.vocabulary void:vocabulary
URL Address
dcat:landingPage
Date of last Modification
dcterms:modified
Date of publication dcterms:issued
Publisher dcterms:publisher
Version owl:versionInfo
Distribution dcat:distribution
Distribution.format dcterms:format
Distribution.size dcat:byteSize
Distribution.URL dcat:downloadURL
Distribution.type dcat:mediaType
Date of DSP creation dcterms:created
The values of some descriptive metadata
(identifier, title, description, keywords, URL
address, date of last modification, date of
publication, publisher, version, and distribution) are
derived of the information extracted directly from
datasets. However, it is not always possible to find
keywords. Since they are used in other phases of the
metadata generation process, it is possible to find the
most frequent terms of a dataset using the TF-IDF
metric (Targino et al., 2017). As a result, a set of
dataset keywords is obtained.
Information about recommended domain
vocabularies and the knowledge domain of a dataset
are also not usually found. Thereby these two
specific metadata are contributions of this work.
Their definitions are established as follows.
Definition 4. Domain Identification (DI(ɗ)) - DI(ɗ)
refers to the identification of a knowledge domain to
which a dataset ɗ belongs.
Examples of data knowledge domains are Music
and Education. For the identification of a given
domain, a semantic background knowledge can be
used. In this work, we use the DBpedia ontology
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92

since it provides the classification of a huge amount
of knowledge domains. To this end, by using the
dataset keywords, corresponding classes or
properties of the ontology are identified. When a
property is returned, its corresponding class is then
identified. Thereby, a domain term is returned based
on the most frequent class among all the obtained
class results (Targino et al., 2017).
Definition 5. Recommendation of Domain
Vocabularies (RDV(ɗ)) - RDV(ɗ) refers to a
suggestion of domain vocabularies which are
considered as appropriate for a dataset ɗ.
The vocabulary recommendation can, for
example, help data conversion processes when
transforming source data (e.g., in CSV formats) into
target RDF ones. This recommendation makes use
of an open vocabulary repository as background
knowledge. During this process, vocabularies related
to the identified dataset keywords are identified.
Among the identified vocabularies, the ones that
have the highest number of occurrences and which
are preferably active are prioritized. As a result, the
best ranked vocabulary is recommended as RDV(ɗ).
If there are more than one vocabulary presenting the
higher and same number of occurrences, all of them
are provided as recommendations (Targino et al.,
2017).
3.2 Structural Metadata
Structural metadata are defined as follows.
Definition 6. Structural Metadata (SM(ɗ)) -
Structural metadata SM(ɗ) describe the internal
structure of a dataset ɗ in terms of its properties.
In a DSP(d), the SM(ɗ) are organized through a
specific scheme, as described in Table 2. To this
end, each element of the scheme is semantically
referenced by a recommended vocabulary term.
Table 2: SM(ɗ).
Metadata Vocabulary Term
Number of properties void:properties
Property void:property
Property.name rdfs:label
Property.type dcterms:type
3.3 Quality Metadata
Since IQ is based on a set of criteria, we need do
identify and define ways of assessing specific
quality indicators related with datasets on the web.
We define a Quality Indicator as follows.
Definition 7. Quality Indicator
(

(
ɗ
)

)
- A
quality indicator QI
(
ɗ
)

 represents a measurable
characteristic of a dataset ɗ that is related with the
quality of its data or metadata.
Quality indicators may provide information
regarding data content, data meta-information, and
human ratings that give indications about the
suitability of datasets for some intended usage. We
argue that a number of specific quality indicators
may be assessed in order to produce IQ measures.
IQ measures are defined in this work as Quality
Metadata, as follows.
Definition 8. Quality Metadata (
(
ɗ
)
) –
Quality Metadata QM
(
ɗ
)
correspond to specific
quality criteria related with a given dataset ɗ, which
are assessed from a set of quality indicators I(ɗ).
Considering issues related with datasets
publication and consumption and also with benefits
suggested by the W3C Best Practices (Lóscio et al.,
2017), two IQ criteria have been proposed in this
work, namely: (i) dataset comprehensibility, and (ii)
dataset processability. These IQ criteria are
explained in the following sections.
3.3.1 Dataset Comprehensibility
Dataset comprehensibility is an IQ criterion which
takes into account quality indicators related with the
understanding of a given dataset by humans. It is
defined as follows.
Definition 9. Comprehensibility (C(ɗ)) – The
comprehensibility of a dataset ɗ, denoted by C(ɗ), is
stated as the degree to which ɗ presents information
that promotes or facilitates its understanding by
human users. C(ɗ) is measured from a set of quality
indicators QI
(
ɗ
)

in such a way that:

