Heterogeneous Data Integration: A Literature Scope Review

Silvia Lucia Borowicc

1 a

and Solange Nice Alves-Souza

2 b

School of Arts, Sciences and Humanities, Universidade de S

ao Paulo, Arlindo Bettio 1000, S

ao Paulo, Brazil

Polytechnic School, Universidade de S

ao Paulo, S

ao Paulo, Brazil

Keywords:

Data Integration, Heterogeneous Data, Ontology.

Abstract:

Data have been collected by communities for analysis, visualization, predictions and other activities to support

data-driven decision. Obtaining value from data assets directly depends on the data integration task. However,

Big Data poses new challenges to integration due to data heterogeneity. It is essential to understand the main

problems and to know technologies and techniques that have been employed to improve the ability to obtain

value by heterogeneous data integration. This paper presents a literature scope review that highlights the

main techniques applied to heterogeneous data integration. The literature reviewed presents solutions mostly

focusing on a speciﬁc purpose or part of the integration process instead of a clear understanding of how the

techniques can be used in a complete integration process. Therefore, this work shows a whole picture of a data

integration process organizing the techniques according to their functionalities and presents a workﬂow with

tasks associated to techniques and resources, focusing on semantic mediation, such as mapping and matching

tasks. Ontologies and semantic web technologies are promising to address data heterogeneity and have been

used in the semantic enrichment of data and semantic mediation between data sources and global model.

However, some aspects remain to be further investigated, such as ontology and terminology construction, data

processing scalability and semantic mediation, especially for mapping deﬁnition.

1 INTRODUCTION

The existence of data, even in large volumes, is not

enough to guarantee that the information demand will

be effectively and timely met. Getting value from data

assets in a Big Data context faces challenges related

to the integration of multiple and heterogeneous data

sources. The volume and heterogeneity of data hin-

der integration, especially when incorporating semi-

structured or unstructured and semantically different

data (Nathalie, 2009).

An integrated view of data can greatly contribute

to obtain new information and knowledge. In the

health ﬁeld, for example, it is necessary to integrate

several sources to assess as many risk factors as pos-

sible for a disease to manifest (Zhang et al., 2018).

Likewise, in environmental analyses, it may be nec-

essary to integrate data from different geographic di-

mensions or from different types of equipment and

sensors to enable a complete evaluation and reduce

model inconsistencies (Nundloll et al., 2021).

However, data can be spread over different organi-

https://orcid.org/0000-0001-7399-274X

https://orcid.org/0000-0002-6112-3536

zations such as research centers, institutions and com-

panies, which makes it difﬁcult to cross-reference this

information in computer systems. Besides technical

and structural factors, the meaning of data is an as-

pect that increases the complexity of the integration

process.

Hence, when integrating data, resources are nec-

essary to make the data semantics explicit to allow a

clear understanding of the data whose meanings are

dispersed in applications and other artifacts, or even

exclusively in the memory of the users. In data inte-

gration processes, metadata are essential and must be

accessible for the correct use of the data.

Therefore, in Big Data contexts, to move toward

solutions to integrating heterogeneous data, it is im-

portant to identify and evaluate techniques and ap-

proaches that have been employed, considering the

whole data integration cycle, which includes metadata

management (DAMA, 2017). Different researches

rely on the semantic web, using techniques and tech-

nologies to solve semantic issues in the data inte-

gration from heterogeneous sources for speciﬁc do-

mains (Dirgahayu et al., 2020; Kamm et al., 2021;

Nagpal et al., 2021).

Borowicc, S. and Alves-Souza, S.

Heterogeneous Data Integration: A Literature Scope Review.

DOI: 10.5220/0012551000003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 189-200

ISBN: 978-989-758-692-7; ISSN: 2184-4992

189

Knowledge graphs and ontology appear in recent

studies and have the potential to play an important

role in data and information integration (Fathy et al.,

2019; Cudr

e-Mauroux, 2020; Ma and Moln

ar, 2020),

as well as data fusion with machine learning tech-

niques, focusing on the Big Data variety(Divya and

Manish, 2020; Kumar and Das, 2019).

This review highlights the requirements imposed

by the ever-increasing demand for using data and the

high complexity of integration processes due to data

heterogeneity in multiple application contexts. Tech-

niques and resources presented as solutions in the lit-

erature reviewed are detailed and organized accord-

ing to the application functionality, helping other re-

searchers to choose techniques and strategies for inte-

grating heterogeneous data.

As far as it was possible to search, previous works

focusing on heterogeneous data integration are di-

rectly linked to a speciﬁc application area or refer to

part of the integration process (Noriega and Sanchez,

2019; Dirgahayu et al., 2020). Therefore, it is not sim-

ple to understand how to use the resources through-

out a heterogeneous data integration cycle. Thus,

this paper presents a uniﬁed view, developed from a

broad investigation, considering the whole data inte-

gration cycle and without delimiting domains. This

view shows how and in which tasks the different tech-

niques, models, strategies, and patterns should be em-

ployed. We also suggest a data integration workﬂow,

focused on semantic mediation tasks, which, in our

view, facilitates integrated understanding of previous

work.

This paper is structured as follows. Section 2

introduces basic concepts associated with heteroge-

neous data integration; Sections 3 and 4 present the

review process and its results. Finally, Section 6 con-

tains the conclusion and suggests future research.

2 HETEROGENEOUS DATA

INTEGRATION-RELATED

CONCEPTS

A variety of data integration approaches and tech-

niques have been developed over time. Starting from

approaches based on functional or relational models,

with highly coupled solutions that provided a global

data schema focused on structured data, until the in-

corporation of unstructured data with the advent of

the internet (Ziegler and Dittrich, 2004).

The volume and heterogeneity of data currently

generated make traditional approaches difﬁcult, es-

pecially with semantic differences (Nathalie, 2009).

The syntactic and semantic heterogeneities are related

to aspects such as polysemy, synonyms and abbrevi-

ations (Calva and Piedra, 2020). Ambiguities have to

be eliminated when grouping, combining or complet-

ing data from different sources. Metadata, with ex-

plicit and precise semantics, can be used for correctly

integrating semantically heterogeneous data.

2.1 Ontology

In information systems, ontologies specify and for-

malize concepts by modeling characteristics and phe-

nomena of the world. Ontologies are deﬁned by

classes, properties, relationships and dependencies

considering a speciﬁc knowledge domain (Mahmoud

et al., 2021).

The use of ontologies can beneﬁt data integration

activities in several ways, according to (Xiao, 2006),

including:

• metadata representation,

• automated data checking,

• global conceptualization,

• support for high-level semantic queries,

• description of the semantics of the information

sources, explaining their content, and

• identiﬁcation and association of semantically cor-

responding information concepts.

(a) single (b) multiple

Figure 1: Architectures for using ontologies to explain con-

tent.

