Publishing and Consuming Semantic Views for Construction of

Knowledge Graphs

Narciso Arruda

1 a

, Amanda D. P. Venceslau

1 b

, Matheus Mayron

, V. M. P. Vidal

and V. M. Pequeno

2 c

Departamento de Computação, Federal University of Ceará, Fortaleza, Ceará, Brazil

TechLab, Departamento de Ciências e Tecnologias, Universidade Autónoma de Lisboa Luís de Camões, Portugal

Keywords:

Knowledge Graph, Semantic Integration, Vocabulary, Data Quality, Semantic View.

Abstract:

The main goal of semantic integration is to provide a virtual semantic view that is semantically connected to

data so that applications can have integrated access to data sources through the virtual Knowledge Graph. A

semantic view can be published on a semantic portal to make it reusable for building Knowledge Graphs for

different applications. This paper takes the ﬁrst step towards publishing a semantic view on a semantic portal.

This paper has three main contributions. First, we introduce a vocabulary for specifying semantic views. Then,

we introduce a vocabulary for speciﬁcation and quality assessment of Knowledge Graph. Third, we describe

an approach to automatize the construction of a high-quality Knowledge Graph reusing a semantic view.

1 INTRODUCTION

In recent years, with the increase in the amount of

public data available, it has also increased the number

of applications that demand large volumes of hetero-

geneous data in order to allow greater accuracy in data

analysis. These types of applications require the cre-

ation of a homogeneous view of the data.

Recently, the term Knowledge Graph (KG) has

been used in association with Semantic Web tech-

nologies, linked data, large-scale data analytics and

cloud computing (Ehrlinger and Wöß, 2016). The use

of knowledge graphs is on the rise as a way of build-

ing homogeneous knowledge bases as a graph struc-

ture, combine different heterogeneous databases.

In the literature, the virtual Knowledge Graph

(VKG) approach has been discussed in a paradigm

known as ontology-based data access (OBDA) (Xiao

et al., 2018). The VKG approach proposes to use

one consistent virtual graph, more ﬂexible than a

rigid table structure and embed domain knowledge

(Xiao et al., 2019). In VKG, integration views are

virtual, which enables simpliﬁed design and mainte-

nance as these views can be tested and modiﬁed in-

stantly. However, the amount of data encountered can

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

https://orcid.org/0000-0003-4118-4224

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

cause query performance bottlenecks and to remedy

this problem, many organizations create copies of the

data, also called specialized Knowledge Graph, mate-

rializing a portion of the data as required. In addition,

specialized Knowledge Graph goes through a process

of conﬂict resolution and the use of data quality met-

rics that allow queries over a consolidated database.

We call semantic integration the process that

makes use of a conceptual representation of the data

and its relationship to deal with heterogeneity. The

main goal of semantic integration is to provide a vir-

tual semantic view, which is semantically connected

to data so that applications can have integrated access

to data sources. To make the semantic view reusable,

it should be published on a Semantic Portal, which

is intended to consolidate and semantically integrate

large numbers of heterogeneous data sources into a

comprehensive dataspace. Chem2bio2rdf (Bleiholder

and Naumann, 2010), SemanticDB (D Pierce et al.,

2012), and SemanticSUS (da Cruz et al., 2019) are

examples of semantic portals.

Once a semantic view (see, Section 2) has been

speciﬁed and published, semantic integration has al-

ready been performed a priori, so it can be used to

build a virtual Knowledge Graph or reused to build

specialized Knowledge Graph using Mashup View

(see, Section 3), which can also be published and ac-

cessed by external applications. The semantic view

allows integration approaches like (Collarana et al.,

Arruda, N., Venceslau, A., Mayron, M., Vidal, V. and Pequeno, V.

Publishing and Consuming Semantic Views for Construction of Knowledge Graphs.

DOI: 10.5220/0009421401970204

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 197-204

ISBN: 978-989-758-423-7

197

Figure 1: Three Level Framework for Semantic View Spec-

iﬁcation.

2017; Schultz et al., 2011), to be able to deﬁne the in-

tegration steps, providing the reuse and reduction of

time-consuming for design other applications.