(
ɗ
)
=
∑

(
ɗ
)



#
(
ɗ
)
(1)
Where,

(
ɗ
)

is the value of a quality indicator
related with C
(
ɗ
)
.
#
(
ɗ
)
is the number of quality indicators
associated withC
(
ɗ
)
.
The idea is that humans can have a better
understanding about a given dataset on the web if
some quality indicators are provided. Information
regarding the dataset structure and its descriptive
metadata are examples of quality indicators which
may help such comprehension.
An Approach for Generating and Semantically Enriching Dataset Profiles
93

The comprehensibility criterion C
(
ɗ
)
is
measured from a set of six quality indicators

(
ɗ
)

, namely: (i) Descriptive metadata; (ii)
Structural metadata; (iii) Descriptive metadata
referenced semantically; (iv) Dataset in an RDF
distribution; (v) Metadata in an RDF distribution;
and (vi) Contact point. They are described in the
following.

(
ɗ
)

: Descriptive metadata

(
ɗ
)

=
#DM
(
ɗ
)

#DM

(2)
Where,
#DM
(
ɗ
)

is the amount of desirable
descriptive metadata (DM

) found in dataset ɗ;
#DM

is the amount of desirable
descriptive metadata that can describe the dataset ɗ.
For a dataset ɗ, a certain quantity of descriptive
metadata (DM) should be made available. The idea
is to describe a dataset overall features, in such a
way that its description may improve its
understanding. Based on this idea and in accordance
with suggestions provided by the W3C (Lóscio et
al., 2017), the following descriptive metadata are
considered as desirable to a dataset, namely: title,
keywords, URL address, date of publication, date of
last modification, publisher, and its distribution.

(
ɗ
)

: Structural metadata

(
ɗ
)

=
#SM
(
ɗ
)
#ƥ
(
ɗ
)
(3)
Where,
#SM
(
ɗ
)
is the amount of structural
metadata (SM) provided by the description of
dataset ɗ ;
#ƥ(ɗ) is the number of properties which
exists in ɗ structure .
Structural metadata (SM(ɗ)) should be made
available to describe the properties that compose a
dataset ɗ. Information about all the properties of a
dataset should be provided.

(
ɗ
)

: Descriptive metadata referenced
semantically

(
ɗ
)

=
1
0
(4)
Where,
1 means that the desired descriptive
metadata (DM

) provided by dataset ɗ are
semantically referenced by recommended
vocabularies;
0 means that the desired descriptive
metadata (DM

) provided by dataset ɗ are not
semantically referenced by recommended
vocabularies.
Desirable descriptive metadata (DM

) provided
by a dataset ɗ should be referenced by recommended
vocabularies. Such provided semantics increases the
metadata discovery and consumption capacity. One
of the current recommendations regards the use of
the DCAT vocabulary.

(
ɗ
)

: Dataset in an RDF distribution

(
ɗ
)

=
1
0
(5)
Where,
1 Dataset ɗ is available in an RDF
distribution;
0 Dataset ɗ is not available in an RDF
distribution.
A dataset should be also available in a
distribution format with an RDF serialization. In
RDF, each resource and vocabulary are identified by
URIs, which eliminate ambiguities. Also, data in
RDF are semantically described and have usually
named links. This practice provides not only the
right meaning of a resource but also possible
relationships with other ones.

(
ɗ
)

: Metadata in an RDF distribution

(
ɗ
)

=
1
0
(6)
Where,
1 Metadata is available in an RDF
distribution;
0 Metadata is not available in an RDF
distribution.
The metadata corresponding to dataset ɗ are
available in an RDF format. The RDF model is
indicated for the representation of metadata, since it
allows to formally define its semantics. It also
facilitates the location and access of datasets.