Figure 1 shows architectures for using ontologies

in heterogeneous data integration: [single] in which

the data sources are mapped to a general representa-

tion model of the knowledge domain; [multiple] mod-

els from the original data sources that undergo a map-

ping process among themselves, that is, the global

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model is the result of a Cartesian product obtained by

cross-referencing all the original models; and [hybrid]

which uses the data source models built on a shared

global vocabulary of basic terms that correspond to

a domain, which allows them to be shared with each

other (Wache et al., 2001).

Different approaches can be used to link ontolo-

gies to data sources, either by a relationship with the

database schema or with terms in the database con-

tent. The approaches described by (Wache et al.,

2001) may include the following strategies, either

alone or any combination thereof:

• Structure Resemblance: integration occurs by

generating a model that reﬂects the original struc-

ture in a one-to-one mapping from the ontology

deﬁnitions;

• Deﬁnition of Terms: the ontology is used to de-

ﬁne database or schema terms;

• Structure Enrichment: a combination of the two

previous approaches.

• Meta-Annotation: meta-annotation is used to

add semantic information to an information

source.

Language dependency of ontology needs to be

considered when addressing ontology sharing, merg-

ing, and translation, topics that often involve multiple

vocabularies and conceptualizations (Guarino, 1998).

To relate different ontologies, mediating agents per-

form the translation between the ontologies, either by

lexical relationships that allow comparing language

terms, or using a general ontology related to the other

ontologies, or even by the search for correspondence

semantics between concepts of different ontologies

(Wache et al., 2001).

2.2 Semantic Web and Knowledge

Graph

The interconnected datasets on the Web are called

Linked Data

(LD). W3C refers to the network of in-

terconnected data as the Semantic Web

. The tech-

nologies associated with the Semantic Web allow cre-

ating databases on the Web, as well as developing re-

sources to interconnect and consume these data. The

standard model for representing information on the

Web is based on the Resource Description Frame-

work (RDF)

. This model derives the link structure

of the Web using URIs to identify resources (enti-

ties, concepts, objects) and relationships (interactions,

https://www.w3.org/standards/semanticweb/data

https://www.w3.org/standards/semanticweb/

http://www.w3.org/RDF/

events). The basic unit of data representation is a

triple: ”subject, predicate, object”.

Graphs have become one of the main data

structures used in heterogeneous data integration.

Machine-readable and human-understandable, they

can represent objects and interactions in a ﬂexible

modeling that allows mapping most types of data

(Gomes and Santanch

e, 2015; Jie et al., 2021).

In the semantic web, ontologies are representa-

tions based on RDF triples, and similarly, real-world

entities and relationships can be represented using

knowledge graphs. Data sources with their own se-

mantic models and ontologies can be embedded in

knowledge graphs (Pomp et al., 2017; Hao et al.,

2021; Zhao et al., 2021).

3 LITERATURE SCOPE REVIEW

PROTOCOL

The protocol adopted, based on the (Kitchenham and

Charters, 2007) proposal for systematic reviews in

Software Engineering, is divided into three phases:

planning, conduction and results.

As shown in Figure 2, the review protocol started

from an exploratory analysis of the literature to un-

derstand the concepts related to the integration of het-

erogeneous data. This analysis was the basis of the

objective: to broadly investigate the techniques and

approaches for heterogeneous data integration, con-

sidering the whole data integration cycle.

Table 1: References that supported the exploratory review.

Subject References

data integration or

heterogeneous

data

(Sugawara and Nikaido,

2014);(

Ozsu and Valduriez,

2020);(Ziegler and Dittrich,

2004);(Batini et al.,

1986);(Zhang et al., 2018)

knowledge graph (Hao et al., 2021);(Zhao et al.,

2021)

ontology (Gruber, 1993);(Guarino,

1998);(Nathalie, 2009);(Wache

et al., 2001);(Zhao et al.,

2021);(Zhang et al., 2018)

Also based on the exploratory analysis, the re-

search questions and the planning of this review were

speciﬁed. Table 1 shows the references that sup-

ported the exploratory review, classiﬁed by subjects

related to data integration. Reading began with ref-

erence publications and texts addressing data integra-

tion techniques to understand related terms.

Heterogeneous Data Integration: A Literature Scope Review

191

Figure 2: Literature scope review protocol.

3.1 Research Questions

Research questions (RQ1 e RQ2) are designed to ex-

plore the latest research results in heterogeneous data

integration considering that different techniques and

approaches can be employed in the process.

RQ1. What techniques have been used for heteroge-

neous data integration?

RQ2. How to bring in integrated databases from het-

erogeneous sources?

These questions are intended to provide an

overview of the techniques used to integrate data

with heterogeneous structures, syntaxes or seman-

tics. Design and validation of frameworks, the use

or implementation of tools and approaches used to

meet demands for integrated data were considered.

Techniques for materialized or virtual data integration

were veriﬁed. The search for articles was limited to

the period from 2015 to 2022.

3.2 Search Strategy

The search string comprised three keywords and their

related terms, according to the exploratory analysis

readings, presented in Table 2.

Table 2: Search keywords.

Keyword Related terms

data integration information integration

heterogeneous

data

heterogeneous datasets

heterogeneous datasources

heterogeneous information

unstructured data

ontology knowledge graph

semantic

The inclusion and exclusion criteria are respec-

tively presented in Tables 3 and 4.

Scopus, ACM, Engineering Village, IEEE, Web of

Science and PubMed were selected to search papers

due to their credibility, adequacy to the computing

area and health besides being paid by the university,

Table 3: Inclusion criteria (IC).

Criteria Description

IC-1 The research addresses, applies and

discusses the results of the application of

heterogeneous data integration techniques.

IC-2 The research addresses, applies and

discusses the results of applying integrated

data modeling techniques from

heterogeneous data sources.

IC-3 Paper published in a journal or conference

between 01/01/2015 and 31/12/2022.

Table 4: Exclusion criteria (EC).

Criteria Description

EC-1 Literature review

EC-2 Paper is not written in Spanish, English or

Portuguese

allowing full access to their content. The search was

conducted in January 2023 and the results were im-

ported into the Parsif.al

tool. After excluding dupli-

cates, 508 articles remained for selection, in the Con-

duction phase (Figure 3).

3.3 Conduction

A prior selection was made by reading the title, ab-

stract and keywords. To support the selection process,

ASReview

was used, a tool that implements active

learning, with its standard classiﬁcation algorithm.

ASReview reorders unread texts as it ”learns”

from those that have already been annotated as rel-

evant or irrelevant. The tool contributed to increase

conﬁdence by corroborating the results of the manual

process. After selection, 81 articles were included,

about 16% of the 508 articles found.

https://parsif.al/

https://asreview.nl/lab/

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Figure 3: Conduction workﬂow.

4 RESULTS OF THE

LITERATURE SCOPE REVIEW

The authors mainly focus on integration based on

semantic web technologies. Data are mostly repre-

sented in RDF graphs and ontologies are used to orga-

nize and formalize semantic knowledge. The research

on data integration and the semantic web comple-

ment each other and are often combined to solve prob-

lems of semantic data heterogeneity, promoting data

sharing and efﬁcient use from different autonomous

sources (Kessler et al., 2021; Jeong and Jeong, 2015).