This paper takes the ﬁrst step towards publishing

a semantic view. The proposed approach combines

ontologies and linked data to face the challenges in

developing applications where there is a need to in-

tegrate heterogeneous data sources. The paper has

three main contributions, which are described in sec-

tions 2, 3 and 4. Section 2 introduces a vocabulary

for specifying a semantic view. A semantic view is

speciﬁed with the help of local views and sameAs

linkset views. Section 3 introduces a vocabulary

for speciﬁcation and quality assessment of Knowl-

edge Graphs, providing relevant source information

and quality metrics to beneﬁt applications that aim

to build high quality KG (Collarana et al., 2017). It

also discusses how to reuse a semantic view speciﬁ-

cation for semi-automatic generation of a Knowledge

Graph. Section 4 describes an approach for building

high-quality Knowledge Graphs based on the quality

metadata of a semantic view. Finally, Section 5 con-

tains the conclusions.

2 SEMANTIC VIEW

In this section, we discuss a three-level ontology-

based framework (Vidal et al., 2015), as summarized

in Figure 1, to formally specify semantic view and a

vocabulary to represent this speciﬁcation and meta-

data.

2.1 Semantic View Speciﬁcation

In the proposed framework, the semantic view re-

sulting from semantic integration over data sources

, . . . , S

is a triple λ = (O

, V, L), where:

• O

represents the domain ontology (semantic

view layer). O

is responsible for establishing

a vocabulary to be shared to describe the data

sources;

• V represents a set of local view speciﬁcations

, . . . , V

that describes the data sources S

, . . . , S

using the terms in O

. A local view speciﬁcation

is a tuple (O

, M

), where:

– O

is the ontology of the local view. The vo-

cabulary of O

is a subset of the vocabulary of

whose terms occur in M

– M

is a set of mappings that relate terms of

vocabulary O

with terms Si;

• L is a set of linkage rules that specify virtual

sameAs links between resources in different lo-

cal views. These links are used to relate resources

that represent the same entity of the real world.

We consider two types of sameAs links: imported

sameAs links, which are exported by a Linked

Data source, and mashup sameAs links, which are

automatically created based on a sameAs linkset

view speciﬁcation (Casanova et al., 2014) speciﬁ-

cally deﬁned for the mashup application;

The process for generating the semantic view spec-

iﬁcation λ consists of 3 steps: (1) Modeling of the

domain ontology; (2) Generation of the local views

speciﬁcations; (3) Generation of the linkage rules.

2.2 Semantic View Vocabulary

The vocabulary for describing semantic view (VSV)

is partitioned in three categories: General Metadata,

View Speciﬁcation Metadata and Quality Metadata.

Given that a semantic view is a virtual dataset, the vo-

cabulary for general metadata uses the terms in VoID

vocabulary (Hartig and Zhao, 2010) for providing ba-

sic metadata about a dataset. VoID (preﬁx void:) is

an RDF Schema vocabulary for expressing metadata

about RDF datasets. It is intended as a bridge be-

tween the publishers and users of RDF data, with ap-

plications ranging from data discovery to cataloging

and archiving of datasets. The main terms in VoID

for general metadata are: dcterms:description, dc-

terms:created, dcterms:license, dcterms:source, dc-

terms:Source and dcterms:vocabulary.

The vocabulary for expressing metadata about

a semantic view speciﬁcation is deﬁned as OWL

ontology. Fig. 3 shows the main fragment of the

VSV vocabulary (preﬁx vsv:). The main classes

and properties are: vsv:SemanticViewSpeciﬁcation,

owl:Ontology, vsv:LocalView, vsv:LinkageRule,

vsv:QualityMetadata, vsv:hasQualityMetadata,

vsv:hasLocalView. The vocabulary was developed

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198

Figure 2: A Fragment of the Semantic View Speciﬁcation Vocabulary.

Figure 3: Three Level Framework for Mashup View Speci-

ﬁcation.

with understandability and usability in mind. For this

reason, we apply a consistent scheme for property

names, using "has" followed by a class name.

