Instance-Based Ontology Matching: A Literature Review

Abubakar, Mansir; Hamdan, Hazlina; Mustapha, Norwati; Aris, Teh Noranis Mohd

doi:10.1007/978-3-319-72550-5_44

Cited by 9 publications

(10 citation statements)

References 41 publications

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“…Several approaches have been introduced in the last decade to solve the ontology matching problem 14‐16,27,28 . Matching strategies based on instances are also appropriate for connecting database records 29,30 . Much research has explored methods for improving the efficiency of ontology matching.…”

Section: Related Workmentioning

confidence: 99%

“…[14][15][16]27,28 Matching strategies based on instances are also appropriate for connecting database records. 29,30 Much research has explored methods for improving the efficiency of ontology matching. Solutions regarding the ontology matching problem can be categorized into two groups: (i) solutions based on the reduction of the search space by employing computational intelligence, data mining, and machine learning methods; [6][7][8] and (ii) solutions based on HPC while parallel matching is established.…”

Section: Related Workmentioning

confidence: 99%

“…Assessments and application analyses were provided using the competitive OAEIontology databases competition 2 . Abubakar et al 29 studied the current ontological situation rather than popular conceptual matching with specific considerations of ontological instance-based matching. To estimate relative effectiveness and performance, Nentwig et al 30 then investigated the comparative evaluations of link discovery (DL) frameworks.…”

Section: Toolsmentioning

confidence: 99%

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An ontology matching approach for semantic modeling: A case study in smart cities

Djenouri

Belhadi

Akli-Astouati

et al. 2021

Computational Intelligence

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This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching-based Instances (COMI) and Pattern mining for Ontology Matching-based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart-city data into similar clusters using the generic k-means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state-of-the-art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Toolsmentioning

confidence: 99%

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An ontology matching approach for semantic modeling: A case study in smart cities

Djenouri

Belhadi

Akli-Astouati

et al. 2021

Computational Intelligence

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“…The idea of knowledge graph is widely used in intelligent question answering [ 4 ], recommendation systems [ 5 ], semantic search [ 6 ], and other fields. However, due to the lack of unified presentation standards for data and information, and/or the differences in the methods of obtaining data [ 7 ], the relevant knowledge of the same entity in the real world is represented in various forms among different knowledge graphs. It is not conducive to knowledge sharing between different domains and applications.…”

Section: Introductionmentioning

confidence: 99%

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

Zhu

Wang

Jiang

et al. 2021

Entropy

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Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.

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“…To reconcile these ontologies, alignment techniques have been used. There are many techniques to align ontologies and they have been reviewed in the recent literature (Thiéblin et al, 2019;Mohammadi et al, 2019;Chauhan et al, 2018;Abubakar et al, 2018;Babalou et al, 2016;Otero-Cerdeira et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A framework to aggregate multiple ontology matchers

Souza

Siqueira

Nunes

2019

IJWIS

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Purpose Although ontology matchers are annually proposed to address different aspects of 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 developers to 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 Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for 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.

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Instance-Based Ontology Matching: A Literature Review

Cited by 9 publications

References 41 publications

An ontology matching approach for semantic modeling: A case study in smart cities

An ontology matching approach for semantic modeling: A case study in smart cities

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

A framework to aggregate multiple ontology matchers

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