Figure 4: Papers by document type and publication year.

Figure 4 shows the distribution of papers by pub-

lication type (conference or journal) and year. The

topic is observed to have had increasing interest; in

contrast, conference publications decreased in 2020,

at the peak of the COVID-19 pandemic, which un-

doubtedly affected not only attendance, but also led

to the suspension of several conferences.

4.1 Semantic Enrichment in

Heterogeneous Data Integration

Due to the large volume, variety, velocity and com-

plexity of data, semantic enrichment of data and meta-

data for understanding the context is essential for

obtaining relevant information and knowledge. Re-

searchers have used resources such as ontologies,

knowledge graphs and classiﬁcation to semantically

enrich data and increase the efﬁciency of integration

processes by automating entity mapping. Table 5 lists

publications according to the resources used.

Ontologies are commonly expressed formally us-

ing semantic markup languages such as RDFs or Web

Ontology Language (OWL) and thus describe do-

mains of knowledge from classes, properties and rela-

tionships. These representations are used in semantic

mapping processes with integration purposes. The-

saurus and standards created for data exchange can

also be used in the ontology and graph construction

or in classiﬁcation processes.

4.2 Global Modeling and Semantic

Mediation

Regarding semantic mapping, as well as the architec-

tures for using ontologies, in the content explication

presented in Figure 1, three architectures were ob-

served in the papers. Table 6 lists the publications

according to the mapping approach presented.

In the hybrid approach used by (Nundloll et al.,

2021), a different nomenclature appears to identify

the global and local models. The global ontology is

called domain ontology, whose level of abstraction is

independent of implementations and reﬂects a domain

of knowledge. Local ontology, called data ontology,

models a speciﬁc data source and interfaces between

the data and the domain ontology.

In this review, data source mapping and match-

ing tasks are also referred to as semantic mediation.

To implement data source mapping, tools are used,

mainly based on RDF Mapping Language (RML)

These tools are presented in Table 7 together with the

formats accepted.

Among the cited tools, Karma is pointed out

by (Zhang et al., 2021) as the state of the art for se-

mantic annotation of structured data and publication

in Linked Open Data. Compared to Karma, accord-

ing to (Masmoudi et al., 2019), DISERTO requires

less human intervention in the process by perform-

ing automated mapping but has fewer input formats

allowed.

https://rml.io/

Heterogeneous Data Integration: A Literature Scope Review

193

Table 5: Semantic enrichment resources.

Technique Publications

Ontology (Gil et al., 2021);(Dridi et al.,

2020);(Ding et al., 2020);(Zhang et al.,

2021);(Nundloll et al., 2021);(Sernadela

and Oliveira, 2017);(Rouces et al.,

2018);(Fusco and Aversano,

2020);(Wang et al., 2017);(Zhang et al.,

2018);(Grasso et al., 2015);(Saber et al.,

2018);(Hao et al., 2021);(Masmoudi

et al., 2019);(Sima et al.,

2019);(Baazaoui-Zghal, 2016);(Calva

and Piedra, 2020);(Masseroli et al.,

2016);(Radaoui et al., 2019);(Kim et al.,

2021);(Avila et al., 2019);(Rani et al.,

2019);(Buron et al.,

2020);(Nimmagadda et al.,

2019);(Yadav et al., 2021);(SCHIESSL

and BR

ASCHER, 2017);(Niang et al.,

2016);(Kessler et al., 2021);(Lembo and

Scafoglieri, 2020);(Jeong and Jeong,

2015);(Buron et al.,

2020);(Sengloiluean and Khuntong,

2020);(Shen et al., 2016);(Cheung et al.,

2015);(Xiao et al., 2017);(Zhou,

2016);(Yun et al., 2019);(Qundus et al.,

2021);(Pereira et al., 2020);(Capodieci

et al., 2016);(Dao et al.,

2021);(Mountasser et al., 2021);(Nath

et al., 2017);(Santipantakis et al.,

2020);(Mrhar et al., 2020);(Mami et al.,

2019);(Mahmoud et al.,

2021);(Khnaisser et al.,

2022);(Burgdorf et al.,

2022);(Maga-Nteve et al., 2022);(Wu

et al., 2022);(Ramzy et al., 2022);(Ma

and Moln

ar, 2022);(Haghgoo et al.,

2022);(Phengsuwan et al.,

2022);(Katrandzhiev et al.,

2022);(Krataithong et al., 2022);(Bonte

et al., 2022);(Guedea-Noriega and

Garc

ıa-S

anchez, 2022);(Thirumahal

et al., 2022);(Stroganov et al., 2022)

Knowledge

graph

(Nashipudimath et al.,

2020);(VandanaKolisetty and Rajput,

2021);(Gupta and Gupta,

2021);(Yafooz et al., 2018);(Dhayne

et al., 2018);(Bartusiak and L

assig,

2016);(Asprino et al., 2023);(Zhao

et al., 2022);(Oo et al., 2022)

Classiﬁcation (Vilches-Bl

azquez and Saavedra,

2022);(Ma et al., 2017);(Balachandran

et al., 2019);(Zhao et al.,

2021);(Le Guillarme et al.,

2021);(Sandhya and Roy,

2016);(Pomp et al., 2017);(Jie et al.,

2021);(Vidal et al., 2019);(Gomes and

Santanch

e, 2015)

Moreover, (Mrhar et al., 2020) showed improve-

ment in semantic recognition accuracy, in semi-

automatic mapping, by associating Karma with an

algorithm that combines Long Short-Term Memory

(LSTM) (Hochreiter and Schmidhuber, 1997) with

Conditional Random Field (CRF) (Lafferty et al.,

2001).

Table 6: Semantic mapping architecture.

Mapping Publications

Single (Dridi et al., 2020);(Ding et al.,

2020);(Ma et al., 2017);(Sernadela and

Oliveira, 2017);(Rouces et al.,

2018);(Nashipudimath et al.,

2020);(Wang et al., 2017);(Saber et al.,

2018);(Le Guillarme et al., 2021);(Hao

et al., 2021);(Masmoudi et al.,

2019);(Pomp et al.,

2017);(Baazaoui-Zghal, 2016);(Calva and

Piedra, 2020);(Masseroli et al.,

2016);(Radaoui et al., 2019);(Gupta and

Gupta, 2021);(Avila et al.,

2019);(Nimmagadda et al.,

2019);(SCHIESSL and BR

ASCHER,

2017);(Jeong and Jeong, 2015);(Qundus

et al., 2021);(Capodieci et al.,

2016);(Santipantakis et al., 2020);(Gomes

and Santanch

e, 2015);(Mami et al.,

2019);(Mahmoud et al., 2021);(Asprino

et al., 2023);(Khnaisser et al.,

2022);(Burgdorf et al., 2022);(Zhao et al.,

2022);(Wu et al., 2022);(Ramzy et al.,

2022);(Oo et al., 2022);(Phengsuwan

et al., 2022);(Krataithong et al.,

2022);(Bonte et al.,

2022);(Guedea-Noriega and

Garc

ıa-S

anchez, 2022)

