A framework to aggregate multiple ontology matchers

• Date: 16 October 2019
• Published date: 16 October 2019
• DOI: https://doi.org/10.1108/IJWIS-05-2019-0023
• Pages: 151-169
• Author: Jairo Francisco de Souza,Sean Wolfgand Matsui Siqueira,Bernardo Nunes
• Subject Matter: Information & knowledge management,Information & communications technology,Information systems,Library & information science,Information behaviour & retrieval,Internet

A framework to aggregate

multiple ontology matchers

Jairo Francisco de Souza

Department of Computer Science, Federal University of Juiz de Fora,

Juiz de Fora, Brazil, and

Sean Wolfgand Matsui Siqueira and Bernardo Nunes

Department of Informatics, Federal University of the State of Rio de Janeiro,

Rio de Janeiro, Brazil

Abstract

Purpose –Although ontologymatchers are annually proposed to address different aspectsof the semantic

heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to

propose a computational solution for ontology meta-matching (OMM) and a framework designed for

developersto make use of alignment techniques in their applications.

Design/methodology/approach –The framework includes some similarity functions that can be

chosen by developers and then, automatically,set weights for each function to obtain better alignments. To

evaluate the framework, several simulations were performed with a data set from the OntologyAlignment

Evaluation Initiative. Simplesimilarity functions were used, rather than aligners known in the literature, to

demonstrate that the results would be more influenced by the proposed meta-alignment approachthan the

functionsused.

Findings –The results showed that the framework is able to adapt to different test cases. The approach

achievedbetter results when compared with existingontology meta-matchers.

Originality/value –Although approachesfor OMM have been proposed, it is not easy to use them during

software development.On the other hand, this work presents a framework that can be used by developers to

align ontologies. New ontology matchers can be added and the framework is extensible to new methods.

Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be

easily computed.

Keywords Metadata and ontologies, Semantic interoperability, Schema matching

Paper type Research paper

1. Introduction

With the dissemination of SemanticWeb, ontologies have been built and made available to

formally represent domainconcepts. Ontologies have been used to structure knowledge and

to facilitate the exchange of messages and data among systems. They are builtfor different

purposes and by people with distinguished specializations and skills as well as different

perspectives of the domain. Therefore,there exist distinct ontologies of the same domain as

well as complementary ones, which are built with contrasting structures, names and/or

characteristics. To reconcile these ontologies, alignment techniques have been used. There

are many techniques toalign ontologies and they have been reviewed in the recentliterature

(Thiéblin et al.,2019;Mohammadi et al.,2019;Chauhan et al., 2018;Abubakar et al.,2018;

Babalou et al., 2016;Otero-Cerdeiraet al., 2015).

Different matchers have been proposed on a yearly basis (Thiéblin et al.,2019;

Mohammadi et al.,2019,2018;Chauhanet al.,2018;Xue and Wang, 2017) because of the fact

that ontology alignment is a complex matter, which can be tackled by many approaches

Multiple

ontology

matchers

151

Received17 May 2019

Revised16 September 2019

Accepted17 September 2019

InternationalJournal of Web

InformationSystems

Vol.16 No. 2, 2020

pp. 151-169

© Emerald Publishing Limited

1744-0084

DOI 10.1108/IJWIS-05-2019-0023

The current issue and full text archive of this journal is available on Emerald Insight at:

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such as by using linguistic techniques(Kolyvakis et al., 2018;Wu et al.,2016;Van Hage et al.,

2005), the graph theory (Quintero et al.,2018;Li et al., 2018;Zang et al., 2016) and

mathematical logic (Karimi and Kamandi,2019;Jayasri and Rajmohan, 2015;Sengupta and

Hitzler, 2015;Janowicz and Wilkes, 2009). In addition, each matcher tries to solve the

problem taking into account a subset of characteristics. Therefore, there is no alignment

approach that clearly stands out in relation to all others (Xue and Wang, 2017) and fits all

possible scenarios.To solve the alignment problem in a more general approach that depends

less on the specific scenario, it is possible to aggregate the result of a heterogeneous set of

matchers by adding the characteristicsof each matcher to obtain better results. This context

leads to the emergence of semi-automaticapproaches such as interactive matching (da Silva

et al.,2017) and automatic approaches such as meta-matchers. However, the issue of meta-

matching is far from being trivial (Xue and Pan, 2018;Guli

cet al.,2018;Xueand Liu, 2017).

This is a problem usually solved through an optimization approach, mainly by using

population-basedmeta-heuristics (Souza et al.,2014).

Even though ontology meta-matching (OMM) has become more frequent over the past

few years, only a few papers deal with its usagein practice (Xue et al., 2018b;Martinez-Gil

et al., 2012). There are tools for ontology alignment which have been proposed in the

literature and made available by researchers, but this is not the case for meta-matching

approaches. In addition,OMM is time-consuming and it is often used as a batch service.

We introduce GNOSISþ, a framework for OMMthat can be used by developers to align

ontologies. GNOSISþwas conceived with an architecture that allows the insertion of new

matchers and the adaptation to combine different types of matchers, methods of

correspondence choice, parameterization of the approach for tuning the weights associated

with each matcher and formatof output alignment. It was projected to support developersto

use alignment techniques in their applications.Moreover, the OMM approach is modeled as

a linear equation system, thus the fitnessfunction is less costly than classical functionsused

by other meta-matchers.

To facilitate the understanding of the work, this paper is organizedas follows: Section 2

discusses related works. The architectureof GNOSISþis presented in detail in Section 3. To

demonstrate that theproposed solution can be adapted to different scenarios,we applied the

developed tool on a benchmark. The methodology and results are discussed in Section 4.

Finally, Section 5 presents our conclusions. Our experiments show that the tool was able to

reach results closeto the state of art, but requiring few training data.

2. Related work

According to Xue and Wang (2017) and Martinez-Gil and Aldana-Montes (2012), even

though there are many ontology matching techniques, none has been proved to be fully

efficient technique in all cases. Usually,there is a need for having knowledge on the context

in which the techniques will be applied,on the data available and on the existing differences

according to the model to be applied. Still, Xue and Wang (2017) highlight that ontology

matchers do not necessarily find the same correspondences. The combined, coordinated use

of distinguished techniques, preferably complementary, may benefit the achievement of

better ontology alignment;this is the context for the use of OMM.

Research on ontology alignment generally explores a fixed solution. Considering the

tools submitted to Ontology Alignment Evaluation Initiative (OAEI) campaigns[1], few are

those which can be set up by users or easily adaptedto new types of matchers (Mathur et al.,

2014;Martinez-Gil et al.,2012). According to Martinez-Gil and Aldana-Montes (2012), these

tools are generally prepared exclusively to reach the best results in predetermined tests,

thus, the resultslose their importance in practice.

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