(
ɗ
)

: Contact point

(
ɗ
)

=
1
0
(7)
Where,
1 A contact point is provided by the dataset
publisher, thus enabling data consumers to get in
touch;
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94

0 A contact point is not provided by the
publisher.
The data publisher should provide means to easy
communication with dataset consumers. As an
illustration, emails or contact forms may be
provided.
3.3.2 Dataset Processability
In order to enable machines to automatically process
data within a dataset, it is important that a dataset
publisher applies some good practices (Lóscio et al.,
2017). Examples of good practices are the
following: providing structural and descriptive
metadata; making use of recommended
vocabularies; providing data in more than one
machine readable format; and making data available
through APIs. In accordance with these
recommendations, the dataset processability
criterion has been established as follows.
Definition 10. Processability (P(ɗ)) - The
processability of a dataset ɗ, denoted by P(ɗ),
measures the degree to which ɗ is processable by
machines or software agents. P(ɗ) is assessed from a
set of quality indicators QI
(
ɗ
)

in such a way that:

(
ɗ
)
=
∑

(
ɗ
)



#
(
ɗ
)
(8)
Where,

(
ɗ
)

is the value of a quality indicator
of P
(
ɗ
)
.
#
(
ɗ
)
is the number of quality indicators
of P
(
ɗ
)
.
The processability criterion P
(
ɗ
)
is measured
from a set of five quality indicators, namely: (i) Data
API; (ii) Structural metadata; (iii) Distributions in
machine readable formats; (iv) Dataset download;
and (v) Dataset in more than one distribution. They
are explained in the following:

(
ɗ
)

: Data API

(
ɗ
)

=
1
0
(9)
Where,
1 A data API is available for access to
dataset ɗ;
0 A data API is not available for access to
dataset ɗ.
Among the distributions of a dataset, an API
should be made available. This makes data
processing and accessibility more feasible. It also
provides means to use real-time data.

(
ɗ
)

: Structural metadata

(
ɗ
)

=
#SM
(
ɗ
)
#ƥ
(
ɗ
)
(10)
Where,
#M(ɗ) is the amount of structural
metadata (SM
(
ɗ
)
) made available by dataset ɗ;
#ƥ(ɗ) is the number of properties found in ɗ
structure.
As explained in 
(
ɗ
)

, Structural metadata
(SM(ɗ)) are relevant information when trying to
easy comprehension and processability of datasets.

(
ɗ
)

: Distributions in machine readable
formats

(
ɗ
)

=
1
0
(11)
Where,
1 The dataset ɗ is available in distributions
with machine readable file formats;
0 The dataset ɗ is not available in
distributions with machine readable file formats.
A dataset ɗ is usually made available through
distributions. Distributions must make data available
in file formats that are more easily processed by
machines, such as JSON, RDF, and CSV. This good
practice may help applications to process data.

(
ɗ
)

: Dataset download

(
ɗ
)

=
1
0
(12)
Where,
1 Allows the download of dataset ɗ;
0 Does not allow the download of dataset ɗ.
Dataset ɗ is available for download, increasing
the possibilities of processing and using its data.

(
ɗ
)

: Dataset in more than one
distribution

(
ɗ
)

=
1
0
(13)
Where,
1 The dataset ɗ is available in more than
one distribution;
An Approach for Generating and Semantically Enriching Dataset Profiles
95