Multiple (Balachandran et al., 2019);(Grasso et al.,

2015);(Sima et al., 2019);(Shen et al.,

2016);(Cheung et al., 2015);(Zhou,

2016);(Vidal et al., 2019);(Dao et al.,

2021);(Bartusiak and L

assig, 2016);(Nath

et al., 2017)

Hybrid (Zhang et al., 2021);(Vilches-Bl

azquez

and Saavedra, 2022);(Nundloll et al.,

2021);(Fusco and Aversano,

2020);(Zhang et al., 2018);(Sandhya and

Roy, 2016);(Kim et al., 2021);(Rani et al.,

2019);(Buron et al., 2020);(Niang et al.,

2016);(Kessler et al., 2021);(Buron et al.,

2020);(Sengloiluean and Khuntong,

2020);(Xiao et al., 2017);(Yun et al.,

2019);(Dhayne et al., 2018);(Mountasser

et al., 2021);(Mrhar et al.,

2020);(Maga-Nteve et al.,

2022);(Thirumahal et al., 2022)

Table 7: Data source mapping tools

Tool Format Publications

DISERTO CSV; ENVI (Masmoudi et al., 2019)

HL7toRDF HL7 (Dhayne et al., 2018)

Karma DB; DSV;

XML;

JSON;

KML

(Zhang et al., 2021)(Xiao

et al., 2017)(Yun et al.,

2019)(Qundus et al.,

2021)(Capodieci et al.,

2016)(Mrhar et al., 2020)

OAM DB (Krataithong et al., 2022)

Ontop ERDB (Niang et al., 2016)(Sima

et al., 2019)(Kessler

et al., 2021)(Ding et al.,

2020)(Zhang et al., 2018)

As regards performing semantic queries on data,

the predominant language used to create and process

queries is SPARQL, which is a W3C standard lan-

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guage capable of retrieving and manipulating data

represented in RDF (Dao et al., 2021).

Traditional architectures widely used for data inte-

gration, such as Data Warehouse (DW), can also im-

plement semantic enrichment to solve heterogeneity

problems. Ontology is used in metadata that seman-

tically describes models or in deﬁning relational or

multidimensional database schema.

Table 8 presents some detailed publications im-

plementing the link between the semantic model and

data. Data sources and different approaches for rep-

resenting the semantic model are presented. The ap-

proaches are related to the linking strategies presented

in Section 2.1.

Table 8: Connection between semantic model and data

sources.

Source Connection Publications

DW Deﬁnition of

Terms: schema

(Masseroli et al.,

2016);(Baazaoui-Zghal,

2016)

Relational

Database

Deﬁnition of

Terms: schema

(Khnaisser et al., 2022)

DW Meta-

Annotation

(Nimmagadda et al.,

2019)

DW Deﬁnition of

Terms: RDF

(Mahmoud et al.,

2021);(Nath et al.,

2017)

Knowledge

Graph

Deﬁnition of

Terms: RDF

(Pomp et al., 2017)

Besides traditional data structures, the graph-

based data model has been considered a better choice

and has become one of the main ways to unify het-

erogeneous data, regarding the ease of mapping most

types of data due to modeling ﬂexibility (Gomes and

Santanch

e, 2015; Jie et al., 2021).

By associating graphs with ontologies to deal with

semantic heterogeneity, it is possible to interconnect

and manipulate data, building a coherent and inte-

grated view from multiple and heterogeneous sources.

The RDF graph can be interpreted using an ontol-

ogy and also supports queries on data and ontology

at the same time (Jie et al., 2021; Buron et al., 2020;

Vilches-Bl

azquez and Saavedra, 2022).

A semantic model can also be created directly

from the embedded data. A knowledge graph can be

generated from reading the content of data sources.

The semantic model is not static, being expanded as

new data sources are included by users during the in-

tegration process (Pomp et al., 2017).

4.3 Data Processing Techniques

Pattern recognition and natural language processing

(NLP) have been used in data integration from struc-

tured and unstructured sources, for text reading, se-

mantic mapping, data fusion, linkage, entities recog-

nition and classiﬁcation.

NLP is used to read data from text ﬁles, PDF ﬁles

or from text ﬁelds stored in relational databases. The

papers presented semi-automated entity recognition

processes, with user participation in data validation

and cleaning. In these processes, some methods are

used, such as most frequent terms index and similar-

ity metrics. To deal with imprecise information and

linguistic ambiguity fuzzy logic is used for mapping

and semantic enrichment processes (Baazaoui-Zghal,

2016; Haghgoo et al., 2022; Krataithong et al., 2022;

Stroganov et al., 2022) .

Large databases, ontologies, knowledge graphs in

English language are predominant; nonetheless, mul-

tilingual solutions were found; a publication in which

the authors (SCHIESSL and BR

ASCHER, 2017) use

a database in Portuguese, and (Guedea-Noriega and

Garc

ıa-S

anchez, 2022) worked with Spanish texts.

4.4 Data Storage Approaches and

Scalability

The storage approaches in data integration are

twofold: (i) materialized data integration built in the

storage layer, i.e., data are loaded, transformed and

stored in an integrated database; (ii) virtual data in-

tegration, which occurs in the query layer and whose

searches are performed into a global model, although

the data remains stored only in its original source.

In Big Data environments with a materialized ap-

proach, traditional resources such as DW have not

been highly scalable and add cost to the storage

infrastructure (Rani et al., 2019). In virtual inte-

gration approaches, global query models and feder-

ated databases are adopted to avoid materialization

costs and increase scalability (Masmoudi et al., 2019).

Nevertheless, in this case, there is an impact and an in-

crease in processing infrastructure costs, since all data

is transferred and processed online at the time of the

request.

Big Data technologies are used in some recent

works, aiming at scalability and cost reduction us-

ing distributed infrastructure with low computational

power, scaling by the distribution of processing.

Thereby, tools and techniques such as Hadoop and

MapReduce are used to implement distributed pro-

cessing in the materialized or virtual approach, in

queries and mappings (Rani et al., 2019; Van-

danaKolisetty and Rajput, 2021; Santipantakis et al.,

2020), or storage (Nashipudimath et al., 2020).

To improve performance in semantic mapping and

query processing, (Rani et al., 2019; Kim et al.,

2021) implemented architectures with multiple se-

mantic levels, supporting the process of combining

Heterogeneous Data Integration: A Literature Scope Review

195

Figure 5: Resources and techniques applied to integration workﬂow.

ontologies and queries that can be executed at mul-

tiple levels.

5 DISCUSSION

In the articles included in the review, it was not pos-

sible to identify a workﬂow that demonstrated the use

of techniques and resources in the stages of a data

integration process. Therefore, to facilitate under-

standing, Figure 5 organizes knowledge, including the

most common techniques found in the review, indicat-

ing at which stages they are used.