For expressing metadata about the quality of a se-

mantic view, we use the terms in Data Quality Vo-

cabulary (DQV) (preﬁx dqv:) (Debattista et al., 2016)

discussed in Section 4. In our framework, the quality

of semantic views is computed based on the quality of

the local views and quality of the linkset Views. For

more details see (Zaveri et al., 2013).

3 MASHUP VIEW

The creation of a mashup view is a complex task

which involves four major challenges: (1) selection of

the Linked Data sources that are relevant for the ap-

plication; (2) extraction and translation of data from

different, possibly heterogeneous data sources to a

common vocabulary; (3) identiﬁcation of links be-

tween resources in different Linked Data sources; (4)

combination and fusion of multiple representations of

the same real-world object into a single representation

and resolution of data inconsistencies to improve the

quality of the data.

In this section, we present an ontology-based

framework (Vidal et al., 2015) used in our approach

to specifying a mashup view, and a vocabulary to

represent this speciﬁcation and metadata. The ma-

terialization of a mashup view is calling special-

ized Knowledge Graph, it is automatically processed

based on its speciﬁcation.

3.1 Mashup View Speciﬁcation

We use a three level ontology-based framework Vi-

dal et al. (2015), as summarized in Figure 3, to

formally specify Knowledge Graph. The speciﬁca-

tion of a Knowledge Graph M is a quintuple, λ =

, V

, L

, F

, Q

), where:

• O

is the mashup view ontology;

• V

is a set of exported view speciﬁcations

, ..., E

that describes the data sources S

, ..., S

using the terms in O

. Each view E

is a tuple

, O

), where:

– M

is a set of rules that relate terms of vocab-

ulary O

with terms of vocabulary Si;

– O

is the ontology of the exported view E

The vocabulary of O

is a subset of the vocab-

ulary of O

whose terms occur in M

• L

is a set of linked view speciﬁcations L

, ..., L

between E

, ..., E

. We consider two types of

sameAs links: imported sameAs links, which are

exported by a Linked Data source, and mashup

sameAs links, which are automatically created

based on a sameAs linkset view speciﬁcation

Casanova et al. (2014) speciﬁcally deﬁned for the

mashup application;

Publishing and Consuming Semantic Views for Construction of Knowledge Graphs

199

• Q

is a set of quality requirements, which are re-

quested by the user application;

• F

is a set of fusion rules that specify how to re-

solve the problem of contradictory attribute val-

ues when combining multiple representations of

the same real-world object into a single represen-

tation (canonical IRI).

The process for generating the mashup view speci-

ﬁcation, without reusing a semantic view speciﬁca-

tion, consists of 4 steps: (1) Modeling of the mashup

view ontology; (2) Generation of the exported views

speciﬁcations; (3) Generation of the exported sameAs

linkset view speciﬁcations; (4) Deﬁnition of quality

requirements and fusion rules. Note that, steps 2-4 re-

quires semantic integration of the data source, which

is not an easy task.

In case that a semantic view is previ-

ously speciﬁed, semantic integration is done

a priori, and a mashup view speciﬁcation

λ = (O

, V

, L

, Q

, F

), can be automatically

generated based on O

, Q

, and the semantic

view speciﬁcation λ = (O

, V, L). In this case, the

vocabulary of O

is a subset of the vocabulary of O

therefore O

can be deﬁned using a faceted search

interface by selecting concepts and specifying ﬁlters.

3.2 Mashup View Vocabulary

The vocabulary for describing a mashup view (MV)

is partitioned in three categories: General Metadata,

Data Mashup View Speciﬁcation Metadata and

Quality Metadata. The vocabularies for general

and quality metadata is the same one used by the

semantic view and discussed in previous section. The

vocabulary for expressing metadata about a mashup

view speciﬁcation is deﬁned as an OWL ontology.

Figure 4 shows a fragment of the MV vocabulary

(preﬁx mv:). The main classes and properties

are: mv:MashupViewSpeciﬁcation, owl:Ontology,

mv:ExportedViewSpeciﬁcation, mv:LinkageRule,

dqv:QualityMetadata, mv:hasQualityMetadata,

mv:hasExportedViewS.