0 The dataset ɗ is not available in more than
one distribution.
Dataset ɗ should be available in more than one
distribution. For example, it may be provided in
open formats such as XML, RDF, JSON and/or
CSV. This practice increases the chances of data
consumption, since data consumers may have
preferences on the data formats.
4 IMPLEMENTATION AND
EXPERIMENTS RESULTS
This section presents some results regarding the
implementation of the proposed approach and
accomplished experiments.
4.1 The DSPro+ Tool
The DSPro+ tool allows the user to automatically
generate, view and download a DSP. The main
functionalities available through the tool are
summarily described as follows.
• Search Dataset: It provides a keyword-based
method for searching one or more datasets
that have previously had their DSP generated.
• Query Dataset Information: The tool can
retrieve information about a selected dataset.
• Validate Dataset URL: Before starting the
DSP generation process, the user must
provide the URL of a dataset. The tool then
verifies if it is a valid URL which can enable
the extraction of its underlying information.
• Generate DSP: After validating a dataset
URL, the user can start the process of
generating a DSP. Information regarding the
dataset and the results will be depicted at the
end of the process.
• View DSP: The user can view a DSP
generated by the tool, which contains
descriptive, structural and quality metadata of
a given dataset.
• Download DSP: The user can download a
DSP in an RDF format.
As an example of usage, consider a dataset called
“Rolling Stone's 500 Greatest Albums of All Time”
(Kaggle, 2018), which is available in CSV. Most
descriptive metadata can be directly extracted from
the dataset at hand. Thus, the dataset title, identifier,
description, keywords, URL address, date of last
modification, date of publication, publisher, version,
and distribution are obtained and saved for later use.
The recommendation of domain vocabularies to
that dataset is then performed. To this end, SPARQL
queries are built and executed in order to find
corresponding vocabularies properties to the dataset
properties or keywords. These queries are executed
on the Linked Open Vocabularies endpoint (LOV,
2018), i.e., a web service provided by that open
vocabulary catalog. In this current example, the
vocabulary that presented the highest number of
occurrences among the obtained queries results was
“The Music Ontology”. Thereby, this vocabulary is
the one to be recommended to the example dataset.
For the knowledge domain identification, dataset
keywords are also used to compose SPARQL
queries. These queries are now accomplished on the
public DBPedia endpoint. As a result, a class named
“Musical Work” is identified as the knowledge
domain to the dataset, which had the highest number
of occurrences among the obtained class results.
According to the extracted dataset information,
the properties that compose its structure are
identified. For a dataset in CSV format, the header
line of the file is identified and its columns names
are extracted. Regarding the example, six properties
have been identified: number (numeric), year
(numeric), album (string), artist (string), genre
(string), subgenre (string). Thus, the dataset
structural metadata are produced.
Quality metadata are also generated in
accordance with the information collected from the
dataset. Each quality criterion is assessed according
to its set of quality indicators. Each quality indicator
is measured based on the information provided by or
together with the dataset (e.g., when there is a
profile). Then, corresponding results from each one
are put together in Formula 1 and Formula 8.
Regarding the IQ criterion Comprehensibility,
among the quality indicators for the dataset at hand,
the great majority of them were completely met.
Only the indicator concerned with the existence of
the dataset as an RDF distribution was not found.
Thereby C(ɗ) was measured as 0.83. Referring to
Processability, quality indicators with respect to
providing a data API and more than one distribution
were not met. The other indicators were successfully
served. Consequently, P(ɗ) was measured as 0.6.
The DSP metadata generated are made available
in RDF format. Figure 2 shows a fragment of the
generated DSP for the example at hand. This
fragment corresponds to some descriptive metadata
(identifier, dataset title, description, keywords,
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identified domain, recommended vocabulary) and
the defined quality metadata (i.e., comprehensibility
and processability criteria), where each criterion is
instantiated and specified with its label, value and
definition. The obtained DSP uses recommended
vocabularies to refer the metadata, thus enabling its
consumption or publication along with the dataset.
This feature may be provided in Open Data portals,
for example.
Figure 2: Fragment of a generated DSP for the example,
corresponding to some DM(ɗ) and QM(ɗ).
4.2 Experiments
We have conducted some experiments to verify the
effectiveness of our approach. To this end, we have
used 30 CSV datasets provided in the English
language. They have been divided into three groups
of 10 datasets, where each one belongs to the
following knowledge domains: “Video Games”,
“Automobiles”, and “Music”. These datasets are
made publicly available on Kaggle (Kaggle, 2018).
The data and metadata from these datasets can be