The workﬂow is divided into three stages: (i) def-

inition, whereby the source and target terminologies

are identiﬁed and mappings are speciﬁed; (ii) execu-

tion is the stage in which ingestion, processing and

matching are performed differently depending on the

data storage approach. In virtual scenarios, middle-

ware are commonly used to split and translate feder-

ated queries to the source query language. In materi-

alization scenarios, integrated data is transformed into

RDF graphs and stored; (iii) in the release stage inte-

grated data are available to explore, predominantly by

SPARQL endpoints.

Most of the selected publications, 79 out of the

81, mention the use of some semantic enrichment re-

source, such as ontologies and knowledge graphs. In

semi or unstructured data, natural language process-

ing (NLP) was used to extract data and then subject

the data to pattern recognition to identify entities and

match them with global data models.

In papers regarding transformation tools used to

perform matching according to mapping, in RML,

and conversion to RDF data, data sources and RML

mapping ﬁles are placed as inputs to the matching

process. No mentions were found about creating this

mapping in any other way other than using a tool that

reads the structure of the data source and the model,

submitting the mapping indication to the user or, man-

ually writing the rules in RML, which makes knowl-

edge of the RML language imperative to implement

data transformation to RDF.

6 CONCLUSION

Heterogeneous data integration is essential to obtain

value from data assets. In this review, techniques and

approaches used for integrating heterogeneous data

were investigated.

The review results show that ontologies and se-

mantic web technologies are promising to resolve

data heterogeneities. Also, Big Data technologies

have been used in some proposals for distributed stor-

age and query processing, or mappings, contributing

to scalability. However, there are some aspects of the

research, including ontology construction and seman-

tic mediation, that remain open. Furthermore, aspects

of data governance in the data integration workﬂow,

establishing patterns focusing on semantic mediation,

also remain open for further investigation.

REFERENCES

Asprino, L., Daga, E., Gangemi, A., and Mulholland,

P. (2023). Knowledge graph construction with a

fac¸ade: A uniﬁed method to access heterogeneous

data sources on the web. ACM Transactions on In-

ternet Technology, 23(1):1–31.

Avila, C., Calixto, A., Rolim, T., Franco, W., Venceslau, A.,

Vidal, V., Pequeno, V., and Moura, F. F. D. (2019).

Medibot: An ontology based chatbot for portuguese

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

196

speakers drug’s users. In M., B. A. H. S. F. J. S., editor,

Proceedings of the 21st International Conference on

Enterprise Information Systems, volume 1, pages 25–

36, Heraklion, Crete, Greece. SCITEPRESS - Science

and Technology Publications.

Baazaoui-Zghal, H. (2016). Fuzzy ontology-based spatial

data warehouse for context-aware search and recom-

mendation. In Proceedings of the 11th International

Joint Conference on Software Technologies, pages

161–166, Lisbon, Portugal. SCITEPRESS - Science

and Technology Publications.

Balachandran, S., Ranganathan, V., and Vetriveeran, D.

(2019). Aligning large biomedical ontologies for se-

mantic interoperability using graph partitioning. In-

ternational Journal of Biomedical Engineering and

Technology, 31(2):137–160.

Bartusiak, A. and L

assig, J. (2016). Semantic processing for

the conversion of unstructured documents into struc-

tured information in the enterprise context. In Pro-

ceedings of the 12th International Conference on Se-

mantic Systems, SEMANTiCS 2016, pages 125–128,

New York, NY, USA. Association for Computing Ma-

chinery.

Batini, C., Lenzerini, M., and Navathe, S. B. (1986). A

comparative analysis of methodologies for database

schema integration. ACM Computing Surveys,

18(4):323–364.

Bonte, P., Turck, F. D., and Ongenae, F. (2022). Bridg-

ing the gap between expressivity and efﬁciency in

stream reasoning: a structural caching approach for

iot streams. Knowledge and Information Systems,

64(7):1781–1815.

Burgdorf, A., Paulus, A., Pomp, A., and Meisen, T. (2022).

Docsemmap: Leveraging textual data documenta-

tions for mapping structured data sets into knowledge

graphs. pages 209–216. IEEE.

Buron, M., Goasdou

e, F., Manolescu, I., and Mugnier, M.-

L. (2020). Obi-Wan: Ontology-Based RDF Integra-

tion of Heterogeneous Data. Proceedings of the VLDB

Endowment, 13(12):2933–2936.

Calva, M. and Piedra, N. (2020). Health data representation

through web semantic, a case study applied to elec-

tronic records medical in the utpl hospital. In 2020

XLVI Latin American Computing Conference (CLEI),

pages 284–293, Loja, Ecuador. IEEE.

Capodieci, A., Mainetti, L., and Carrozzo, S. (2016). Se-

mantic enterprise service bus for cultural heritage. In

2016 12th International Conference on Innovations in

Information Technology (IIT), pages 1–8. IEEE.

Cheung, C. M., Goyal, P., Harris, G., Patri, O., Srivas-

tava, A., Zhang, Y., Panangadan, A., Chelmis, C., Mc-

Kee, R., Theron, M., Nemeth, T., and Prasanna, V. K.

(2015). Rapid data integration and analysis for up-

stream oil and gas applications. In SPE Annual Tech-

nical Conference and Exhibition, pages 2573–2590,

Houston, Texas, USA. SPE.

Cudr

e-Mauroux, P. (2020). Leveraging knowledge graphs

for big data integration: the xi pipeline. Semantic Web,

11(1):13–17.

DAMA, I. (2017). DAMA-DMBOK: Data Management

Body of Knowledge (2nd Edition). Technics Publica-

tions, LLC, Denville, NJ, USA.

Dao, J., Ng, S. T., Yang, Y., Zhou, S., Xu, F. J., and Skit-

more, M. (2021). Semantic framework for interde-

pendent infrastructure resilience decision support. Au-

tomation in Construction, 130.

Dhayne, H., Kilany, R., Haque, R., and Taher, Y. (2018).

Sedie: A semantic-driven engine for integration of

healthcare data. In 2018 IEEE International Con-

ference on Bioinformatics and Biomedicine (BIBM),

pages 617–622, Madrid, Spain. IEEE.

Ding, L., Xiao, G., Calvanese, D., and Meng, L. (2020).

A framework uniting ontology-based geodata integra-

tion and geovisual analytics. ISPRS International

Journal of Geo-Information, 9(8).

Dirgahayu, T., Hendrik, and Setiaji, H. (2020). Semantic

web in disaster management: A systematic literature

review. IOP Conference Series: Materials Science

and Engineering, 803(1).

Divya, G. and Manish, T. I. (2020). Machine learning

techniques and frameworks for heterogeneous data

fusion in big data analytics. In 2020 4th Inter-

national Conference on Electronics, Communication

and Aerospace Technology (ICECA), pages 1568–

1574. IEEE.

Dridi, A., Sassi, S., Chbeir, R., and Faiz, S. (2020).