4 BUILDING HIGH-QUALITY

KNOWLEDGE GRAPH

We start this section by presenting the main concepts

of data quality and then discuss how to represent those

concepts using the DQV vocabulary. We conclude

this section by summarizing a data quality assessment

methodology.

4.1 Data Quality Vocabulary

The standardized formulation of data quality in

RDF/OWL facilitates transparency, veriﬁcation, and

sharing of linked data quality. Data on Web Best

Practices (DWBP) point to the importance of publish-

ing data quality information about data on the Web.

For this purpose, the DWBP created a vocabulary to

express data quality, called Data Quality Vocabulary

(DQV) (Debattista et al., 2016).

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 dimensions

(Bizer and Cyganiak, 2009). More speciﬁcally, qual-

ity metrics are heuristics designed to ﬁt a speciﬁc as-

sessment situation Wang (2005).

Figure 5 shows a fragment of the DQV vo-

cabulary. The DQV vocabulary distinguishes be-

tween three layers of abstraction (metric, dimen-

sions, and category), 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

represents a quality metric measure of a given re-

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

a set of links, a graph or a set of triples in which qual-

ity measurement is performed.

Many quality metrics have been proposed to as-

sess the quality of Linked Data sources because of the

importance and use of this data (Zaveri et al., 2016).

For example, in Table 1 the metrics M8 and M9 as-

sess the quality of the interoperability dimension and

the metrics M5, M6, and M7 assess the quality of the

accessibility category.

4.2 Materialization and Quality

Assessment of Knowledge Graph

In this section, we propose a method for generation

and quality assessment of knowledge graph. To be

useful, a knowledge graph must have good quality.

Quality assessment of knowledge graph is not a sim-

ple process, as it involves other factors such as qual-

ity of data sources, quality of mappings, and quality

of sameAs links. Normally, KG quality is calculated

along at least three dimensions (Dong and Naumann,

2009): completeness, conciseness, and consistency.

Consistency expresses how much the data are in the

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200

Figure 4: A Fragment of the Mashup View Speciﬁcation Vocabulary.

dqv:QualityMeasurement

+ dqv:value

rdfs:Resource

dqv:computedOn

dqv:Metric

+ dqv:expectedDataType

dqv:Dimensiondqv:Category

dqv:inDimension

dqv:isMeasurementOf

dqv:inCategory

dqv

QualityMeas

esource

dqv:hasQualityMeasurement

Figure 5: A Fragment of the DQV Vocabulary.

real world, while completeness and conciseness are,

in a way, analogous to recall and precision in infor-

mation retrieval (Knap et al., 2012).

As shown in Figure 6, materialization is per-

formed incrementally, and at each step, the quality

of the triples and datasets (materialized views) gener-

ated is also computed. The process of computing data

quality is called quality assessment, in which process

quality metadata is computed to measure data quality.

Thus, errors can be detected by directing modiﬁca-

tions that increase the quality of the data.

Table 2 shows quality metrics of exported view,

linkset view and mashup view used to quality assess-

ment of consistency dimension. The consistency of

the mashup view is computed by the quality metrics

MV M1, MV M2, and by the consistency of the in-

stances of mashup view, that is computed by the con-

sistency of its triples. Which in turn is computed by

the metrics MV M3 and MV M4, and by the consis-

tency of the exported views and linkset views. The

consistency of the exported views and linkset views

are computed by the metrics LV M1, LV M2 and met-

rics EV M1, EV M1, respectively. They depend on

the consistency of the data source, the consistency of

the linkset view also depends on the consistency of

the exported views.

The generation of KG is processed automatically

using as input the mashup view speciﬁcation, data

sources and quality metadata from data sources. As

shown in Figure 6, the process for building the spe-

cialized knowledge graph consists of 3 steps, de-

scribed in following.

Step 1. Materialization and Quality Assessment of

Exported Views

In this step, each view in is materialized using the

mappings. In this step, the quality of the exported

view is also computed based on the quality metadata

of the data sources, mapping rules, and the exported

view materialization.

Step 2. Materialization and Quality Assessment of

Linkset Views

This step identiﬁes and materializes sameAs links.

Each view is materialized using the linkage rule.