accessed through their page. Some descriptive
metadata are represented in JSON-LD scripts, which
uses the vocabulary Schema.org.
The proposed DSP+ approach takes into account
different aspects (e.g., knowledge domain
identification, dataset quality) in order to produce a
DSP. Thus, it was not possible to find a single
baseline that could be used to accomplish
experiments and compare results. Instead, four kinds
of evaluations have been defined and accomplished,
as follows.
The goal of the first experiment is to verify
whether the generated DSP represents a more
comprehensive and semantically rich description of
a given dataset. To this end, the original descriptive
set of metadata (made available through JSON-LD
scripts in datasets) is compared with the produced
metadata of the DSPro+ approach. As shown in
Figure 3, a larger amount of metadata is provided
from the generated DSP. Furthermore, these
generated metadata consider more aspects of a
dataset, generating new descriptive metadata (e.g.,
domain and recommended vocabulary), as well as
structural and quality ones. In the available JSON-
LD scripts, only descriptive metadata are provided.
Usually they regard to the dataset title, description,
identifier, version, and some information, such as a
set of comments and number of downloads.
For the second and third experiments, gold
standards have been manually produced by domain
users of our group. In order to verify the results, we
have used the traditional Precision, Recall and F-
Measure metrics (Baeza-Yates et al. 1999).
The second experiment aims to check whether
the automatic identification of the knowledge
domain of a dataset presents a similar result in
comparison with its manual identification by a
human. It has been observed that obtained keywords
belonging to datasets of a same knowledge domain
group can have differences among them and this can
directly affect the results. During the comparative
analysis, it was found that among the 30 datasets
used in the experiment, only four of them did not
receive the recommendation according to the
domain recommended by the specialists. As depicted
in Figure 4, considering the domain identification
for datasets on “Video Games”, all of them
corresponded to the gold standard, resulting in a
value of 1 for Precision, Recall and F-measure. For
the “Music” group, two datasets have not received
the expert-defined domains, resulting in a value of
0.8 for Precision, Recall, and F-Measure metrics. In
the “Automobile” group, eight datasets presented the
identified domains in accordance with the gold
standards, but for one dataset does not have been
identified more than one domain as expected
according to the gold standard. The following values
were obtained: Precision = 0.8, Recall = 0.67 and F-
measure = 0.73.
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Figure 3: Comparison between the metadata provided by the generated DSPs and the metadata provided by the datasets.
The third experiment verifies whether the
automatic recommendation of domain vocabularies
for a dataset presents a similar result when compared
with the recommendation provided by humans. It
was observed that, in many cases, the approach
recommends more than one vocabulary. This also
occurred among the gold standards generated by the
experts. However, due to the number of vocabularies
that are related to the same knowledge area, among
the results obtained for each dataset, not all the
recommended vocabularies sometimes correspond to
all the gold standard vocabularies. As shown in
Figure 5, among the results of the “Video Games”
group, the vocabularies defined as the gold standard
were recommended for all datasets, but except for
one dataset, only one vocabulary was recommended
diverging from the gold standard, resulting in
Precision = 1, Recall = 0.91 and F-Measure = 0.95.
Considering the datasets of the “Music” group,
seven of them obtained the recommendation of
vocabularies defined as the gold standard. However,
for some of them, none of the expected vocabularies
were recommended, resulting in Precision = 0.73,
Recall = 0.85 and F-Measure = 0.78. Among the
datasets of the “Automobile” group, only four
datasets presented some vocabulary corresponding
to the one recommended by the specialists, with
values of Precision = 0.21, Recall = 0.31 and F-
Measure = 0.25. In these obtained results, it has been
noted that when the dataset belongs to a more
specific knowledge area, its properties and keywords
are very specialized. Thus, most suitable
vocabularies are found, making it more likely to
achieve the expected outcomes, as observed in the
light of the "Video Games" group.
The fourth experiment intends to measure the
degree of comprehensibility and processability of
the datasets. To this end, we have performed two
evaluations: (i) considering information originally
provided by the datasets and measuring both IQ
criteria and (ii) considering the information
originally provided by the datasets plus the
information provided by the generated DSP and
measuring both IQ criteria. In these evaluations, a
significant improvement was observed between the
values assigned to the quality criteria after the
profile generation. Then, we compared the obtained
results.
Figure 4: Measures w.r.t. the Domain Identification.
Figure 5: Measures w.r.t. the Vocabulary
Recommendation.
As shown in Figure 6, for the comprehensibility
criterion of a dataset, an improvement of at least