A ﬂexible semantic integration framework for fully-

integrated ehr based on fhir standard. In ICAART 2020

- Proceedings of the 12th International Conference

on Agents and Artiﬁcial Intelligence, volume 2, pages

684–691, Valletta, Malta. SCITEPRESS - Science and

Technology Publications.

Fathy, N., Gad, W., and Badr, N. (2019). A uniﬁed

access to heterogeneous big data through ontology-

based semantic integration. In 2019 Ninth Interna-

tional Conference on Intelligent Computing and In-

formation Systems (ICICIS), pages 387–392. IEEE.

Fusco, G. and Aversano, L. (2020). An approach for seman-

tic integration of heterogeneous data sources. PeerJ

Computer Science, 6(3).

Gil, R. M., de Buenaga Rodr

ıguez, M., Galisteo, F. A.,

aez, D. G., and Garc

ıa-Cuesta, E. (2021). A domain-

adaptable heterogeneous information integration plat-

form: Tourism and biomedicine domains. Informa-

tion, 12(11).

Gomes, L. and Santanch

e, A. (2015). The web within:

Leveraging web standards and graph analysis to en-

able application-level integration of institutional data.

Lecture Notes in Computer Science, 8990:26–54.

Grasso, C. T., Joshi, A., and Siegel, E. (2015). Beyond ner:

Towards semantics in clinical text. In Proceedings of

International Workshop on Biomedical Data Mining,

Modeling, and Semantic Integration: A Promising

Approach to Solving Unmet Medical Needs (BDM2I

2015), volume 1428, Bethlehem, PA, USA. CEUR

Workshop Proceedings.

Gruber, T. R. (1993). A translation approach to portable

ontology speciﬁcations. Knowledge Acquisition,

5(2):199–220.

Heterogeneous Data Integration: A Literature Scope Review

197

Guarino, N. (1998). Formal ontology in information sys-

tems. pages 3–15.

Guedea-Noriega, H. H. and Garc

ıa-S

anchez, F. (2022). In-

tegroly: Automatic knowledge graph population from

social big data in the political marketing domain. Ap-

plied Sciences, 12(16).

Gupta, N. and Gupta, B. (2021). Machine learning approach

of semantic mapping in polystore health information

systems. International Journal of Computer Infor-

mation Systems and Industrial Management Applica-

tions, 13:222–233.

Haghgoo, M., Mazary, A. N. A., and Monti, A. (2022).

Siseg-auto semantic annotation service to integrate

smart energy data. Energies, 15(4):1428.

Hao, X., Ji, Z., Li, X., Yin, L., Liu, L., Sun, M., Liu, Q.,

and Yang, R. (2021). Construction and application of

a knowledge graph. Remote Sensing, 13(13).

Hochreiter, S. and Schmidhuber, J. (1997). Long short-term

memory. Neural Computation, 9(8):1735–1780.

Jeong, H. and Jeong, H. (2015). Ontology-based inte-

gration and reﬁnement of evaluation-committee data

from heterogeneous data sources. Indian Journal of

Science and Technology, 8(23):1–7.

Jie, F., Huang, Y., Bai, Q., and Wu, X. (2021). Hao unity: A

graph-based system for unifying heterogeneous data.

In Proceedings of the 30th ACM International Con-

ference on Information & Knowledge Management,

CIKM ’21, page 4725–4729, New York, NY, USA.

Association for Computing Machinery.

Kamm, S., Jazdi, N., and Weyrich, M. (2021). Knowledge

discovery in heterogeneous and unstructured data of

industry 4.0 systems: Challenges and approaches.

Procedia CIRP, 104:975–980.

Katrandzhiev, K., Gocheva, K., and Bratanova-Doncheva,

S. (2022). Whole system data integration for condi-

tion assessments of climate change impacts: An ex-

ample in high-mountain ecosystems in rila (bulgaria).

Diversity, 14(4).

Kessler, I., Perzylo, A., and Rickert, M. (2021). Ontology-

based decision support system for the nitrogen fertil-

ization of winter wheat. In E., O.-P. M. G., editor,

Metadata and Semantics Research, volume 1355 of

Communications in Computer and Information Sci-

ence, pages 245–256, Madrid, Spain. Springer, Cham.

Khnaisser, C., Lavoie, L., Fraikin, B., Barton, A., Dussault,

S., Burgun, A., and Ethier, J.-F. (2022). Using an

ontology to derive a sharable and interoperable rela-

tional data model for heterogeneous healthcare data

and various applications. Methods of Information in

Medicine, 61(2):e73–e88.

Kim, J., Kong, J., Sohn, M., and Park, G. (2021). Layered

ontology-based multi-sourced information integration

for situation awareness. The Journal of Supercomput-

ing, 77(9):9780–9809.

Kitchenham, B. A. and Charters, S. (2007). Guidelines for

performing systematic literature reviews in software

engineering. Technical Report EBSE 2007-001, Keele

University and Durham University Joint Report.

Krataithong, P., Anutariya, C., and Buranarach, M. (2022).

A taxi trajectory and social media data management

platform for tourist behavior analysis. Sustainability,

14(8):4677.

Kumar, N. and Das, S. (2019). New distributed data fu-

sion using pregel for large text dataset. Interna-

tional Journal of Scientiﬁc and Technology Research,

8(12):2090–2098.

Lafferty, J. D., McCallum, A., and Pereira, F. C. N.

(2001). Conditional random ﬁelds: Probabilistic mod-

els for segmenting and labeling sequence data. page

282–289.

Le Guillarme, N., Hedde, M., and Thuiller, W. (2021).

Building a trophic knowledge graph to support soil

food web reconstruction. In S4BioDiv 2021: 3rd In-

ternational Workshop on Semantics for Biodiversity,

volume 2969, Bolzano, Italy. CEUR Workshop Pro-

ceedings.

Lembo, D. and Scafoglieri, F. M. (2020). Ontology-based

document spanning systems for information extrac-

tion. International Journal of Semantic Computing,

14(01):3–26.

Ma, B., Jiang, T., Zhou, X., Zhao, F., and Yang, Y. (2017).

A novel data integration framework based on uniﬁed

concept model. IEEE Access, 5:5713–5722.

Ma, C. and Moln

ar, B. (2020). Use of ontology learning

in information system integration: A literature survey.

In Communications in Computer and Information Sci-

ence, volume 1178 CCIS, pages 342–353. Springer,

Singapore.

Ma, C. and Moln

ar, B. (2022). Ontology learning from rela-

tional database: Opportunities for semantic informa-

tion integration. Vietnam Journal of Computer Sci-

ence, 09(01):31–57.

Maga-Nteve, C., Kontopoulos, E., Tsolakis, N., Katakis, I.,

Mathioudis, E., Mitzias, P., Avgerinakis, K., Medit-

skos, G., Karakostas, A., Vrochidis, S., and Kompat-

siaris, I. (2022). A semantic technologies toolkit for

bridging early diagnosis and treatment in brain dis-

eases: Report from the ongoing eu-funded research

project alameda. volume 1537 of CCIS, pages 349–

354. Springer Science and Business Media Deutsch-

land GmbH.