Due to the importance of sameAs links, various ap-

proaches have been proposed to compute link quality,

for example, based on functional properties (Papaleo

et al., 2014) and using network measurements (Guéret

et al., 2012)

Step 3. Data Fusion and Quality Assessment of

Knowledge Graph

In this step, the fusion of multiple representations

representing the same real-world entity into a single

representation is performed. Fusion rules in F deﬁne

how to solve the problem of conﬂicts that can occur

in fusion objects. Resolving data inconsistency im-

proves the quality of knowledge graph.

In the proposed framework, the speciﬁcation of

quality requirements, with the help of the user, should

help in choosing which function to use to resolve a

particular type of conﬂict. For example, if the user

opts for a more complete mashup view, conﬂicts be-

tween values are not resolved.

As shown in Figure 6, during the data fusion pro-

cess, the quality assessment of the generated triples

Publishing and Consuming Semantic Views for Construction of Knowledge Graphs

201

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)

Table 2: Quality Metrics of the Consistency Dimension for Knowledge Graph.

Factor Metric Description

Mashup View MV_M1 conformance of the source ontology and mashup ontology

(schema consistency) (Wang (2012))

MV_M2 mappings conforms to the semantics of information

represented (mapping consistency) (Wang (2012))

MV_M3 difference between value v and other (conﬂicting) values (Knap et al. (2012))

MV_M4 conﬁrmation values (Knap et al. (2012))

Exported View EV_M1 The degree to which exported ontology is free

of (logical/formal) contradictions (Zaveri et al. (2016))

EV_M2 Proportion of mappings in the exported view

error-free (Zaveri et al. (2016))

Linkset View LV_M1 measures the similarity of instances linked to sameAs based

on functional properties (Papaleo et al. (2014))

LV_M2 measures the similarity of instances linked to sameAs

based on linkage in linkset view

is performed. The quality of export views and links

are important in determining the quality of the triple,

also taking into account the equality and similarity of

conﬂicting values (Knap et al., 2012).

5 CONCLUSIONS AND FUTURE

WORK

This paper introduces a framework to publishing vir-

tual Knowledge Graph on a semantic portal. First, we

introduce a vocabulary for specifying semantic views.

Then, we introduce a vocabulary for speciﬁcation and

quality assessment of Data Mashup view. Third, we

describe an approach to automatize the construction

of a high-quality Knowledge Graph reusing a seman-

tic view speciﬁcation. The proposed vocabularies as

provides metadata for describing quality information

about the semantic view and the mashup view. The

quality information provided by the proposed vocab-

ulary, enables the data quality assessment.

As a case study, we built SemanticSUS

, a se-

mantic portal which is intented to offer a semantic

view that semantically integrates data sources from

the uniﬁed health system of Brazil (SUS). In its cur-

rent state, SemanticSUS semantically integrates three

SUS data sources which are available on the GISSA

platform (Freitas et al., 2017). The portal semantic

View was used to generate the speciﬁcation of the

knowledge graph NDR (Neonatal Death Risk). The

RMN mashup integrates information about children

who lived less then 28 days (neonatal period), and it

was used to develop a predictive model to establish

the risk of neonatal death.

As a suggestion for future work, new metrics can

be incorporated into the quality vocabulary. In (Ar-

ruda et al., 2019) we propose a Fuzzy evaluation ap-

proach to allow the creation of semantic rules (close

to spoken language) to relate and evaluate the quality

of the linked data. So using this fuzzy approach con-

tribute to a more comprehensive quality assessment.

https://semanticsus.github.io/semanticSUS/index.html

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202

Figure 6: Knowledge Graph Materialization.

We are also developing a tool to facilitate the process

of building high-quality Data Mashup views and in-

cremental maintenance by reusing a Semantic view

speciﬁcation (Arruda, 2019). In our approach, the

Data Mashup view is generated in three steps: ﬁrst,

the user speciﬁes the mashup view ontology and

the quality requirements of the mashup application.

Then, the speciﬁcation of the mashup view is auto-

matically generated by reusing the mapping and link-

age rules deﬁned by the semantic view speciﬁcation.

Finally, the materialization and quality assessment of

the Data Mashup view is automatically accomplished

using the strategy described in Section 4.

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