33% in the value received after the DSP generation
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Figure 6: Dataset Comprehensibility Measures before and after the generation of DSPs.
Figure 7: Dataset Processability Measures before and after the generation of DSPs.
has been obtained. This occurred because before the
DSP generation there were no metadata available in
RDF format and the metadata were not available
using recommended vocabularies.
For the processability criterion of a dataset
(Figure 7), no significant improvements were
observed when analyzing the datasets as a whole,
since most datasets already provided structural
metadata on their page. For this criterion,
improvements were observed when before the DSP
generation there were no structural metadata
provided by the dataset. Thus, it resulted in a small
variation between the outcomes obtained before and
after the DSP generation. However, when a specific
dataset is observed, significant improvements are
identified. For example, dataset 23, that presented a
processability of 0.4 before the generation of DSP,
improved to a value of 0.6 after the profile
production, resulting in 20% of improvement in the
processability criterion.
5 RELATED WORKS
The generation of datasets profiles with enriched
metadata is an activity composed of different phases,
which consider the generation of descriptive,
structural and quality metadata. Considering
semantic enrichment, more specifically the
recommendation of vocabularies, the works of Ellefi
et al., (2015) and Schaible et al., (2013) present
approaches for recommending vocabularies for each
concept of a data source. In our work, vocabularies
are identified associated with the properties of the
dataset, but what is recommended are domain
vocabularies, without the need of user assistance.
Among the works related to domain
identification, the works of Ouksili et al. (2014) and
Lalithsena et al. (2013) present approaches to
identify the domains of datasets. However, in these
works it is possible to identify the domain only for
datasets in RDF format. Also, metadata about the
identified domain that could be made available to
users are not generated.
Considering the works that use IQ criteria to
evaluate the quality of datasets, in the work of Assaf
et al. (2016) a framework was developed to evaluate
the quality of connected datasets through quality
criteria and indicators. Although the quality
framework is related to the generation of a profile,
the results obtained related to the dataset quality are
not inserted in the profile. Also the processability
criterion is not considered.
In terms of dataset generation, in the work
produced by Abele (2016), two approaches are
proposed for the generation of metadata representing
the content of the datasets and for the identification
of connections between the datasets. The work
considers only datasets in RDF format and, for the
generation of metadata, only descriptive and
structural aspects are considered. The work
presented by Assaf et al. (2015) proposes an
approach for extracting, validating, correcting and
generating data profile, which is generated in JSON
format. However, structural or quality metadata are
not generated. When comparing works related to
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DSP generation, such as Abele (2016) and Assaf et
al. (2015), an approach which provides more
detailed information about datasets, including
descriptive, structural, and quality metadata is not
found. In addition, some of them do not use
vocabulary terms associated to the metadata
provided by the profile. This allows to assign more
meaning and a representation of the metadata which
facilitates its consumption.
6 CONCLUSIONS
In this work, we have presented an approach for the
generation of semantically enriched Dataset Profiles.
To help matters, a DSP composed of descriptive,
structural and quality metadata is proposed. During
the DSP generation process, some metadata are
extracted from the datasets, and, additionally, the
dataset domain is identified and domain
vocabularies are suggested. Furthermore, the process
includes the generation of structural metadata and
quality metadata, which proposes two IQ criteria to
be measured as relevant and additional information.
The main idea of providing enriched DSPs is to
facilitate the communication between dataset
publishers and consumers (humans and machines).
In order to evaluate the proposed approach, a
prototype has been implemented. It provides an
automatic DSP generation process. The tool assists
data producers who wish to make DSPs available to
certain datasets. Dataset consumers can also
generate a DSP, without the need of prior knowledge
about the data.
The experiments used datasets from different
knowledge domains. They demonstrated that the
proposed strategy produces good results, by
allowing the generation of new metadata.
Improvements were also observed with respect to
the quality of the datasets after the DSP generation.
As future works, we consider to include user
feedback and other IQ criteria (e.g., completeness,
correctness), to link the approach to an existing
dataset catalog, and also to include in the DSP the
recommendation of vocabularies for each identified
structural metadata. New experiments with expert
users and datasets belonging to a wider range of
domains will also be accomplished.
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