Mahmoud, A., Shams, M. Y., Elzeki, O. M., and Awad,

N. A. (2021). Using semantic web technologies to

improve the extract transform load model. Comput-

ers, Materials & Continua, 68(2):2711–2726.

Mami, M. N., Graux, D., Scerri, S., Jabeen, H., Auer, S.,

and Lehmann, J. (2019). Uniform access to multi-

form data lakes using semantic technologies. In Pro-

ceedings of the 21st International Conference on In-

formation Integration and Web-Based Applications &

Services, iiWAS2019, page 313–322. Association for

Computing Machinery.

Masmoudi, M., Karray, M. H., Lamine, S. B. A. B., Zghal,

H. B., and Archimede, B. (2019). Diserto: Semantics-

based tool for automatic and virtual data integration.

In 2019 IEEE/ACS 16th International Conference on

Computer Systems and Applications (AICCSA), pages

1–8, Abu Dhabi, United Arab Emirates. IEEE.

Masseroli, M., Canakoglu, A., and Ceri, S. (2016). Inte-

gration and querying of genomic and proteomic se-

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

198

mantic annotations for biomedical knowledge extrac-

tion. IEEE/ACM Transactions on Computational Bi-

ology and Bioinformatics, 13(2):209–219.

Mountasser, I., Ouhbi, B., Hdioud, F., and Frikh, B. (2021).

Semantic-based big data integration framework using

scalable distributed ontology matching strategy. Dis-

tributed and Parallel Databases, 39(4):891–937.

Mrhar, K., Douimi, O., Abik, M., and Benabdellah, N. C.

(2020). Towards a semantic integration of data from

learning platforms. IAES International Journal of Ar-

tiﬁcial Intelligence, 9(3):535–544.

Nagpal, P., Chaudhary, D., and Singh, J. (2021). Know-

ing the unknown: Unshielding the mysteries of se-

mantic web in health care domain. In Proceedings

of the Workshop on Advances in Computational Intel-

ligence, its Concepts & Applications (ACI 2021), vol-

ume 2823, pages 37–44. CEUR Workshop Proceed-

ings.

Nashipudimath, M. M., Shinde, S. K., and Jain, J. (2020).

An efﬁcient integration and indexing method based on

feature patterns and semantic analysis for big data. Ar-

ray, 7.

Nath, R. P. D., Hose, K., Pedersen, T. B., and Romero,

O. (2017). Setl: A programmable semantic extract-

transform-load framework for semantic data ware-

houses. Information Systems, 68:17–43.

Nathalie, A. (2009). Schema matching based on attribute

values and background ontology. In 12th AGILE In-

ternational conference on geographic information sci-

ence, volume 1, pages 1–9. Springer Berlin, Heidel-

berg.

Niang, C., Marinica, C.,

Elise Leboucher, Bouiller, L.,

Capderou, C., and Bouchou, B. (2016). Ontology-

based data integration system for conservation-

restoration data (obdis-cr). In Proceedings of the 20th

International Database Engineering & Applications

Symposium, IDEAS ’16, page 218–223, New York,

New York, USA. Association for Computing Machin-

ery.

Nimmagadda, S. L., Reiners, T., and Wood, L. C.

(2019). On modelling big data guided supply chains

in knowledge-base geographic information systems.

Procedia Computer Science, 159:1155–1164.

Noriega, H. H. G. and Sanchez, F. G. (2019). Semantic (big)

data analysis: an extensive literature review. IEEE

Latin America Transactions, 17(05):796–806.

Nundloll, V., Lamb, R., Hankin, B., and Blair, G. (2021).

A semantic approach to enable data integration for

the domain of ﬂood risk management. Environmen-

tal Challenges, 3.

Oo, S. M., Haesendonck, G., Meester, B. D., and Dimou,

A. (2022). Rmlstreamer-siso: An rdf stream generator

from streaming heterogeneous data. In International

Semantic Web Conference, pages 697–713.

Pereira, A., Lopes, R. P., and Oliveira, J. L. (2020).

Scaleus-fd: A fair data tool for biomedical applica-

tions. BioMed research international.

Phengsuwan, J., Shah, T., Sun, R., James, P., Thakker, D.,

and Ranjan, R. (2022). An ontology-based system for

discovering landslide-induced emergencies in electri-

cal grid. Transactions on Emerging Telecommunica-

tions Technologies, 33(3).

Pomp, A., Paulus, A., Jeschke, S., and Meisen, T. (2017).

Eskape: Platform for enabling semantics in the con-

tinuously evolving internet of things. In 2017 IEEE

11th International Conference on Semantic Comput-

ing (ICSC), pages 262–263, San Diego, CA, USA.

IEEE.

Qundus, J. A., Sch

afermeier, R., Karam, N., Peikert, S.,

and Paschke, A. (2021). Roc: An ontology for coun-

try responses towards covid-19. In 2nd International

Conference on Digital Curation Technologies, volume

2836, Berlin, Germany. CEUR Workshop Proceed-

ings.

Radaoui, M., Ben Abdallah Ben Lamine, S., Zghal, H. B.,

Guegan, C. G., and Kabachi, N. (2019). Knowledge

guided integration of structured and unstructured data

in health decision process. In Proceedings of the 28th

International Conference on Information Systems De-

velopment: Information Systems Beyond 2020, ISD

2019, Toulon, France: ISEN Yncr

ea M

editerran

ee.

Ramzy, N., Auer, S., Ehm, H., and Chamanara, J. (2022).

Mare: Semantic supply chain disruption management

and resilience evaluation framework. In Proceed-

ings of the 24th International Conference on Enter-

prise Information Systems, volume 2, pages 484–493.

SCITEPRESS - Science and Technology Publications.

Rani, P. S., Suresh, R. M., and Sethukarasi, R. (2019).

Multi-level semantic annotation and uniﬁed data inte-

gration using semantic web ontology in big data pro-

cessing. Cluster Computing, 22(5):10401–10413.

Rouces, J., de Melo, G., and Hose, K. (2018). Addressing

structural and linguistic heterogeneity in the web1. AI

Communications, 31(1):3–18.

Saber, A., Al-Zoghby, A. M., and Elmougy, S. (2018). Big-

data aggregating, linking, integrating and represent-

ing using semantic web technologies. In Hassanien,

A. E., Tolba, M. F., Elhoseny, M., and Mostafa,

M., editors, The International Conference on Ad-

vanced Machine Learning Technologies and Appli-

cations (AMLTA2018), volume 723, pages 331–342,

Cairo, Egypt. Springer International Publishing.

Sandhya, H. and Roy, M. M. (2016). Data integration of

heterogeneous data sources using qr decomposition.

In S., T. S. M. B. S. D., editor, International Sympo-

sium on Intelligent Systems Technologies and Applica-

tions, volume 385 of Advances in Intelligent Systems

and Computing, pages 333–344. Springer Verlag.

Santipantakis, G. M., Glenis, A., Patroumpas, K., Vlachou,

A., Doulkeridis, C., Vouros, G. A., Pelekis, N., and

Theodoridis, Y. (2020). Spartan: Semantic integra-

tion of big spatio-temporal data from streaming and

archival sources. Future Generation Computer Sys-

tems, 110:540–555.

SCHIESSL, M. and BR

ASCHER, M. (2017). Ontol-

ogy lexicalization: Relationship between content

and meaning in the context of information retrieval.

Transinformac¸

ao, 29(1):57–72.

Sengloiluean, K. and Khuntong, R. (2020). Ontology-

based semantic integration of heterogeneous data

Heterogeneous Data Integration: A Literature Scope Review

199

sources using ontology mapping approach. Journal

of Theoretical and Applied Information Technology,

98(22):3489–3502.

Sernadela, P. and Oliveira, J. L. (2017). A semantic-

based workﬂow for biomedical literature annotation.

Database, 2017.

Shen, F., Liu, H., Sohn, S., Larson, D. W., and Lee,

Y. (2016). Predicate oriented pattern analysis for

biomedical knowledge discovery. Intelligent Informa-

tion Management, 08(03):66–85.

Sima, A. C., de Farias, T. M., Zbinden, E., Anisi-

mova, M., Gil, M., Stockinger, H., Stockinger, K.,

Robinson-Rechavi, M., and Dessimoz, C. (2019). En-

abling semantic queries across federated bioinformat-

ics databases. Database : the journal of biological

databases and curation.

Stroganov, O., Fedarovich, A., Wong, E., Skovpen,

Y., Pakhomova, E., Grishagin, I., Fedarovich, D.,

Khasanova, T., Merberg, D., Szalma, S., and Bryant,

J. (2022). Mapping of uk biobank clinical codes:

Challenges and possible solutions. PLOS ONE,

17(12).

Sugawara, E. and Nikaido, H. (2014). Properties of adeabc

and adeijk efﬂux systems of acinetobacter baumannii

compared with those of the acrab-tolc system of es-

cherichia coli. Antimicrobial agents and chemother-

apy, 58(12):7250–7.

Thirumahal, R., Sadasivam, G. S., and Shruti, P. (2022). Se-

mantic integration of heterogeneous data sources us-

ing ontology-based domain knowledge modeling for

early detection of covid-19. SN Computer Science,

3(6).

VandanaKolisetty, V. and Rajput, D. S. (2021). Integration

and classiﬁcation approach based on probabilistic se-

mantic association for big data. Complex & Intelligent

Systems.

Vidal, M.-E., Jozashoori, S., and Sakor, A. (2019). Seman-

tic data integration techniques for transforming big

biomedical data into actionable knowledge. In 2019

IEEE 32nd International Symposium on Computer-

Based Medical Systems (CBMS), pages 563–566.

IEEE.

Vilches-Bl

azquez, L. M. and Saavedra, J. (2022). A graph-

based representation of knowledge for managing land

administration data from distributed agencies – a case

study of colombia. Geo-spatial Information Science,

25(2):259–277.

Wache, H., V

ogele, T., Visser, U., Stuckenschmidt, H.,

Schuster, G., Neumann, H., and H

ubner, S. (2001).

Ontology-based integration of information - a survey

of existing approaches. In Proceedings of the IJCAI-

01 Workshop on Ontologies and Information Sharing,

pages 108–118. CEUR Workshop Proceedings.

Wang, X., Xu, J., Liu, M., Wei, Z., Bu, W., and Hong, T.

(2017). An ontology-based approach for marine geo-

chemical data interoperation. IEEE Access, 5:13364–

13371.

Wu, J., Orlandi, F., O’Sullivan, D., and Dev, S. (2022).

A workﬂow to convert live atmospheric sensor data

into linked data. In IGARSS 2022 - 2022 IEEE Inter-

national Geoscience and Remote Sensing Symposium,

pages 4086–4089. IEEE.

Xiao, H. (2006). Query Processing for Heterogeneous Data

Integration Using Ontologies. PhD thesis.

Xiao, W., Guoqi, L., and Bin, L. (2017). Research on

big data integration based on karma modeling. In

IEEE International Conference on Software Engineer-

ing and Service Science, pages 245–248, Beijing,

China. IEEE.

Yadav, M., Singh, V., and Prachi (2021). Ontology based

data integration and mapping for adverse drug reac-

tion. In 2021 6th International Conference on Signal

Processing, Computing and Control (ISPCC), pages

719–727, Solan, India. IEEE.

Yafooz, W. M. S., Abdin, S. Z., and Fahad, S. A.

(2018). Managing textual data semantically in rela-

tional databases. In 2018 International Conference

on Smart Computing and Electronic Enterprise (IC-

SCEE), pages 1–5, Shah Alam, Malaysia. IEEE.

Yun, H., He, Y., Lin, L., and Wang, X. (2019). Research on

multi-source data integration based on ontology and

karma modeling. International Journal of Intelligent

Information Technologies, 15(2):69–87.

Zhang, H., Guo, Y., Li, Q., George, T. J., Shenkman, E.,

Modave, F., and Bian, J. (2018). An ontology-guided

semantic data integration framework to support inte-

grative data analysis of cancer survival. BMC Medical

Informatics and Decision Making, 18(2).

Zhang, S., Tang, Y., Yan, J., Li, L., Li, T., Li, J., Xie, P.,

Gu, Y., Xu, J., Feng, Z., Zhang, W., Xia, J., Mayer,

W., Zhang, H.-Y., He, G.-C., and He, K. (2021). A

graph-based approach for integrating biological het-

erogeneous data based on connecting ontology. In

2021 IEEE International Conference on Bioinformat-

ics and Biomedicine (BIBM), pages 600–607, Hous-

ton, TX, USA. IEEE.

Zhao, Q., Liu, J., Sullivan, N., Chang, K. C., Spina, J.,

Blasch, E., and Chen, G. (2021). Anomaly detec-

tion of unstructured big data via semantic analysis

and dynamic knowledge graph construction. In Proc.

SPIE 11756, Signal Processing, Sensor/Information

Fusion, and Target Recognition XXX. SPIE.

Zhao, W., Zhou, B., and Zhang, C. (2022). Heterogeneous

social linked data integration and sharing for public

transportation. Journal of Advanced Transportation,

pages 1–14.

Zhou, Q. (2016). Research on heterogeneous data integra-

tion model of group enterprise based on cluster com-

puting. Cluster Computing, 19(3):1275–1282.

Ziegler, P. and Dittrich, K. R. (2004). Three decades of

data intecration — all problems solved? volume 156,

pages 3–12. Springer US.

Ozsu, M. T. and Valduriez, P. (2020). Principles of Dis-

tributed Database Systems. Springer International

Publishing, 4 edition.

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